scenario questions

Your team uses a CI/CD pipeline with Cloud Build to train and deploy ML models on Vertex AI. You want to ensure that only models that pass validation checks (e.g., accuracy threshold, fairness metrics) are promoted to production. What is the best way to implement this?

A financial services company stream trades into Pub/Sub and processes with Dataflow. The pipeline must ensure exactly-once processing of each trade for regulatory compliance. However, Pub/Sub guarantees at-least-once delivery. Which combination of features should the Dataflow pipeline use to achieve exactly-once semantics?

You are setting up Dataplex data quality rules for a BigQuery table. You want to define rules that check for non-null values in key columns and also validate that a column's values fall within a certain range. Which TWO rule types must you use? (Choose 2)

A data engineer needs to share a large BigQuery table with a different team, but wants to minimize storage costs. The table is 1 TB in size and is updated daily. The other team only needs read access to the data as of a specific point in time (e.g., end of each day). Which BigQuery feature should be used to provide a read-only copy without duplicating the entire table?

Refer to the exhibit. An auditor sees the following output from `gcloud ai models list`. What can they conclude about versioning?

A company processes IoT sensor data in near real-time. They ingest data via Cloud Pub/Sub, then a Dataflow streaming pipeline writes to Bigtable for low-latency queries. Recently, they observed increased Pub/Sub message backlog during traffic spikes. What is the most effective scaling strategy?

A Dataflow streaming job is processing data from Pub/Sub and writing to BigQuery. The job is stuck with the message 'No progress has been made' for several minutes. Which TWO actions should the team take to troubleshoot and resolve the issue? (Choose TWO.)

A company uses Pub/Sub with push subscriptions to deliver events to a Cloud Run service. Recently, the service has been returning HTTP 429 (Too Many Requests), causing messages to be retried and eventually sent to the dead letter topic. What is the MOST likely cause?

A healthcare organization stores patient data in BigQuery. They need to encrypt a specific column (e.g., SSN) using a key they manage, and decrypt it only for authorized queries via a user-defined function. Which approach should they use?

Which TWO actions can reduce the cost of running a Dataproc cluster for a nightly batch job?

A company is designing a data pipeline that ingests real-time events from IoT devices and must handle late-arriving data (up to 1 hour late) while minimizing duplicate processing. They plan to use Dataflow with Pub/Sub. Which combination of windowing and trigger settings should they use?

A data engineer needs to create a unified table that combines data from Cloud Storage (Parquet files) and BigQuery native tables, with fine-grained access control and governance. Which three Google Cloud features should they use together? (Choose THREE.)

A data pipeline processes sensitive customer data. You need to ensure that only authorised users can query the data in BigQuery, and that the data is encrypted at rest and in transit. Which THREE steps should you take? (Choose three.)

A company is designing a streaming data pipeline to process real-time clickstream events. They need to aggregate events by session window with a 5-minute gap and enable exactly-once processing semantics. Which Google Cloud service should they use?

You are using Vertex AI Feature Store to serve features for online predictions. Your model requires features from multiple sources with low latency (<10ms). Which type of serving should you use?

Which TWO metrics are most important to monitor for a real-time online prediction system to ensure service reliability and model performance?

A company needs to stream data from a MySQL database to BigQuery with a latency under 10 seconds. They also need to handle schema changes automatically. Which TWO services should they combine?

A company needs to predict whether a product image contains a specific defect. They have 10,000 labeled images and want to build a model quickly without writing custom code or training from scratch. Which GCP service should they use?

A team trained a model on a Vertex AI custom training job and wants to deploy it to an endpoint for online predictions. They have the model artifacts stored in Cloud Storage. What steps are required?

A data science team uses Cloud Build and Vertex AI to implement CI/CD for their machine learning models. Which THREE steps are essential for a production-ready operationalization pipeline? (Choose 3.)

A data pipeline processes streaming data from Pub/Sub to BigQuery. The pipeline needs to handle late-arriving data that is up to 1 hour late. Which Dataflow feature should be used?

You need to estimate the cost of a BigQuery query before running it. Which command or feature should you use?

A company runs a daily batch data processing pipeline using Cloud Dataproc. The pipeline reads 10 TB of CSV files from Cloud Storage, performs a heavy aggregation (GroupBy) and joins with a small reference table, then writes the results to BigQuery. The cluster consists of 20 n1-standard-8 nodes, including 10 preemptible workers for cost savings. Recently, the job completion time has doubled from 30 minutes to over an hour. The job logs show many tasks being retried, and the Shuffle spill ratio is high. No significant data volume change was observed. What is the most likely root cause?

Drag and drop the steps to configure a VPC network with private Google access for on-premises connectivity using Cloud VPN into the correct order.

A company uses Cloud Dataflow to process streaming data from Pub/Sub into BigQuery. The pipeline uses a side input from a Cloud Bigtable table containing user profile information to enrich the events. The side input is updated every hour. Which approach should the company use to ensure that the pipeline uses the latest profile data without causing high memory usage?

A production model deployed on Vertex AI Endpoint is experiencing high latency during traffic spikes. The current configuration uses a single replica. What is the most efficient solution?

Which TWO configurations are required to enable online prediction for a model deployed on Vertex AI Endpoints?

You are building a Dataflow pipeline in Python that reads messages from Pub/Sub, enriches them with data from a BigQuery table, and writes the results to BigQuery. The enrichment lookup table is large and changes infrequently. Which approach minimizes cost and latency?

You are designing a data pipeline that ingests streaming data from Pub/Sub, processes it with Dataflow, and writes to BigQuery. You need to ensure that schema changes in the incoming data (new fields) are handled without pipeline failure. Which THREE steps should you take? (Choose THREE.)

A financial services firm processes sensitive transactions using Cloud Dataflow. The pipeline reads from Pub/Sub, performs stateful processing (e.g., fraud detection), and writes to Cloud Spanner. Compliance requires exactly-once processing semantics. Which configuration ensures exactly-once processing?

A company runs a streaming Dataflow pipeline that reads from Pub/Sub, enriches data with a side input from BigQuery, and writes to BigQuery. After updating the pipeline code (adding a new field to the output), the engineer notices that the new pipeline version is not picking up the updated code because the job was started from a template. The engineer wants to update the streaming pipeline without draining it. What should the engineer do?

You need to perform a one-time migration of historical data from an on-premises Teradata data warehouse to BigQuery. The data volume is 50 TB and you have a high-speed network connection (10 Gbps). What is the most efficient way to load the data?

An organization is using BigQuery for analytics. They have a table that is 500 GB and is frequently queried by 'date' and 'region'. They want to optimize query performance and reduce costs. Which TWO actions should they take?

A company uses Workflows to orchestrate a series of Google Cloud services for data processing. They need to call an external HTTP API as part of the workflow and handle potential failures with retries. Which Workflows feature should they use?

Your organization is designing a data lake on Google Cloud using Cloud Storage. You need to choose a file format for storing raw data that supports schema evolution, is splittable for parallel processing, and is optimized for query performance in BigQuery. Which TWO formats meet these requirements? (Choose 2.)

A data science team is operationalizing a batch prediction job using Vertex AI Batch Prediction. The model uses a custom container that requires a specific GPU for inference. The job processes a large dataset stored in Cloud Storage. The team wants to minimize cost while ensuring the job completes within a 2-hour window. Which configuration should they choose?

Your team has a Dataflow pipeline that reads from BigQuery, transforms data, and writes to GCS. The pipeline is failing with 'Out of Memory' errors on the worker nodes. The input data is large but fits within the total cluster memory. Which configuration change is most likely to resolve the issue without increasing costs significantly?

You are running a Dataproc cluster for batch processing. The job is not latency-sensitive and you want to minimize cost. You notice that the cluster is underutilized during the job. Which configuration change would reduce costs most effectively?

A team is implementing CI/CD for their ML models using Google Cloud. They want to automatically retrain and deploy a new model version when new training data arrives in Cloud Storage. Which combination of services should they use?

Refer to the exhibit. What is the most likely cause of the error?

A company uses Looker Studio to build dashboards from BigQuery data. They notice that queries take several seconds to return. They want to improve performance without changing the schema or adding materialized views. Which option should they use?

You are designing a data quality pipeline that must inspect PII in BigQuery tables and de-identify sensitive columns before sharing with analysts. Which GCP service should you use?

A company uses Cloud Dataproc to run nightly Spark ETL jobs that process about 500 GB of data each night. The jobs currently take 4 hours to complete. The company wants to reduce the runtime to under 2 hours to meet a new SLA. The cluster is configured with 10 worker nodes (n1-standard-4) and 1 master node (n1-standard-4). The jobs are CPU-bound and use only default settings. The cluster is deleted after each job and recreated. The data is stored in Cloud Storage. The company is open to increasing cost but wants the most cost-effective solution to meet the SLA. Which approach should they take?

You need to schedule a simple workflow that fetches data from an API every hour, transforms it using Cloud Functions, and writes the result to Cloud Storage. The workflow has no complex branching or retry logic beyond basic retries. Which orchestration service is the MOST cost-effective and simplest to implement?

A data science team has trained a TensorFlow model for image classification and wants to deploy it to production with minimal latency. They have already exported the model as a SavedModel directory. Which service should they use to create an online prediction endpoint?

A company uses Cloud Datastream to replicate data from a MySQL database to BigQuery in near real-time. Which TWO BigQuery features are automatically used by Datastream for optimal performance and consistency? (Choose TWO.)

A company runs Apache Spark jobs on Dataproc. They want to reduce costs by using preemptible instances for worker nodes. The jobs are fault-tolerant and can handle occasional node loss. However, the cluster must remain available for interactive querying during business hours. Which Dataproc cluster configuration meets these requirements?

An e-commerce company uses BigQuery to analyze customer behavior. They need to compute the number of distinct customers per day, approximate quantiles of purchase amounts, and assign a row number per customer partition by date. Which BigQuery SQL functions should they use? (Choose THREE)

A team wants to ingest streaming data from millions of IoT devices and store historical data in BigQuery for analysis. They need near real-time analytics on the most recent data, with sub-second latency. Which architecture should they use?

You need to set up a BigQuery reservation that provides a baseline of 500 slots for daily workloads and can automatically scale up to 1000 slots during peak times. You want to pay only for the slots used beyond the baseline. Which reservation configuration should you choose?

A team is deploying a TensorFlow model for online predictions on AI Platform Prediction. They want to monitor for data drift and model performance degradation. Which TWO Google Cloud services should they use?

Match each data pipeline term to its definition.

A team is debugging a sudden increase in prediction latency for a model deployed on Vertex AI Endpoints. Which TWO metrics in Cloud Monitoring should they examine first? (Choose two.)

A team wants to retrain a model weekly using new data stored in BigQuery. They want to minimize manual effort. Which approach should they use?

A company has a Cloud Functions function that triggers on new files in Cloud Storage and writes a message to Pub/Sub for downstream processing. Recently, the function has been timing out after 60 seconds. The downstream processing is critical. What is the best solution?

Refer to the exhibit. A data engineer sees these metrics from Cloud Monitoring for a deployed Vertex AI Endpoint. What is the most effective action to reduce latency?

Which THREE steps are required to set up a continuous training pipeline on Google Cloud using Vertex AI?

Which TWO are common causes of prediction bias in a deployed machine learning model in production?

An organization wants to integrate BigQuery Omni to query data stored in AWS S3. They have set up the necessary connections. What is the primary benefit of using BigQuery Omni over simply copying the data to BigQuery?

A company uses Pub/Sub to ingest events from multiple sources. They need to ensure that messages from a specific source are processed in order (per source partition). They also need to deduplicate messages. Which TWO features should they use?

A company has a data lake on Cloud Storage with raw data in the 'raw' bucket, curated data in 'curated', and processed data in 'processed'. They want to implement lifecycle management to reduce costs. Which TWO actions should they take? (Choose 2)

A real-time recommendation system uses a custom container deployed on AI Platform Prediction. The model requires a large in-memory embedding lookup table that is loaded from Cloud Storage at startup. The current startup time is over 5 minutes, causing prediction requests to timeout. Which strategy would most effectively reduce startup time?

Refer to the exhibit. The feature store 'my_fs' responds to offline queries but online serving requests fail. What is the most likely cause?

Refer to the exhibit. A Dataflow streaming pipeline subscribes to this Pub/Sub subscription. The pipeline occasionally takes more than 10 seconds to process a message. Which behavior will occur?

A company wants to version its ML models and track lineage from training data to deployed model. Which Google Cloud service should they use?

A company is migrating on-premises Hadoop Hive workloads to Google Cloud. They want to use Dataproc for Spark processing and require a managed Hive metastore that can be shared across multiple Dataproc clusters. Which TWO components should they use?

A company uses Cloud Spanner and needs to store a parent-child relationship where the child table is frequently queried together with the parent. The parent has millions of rows and the child billions. Which Spanner feature optimizes performance for this pattern?

Which BigQuery SQL function returns the rank of a row within a window, with gaps in the ranking for ties?

Which Dataflow feature automatically scales the number of workers based on the pipeline's current workload, and also selects the optimal machine type for each worker based on the pipeline's resource requirements?

Your Dataflow streaming job is experiencing high system lag. You want to identify the root cause. Which Cloud Monitoring metrics should you examine first? (Choose the best option.)

Refer to the exhibit. What is the most likely cause?

A data pipeline ingests streaming data from Pub/Sub into BigQuery via Dataflow. Recently, the pipeline has been failing with 'deadline exceeded' errors. What is the most likely cause?

Your team is processing a large dataset with Apache Beam on Dataflow. The pipeline sometimes fails due to transient errors when writing to a BigQuery sink. You need to ensure that failed records are not lost and can be reprocessed later without blocking the pipeline. What is the best approach?

A retail company is using a machine learning model for inventory forecasting. They observe that the model's predictions become less accurate over time, especially during holiday seasons. Which monitoring metric should they prioritize?

A Dataflow pipeline with multiple steps uses a side input from a slowly changing reference table stored in BigQuery. The side input is updated every hour. To avoid reprocessing the entire pipeline on each update, which approach should you use?

What is the primary purpose of Vertex AI Feature Store?

A company uses Dataplex to manage data quality across multiple BigQuery datasets. They want to define a data quality rule that checks if a column 'email' contains a valid email format. Which Dataplex feature should they use?

A company needs to process real-time clickstream data and store it in a data warehouse for SQL-based analytics. The data volume is moderate. Which combination of Google Cloud services is most cost-effective?

You are building a forecasting model to predict daily sales for the next 90 days using historical sales data with clear seasonality and trend. You want to use BigQuery ML with minimal manual tuning. Which model type should you choose?

Which THREE considerations are important when designing a batch prediction pipeline for a large dataset on Vertex AI?

A data engineer needs to design a stream processing pipeline that reads events from Pub/Sub, enriches them with data from a Cloud Storage file, and writes aggregated results to BigQuery. The pipeline must handle late-arriving events up to 1 hour. Which Dataflow feature should be used to manage late data?

You are designing a BigQuery data lake for a healthcare organization. The data includes patient records that must be access-controlled at the row level. Which TWO features should you use to meet this requirement?

A company runs a Dataflow pipeline that processes a high-volume data stream. They notice that the pipeline's worker CPU utilisation is near 100% and the system lag is increasing. Which three actions can improve performance? (Choose three.)

A financial company processes transactions in real-time and requires exactly-once processing semantics. They also need to reprocess historical data for backtesting. Which Google Cloud service should they use?

What is the most likely cause of this error?

Your streaming Dataflow pipeline reads from Pub/Sub and writes to BigQuery. You need to update the pipeline to add a new transformation step without losing any messages or causing duplicate processing. Which TWO actions should you take? (Choose 2)

Your organization deploys multiple versions of the same model to Vertex AI Endpoint for A/B testing. You have a production model (v1) serving 90% of traffic and a candidate model (v2) serving 10%. After one week, you observe that v2 has a slightly lower AUC but significantly higher business metrics like click-through rate. The product team wants to gradually increase v2's traffic. However, you need to ensure that the overall prediction latency remains under 200 ms. Currently, the endpoint has 10 replicas for v1 and 2 replicas for v2. What is the best approach to roll out v2 while maintaining latency SLO?

A data engineer needs to enforce that all datasets in a project expire after 90 days to reduce storage costs. They want to automate this without manual intervention. Which approach should they use?

Your team uses Looker Studio to build dashboards on top of BigQuery. The dashboards are slow when filtering on a high-cardinality dimension (e.g., user ID). You want to improve performance without changing the underlying BigQuery table design. Which action should you take?

Your team has implemented a CI/CD pipeline using Cloud Composer (Apache Airflow) to retrain a model every day. The pipeline reads new data from BigQuery, trains a model using Vertex AI Training, evaluates it, and if the accuracy improves, deploys it to a Vertex AI Endpoint. For the past week, the pipeline has been running successfully but no new model has been deployed because the evaluation accuracy never exceeds the previous model's accuracy. The training data volume has been consistent. You suspect that the model is not learning from the new data. What should you do?

An organization needs to continuously replicate change data from a MySQL database to BigQuery with sub-minute latency. The database is running on-premises. Which Google Cloud service should they use?

Match each data lifecycle stage to its description.

You are monitoring a Dataflow streaming job and need to track the freshness of data being processed. What metric should you alert on?

You have a BigQuery table that is partitioned by ingestion time and clustered on user_id. The table stores event logs and is queried frequently by user_id to analyze user behavior over the last 30 days. Queries are still scanning too many partitions. Which optimization should you apply first?

A company is migrating their on-premises Hadoop cluster to Google Cloud. The existing cluster runs HDFS, Hive, and Spark jobs. The migration must minimize changes to existing job code and configuration. The data volume is 50 TB and growing. The team expects to run both batch and interactive SQL queries. Which architecture should they use?

Which TWO statements about designing a data processing pipeline on Google Cloud are correct? (Choose 2.)

A company wants to track data lineage for their BigQuery tables to understand how data flows from source to derived tables. Which TWO Google Cloud services can be used to capture and visualize data lineage? (Choose 2.)

A data engineer needs to design a batch pipeline that processes daily log files from Cloud Storage and writes aggregated results to BigQuery. Which service is most appropriate for this ETL job?

A data engineer needs to restrict access to BigQuery datasets such that only data from approved VPC networks can query them. They also need to audit data access. Which two security controls should they implement? (Choose two.)

A data engineer needs to monitor model performance over time for drift detection. What tool is specifically designed for this?

An organization wants to ingest on-premises Oracle database changes into BigQuery for real-time analytics with minimal latency. The Oracle database is version 19c and has a high transactional volume. Which Google Cloud service should they use?

A financial services company uses Cloud Bigtable to store trade data. They are experiencing hot-spotting on a single node, causing high latency. The row key format is [trade_id]#[timestamp]. Which row key design change would BEST distribute writes across tablets?

A healthcare organization is deploying a model that processes protected health information (PHI). They need to ensure that the inference data is encrypted in transit and at rest, and access is audited. Which combination of services meets these requirements?

You are designing a data pipeline for a financial services company that requires exactly-once processing semantics. Which TWO services or configurations provide exactly-once guarantees?

An application requires a globally distributed, strongly consistent database with 99.999% availability SLA. The workload is OLTP with high throughput across continents. Which service fits best?

A company wants to run hybrid transactional and analytical workloads on a PostgreSQL-compatible database with high performance. Which service should they choose?

A company needs to store petabytes of time-series IoT sensor data and query it with single-digit millisecond latency at millions of reads per second. The data has a simple key-value structure with timestamps. Which Google Cloud database is MOST appropriate?

A data engineer needs to design a data pipeline that ingests streaming data from Cloud Pub/Sub, performs real-time aggregations, and loads the results into BigQuery for dashboarding. Which Google Cloud service should they use for the streaming aggregation step?

You are optimizing a Dataflow pipeline that performs a group-by-key transformation on a large, skewed dataset. The pipeline is experiencing high latency due to data skew (some keys have many more values). Which TWO actions can help mitigate the skew? (Choose two.)

Your MLOps pipeline uses Vertex AI Pipelines. You want to ensure that model training uses a consistent environment with specific Python package versions. Which approach best achieves this?

A company uses BigQuery and wants to reduce query costs by using BI Engine for Looker Studio dashboards. The data is stored in a BigQuery dataset with 5 TB of frequently accessed tables. The dashboards run dozens of concurrent queries. What is the recommended approach to enable BI Engine acceleration?

Refer to the exhibit. An ML engineer sees this error when invoking a Vertex AI endpoint. What is the most likely cause?

You want to quickly estimate the number of distinct visitors to your website from a large BigQuery table. Which function provides an approximate count with low latency?

Your company runs batch predictions using Vertex AI Batch Prediction on a monthly basis. The predictions are used to generate customer segments for marketing campaigns. This month, the batch prediction job failed with an error: 'The number of rows in the input table does not match the number of rows in the output table.' The input table in BigQuery has 5 million rows, but the output table has only 4.5 million rows. You need to identify and handle the missing predictions. What is the most efficient course of action?

You are using AI Platform Prediction (now Vertex AI) for online predictions. You notice that some requests are failing with a 503 status code. Which is the most likely cause?

A company processes large volumes of GPS sensor data stored in Cloud Storage. Each hour, they run an Apache Spark job that aggregates the data by geohash region. The job must be cost-effective and scale automatically. Currently, they are using a Dataproc cluster with preemptible workers. Which improvement would best reduce costs while maintaining performance?

A company needs to stream real-time user click events from a web application to BigQuery for analysis. Which Google Cloud architecture is most suitable?

A data engineer runs this Dataflow template to load CSV files from Cloud Storage into BigQuery. The job fails with a 'File pattern not matching any files' error. What is the most likely cause?

You need to schedule a Dataproc Spark job to run at 2 AM every day, and upon completion, trigger a BigQuery load job. Which Cloud Composer operator should you use to run the Spark job?

A company uses Cloud Pub/Sub to ingest events from multiple sources. They need to guarantee that each event is processed exactly once by downstream consumers. However, Pub/Sub guarantees at-least-once delivery. Which additional steps should they implement to achieve exactly-once processing?

A team configured a garbage collection rule on a Cloud Bigtable column family with max_age of 100 seconds. After 2 minutes, they notice that data older than 100 seconds is still present. What is the most likely reason?

You need to track data lineage from a BigQuery table through a series of transformations and into a Vertex AI model training pipeline. Which Google Cloud service provides automated data lineage tracking?

Which BigQuery function can be used to retrieve the value of a column from the previous row within a partition, ordered by a timestamp?

A data engineering team is designing a streaming pipeline using Cloud Dataflow. They need to join two unbounded PCollections based on a common key. The join must handle late data up to 10 minutes. Which THREE components should they use?

A company is using Looker to explore their BigQuery data. They have defined a LookML model with an 'explore' that joins two views: 'orders' and 'customers'. The join is a left join. They want to ensure that only customers with orders are shown when exploring. Which LookML parameter should they modify?

A company runs BigQuery workloads with varying demand. They want to use flat-rate pricing with baseline slots and the ability to burst during peak times. Which TWO actions should they take? (Choose 2)

A data scientist wants to test a new model version on a small percentage of traffic before full rollout. Which Vertex AI feature allows this?

A data engineer needs to schedule a recurring transfer of data from a partner's Amazon S3 bucket to a Cloud Storage bucket for further processing. Which THREE components or configurations are necessary? (Choose 3)

A retail company uses a Vertex AI endpoint to serve product recommendations. The model is a TensorFlow model deployed with a custom container. Recently, users have reported that recommendations are stale. The model is retrained daily using Vertex AI Pipelines. The pipeline completes successfully, but the endpoint continues to serve the old model. The team checks the pipeline logs and sees that the new model is uploaded to the Vertex AI Model Registry. The endpoint has traffic split set to 100% for the old model. The team needs to update the endpoint to serve the new model version. What should they do?

Which THREE factors should be considered when designing a Vertex AI Pipeline for continuous training?

You need to build a Looker model that joins multiple tables from BigQuery. Which LookML object defines the relationship between tables?

A mobile app needs an offline-first NoSQL database that syncs data across devices when connectivity is available. Which Google Cloud database meets these requirements?

You are loading 10 GB of daily CSV files from a GCS bucket into a BigQuery table. The files contain some malformed rows that you want to skip. Which BigQuery load configuration should you use?

A data pipeline processes JSON files from Cloud Storage, transforms them using Apache Beam, and writes the output to BigQuery. Some records are malformed and cause the pipeline to fail. How should the engineer handle these errors to ensure the pipeline continues processing while preserving the malformed records for analysis?

You are migrating an on-premises Kafka cluster to Google Cloud. The cluster has 50 topics with a total throughput of 200 MB/s. You want to minimize operational overhead. Which approach is the most cost-effective?

A company uses Vertex AI to serve a model. They notice that some predictions are incorrect due to data drift. What is the best way to detect and retrain the model automatically?

A data analyst runs a complex SQL query in BigQuery that joins multiple large tables and receives the above error. Which action is most likely to resolve the issue?

A data engineer is designing a streaming pipeline with Cloud Pub/Sub and Cloud Dataflow. They need to guarantee at-least-once delivery and handle occasional duplicates. Which TWO configurations should they implement?

Your Looker dashboard uses a BigQuery connection. You notice that some queries take over a minute. Which service can you enable to cache results in memory for sub-second Looker queries?

Drag and drop the steps to create a Cloud Function triggered by Cloud Storage events into the correct order.

A data engineer needs to schedule a nightly transfer of data from an Amazon S3 bucket to Cloud Storage. Which two steps are required to achieve this? (Choose TWO.)

You have deployed a classification model on Vertex AI Endpoints. The model's training data had a balanced class distribution, but over time, the production data has shifted such that one class appears 90% of the time. The model's overall accuracy remains high, but the recall for the minority class has dropped significantly. What is the best approach to detect and address this issue?

You manage a BigQuery reservation with 500 baseline slots and autoscaling up to 2000 slots. Your team runs a mix of interactive queries and batch load jobs. During peak hours, you notice that interactive queries are throttled when autoscaling slots are consumed by long-running batch loads. How can you ensure interactive queries get priority access to slots?

A company uses GKE to run microservices. They want to ensure the application restarts automatically if it becomes unresponsive. Which probes should they configure in their pod spec?

You are building a BigQuery table that contains nested and repeated fields (e.g., order with line items). You need to write a query that counts the number of line items per order. Which TWO SQL functions/techniques can you use?

You run batch predictions using Vertex AI Batch Prediction on a tabular dataset. The job processes 1 million rows and takes 6 hours to complete. You need to reduce the processing time to under 2 hours without increasing cost significantly. What should you do?

Your Dataflow streaming pipeline is experiencing increasing system lag over time. You have enabled autoscaling and the pipeline is using the default streaming engine. Which metric should you monitor in Cloud Monitoring to determine if the pipeline is falling behind due to slow processing or due to a bottleneck in the output sink?

Your streaming Dataflow pipeline reads from Pub/Sub, enriches data with a side input, and writes to BigQuery. You need to update the enrichment logic without draining the pipeline, to minimize data loss and maintain exactly-once semantics. What should you do?

A company needs to store transactional data for a global customer base with strong consistency and 99.999% availability SLA. They anticipate millions of transactions per day across multiple regions. Which TWO storage options meet these requirements? (Choose 2)

A company is designing a data processing system that must handle both batch and streaming workloads with unified pipeline code. Which two Google Cloud services are most suitable for implementing a unified batch and streaming pipeline? (Choose TWO.)

An organization needs to store transactional data for a global e-commerce platform with strong consistency across regions and an SLA of 99.999% availability. The application requires SQL semantics with horizontal scaling. Which Google Cloud database should they choose?

A data engineer is designing a pipeline that reads from Cloud Pub/Sub, aggregates events into 5-minute windows, and writes the results to BigQuery. The engineer wants to ensure that late-arriving data (up to 2 minutes late) is included in the correct window. Which Dataflow feature should they configure?

A financial services company must comply with GDPR "right to be forgotten". They store customer transactions in BigQuery partitioned by date. When a user requests deletion, all their data must be removed within 48 hours. The deletion requests are received via a Pub/Sub topic. What is the most scalable and cost-effective approach?

A data engineer needs to transfer 500 TB of archival data from an on-premises NAS to Cloud Storage. The on-premises network has limited bandwidth (100 Mbps). Which transfer method should they recommend?

A gaming company uses Pub/Sub to ingest player events and Dataflow for real-time analytics. They notice that the Pub/Sub subscription backlog is growing despite the Dataflow pipeline running continuously. The pipeline has a 1-hour window for aggregations. What is the most effective way to reduce the backlog?

A company is designing a data lake on Cloud Storage with different zones. They need to enforce data retention so that objects in the 'raw' zone are automatically deleted after 1 year. Which TWO actions should they take? (Choose 2 correct options)

A startup is deploying a PyTorch model on Google Cloud. They need to serve predictions for a mobile app with bursty traffic. Which service is most cost-effective?

A company wants to use Dataprep to clean and transform raw CSV files stored in Cloud Storage before loading into BigQuery. The data quality checks show missing values and inconsistent date formats. Which Dataprep feature should they use to handle these issues?

You are designing a data pipeline that processes streaming events with late-arriving data (up to 2 hours late). The pipeline must compute hourly aggregations and emit results as soon as possible, but must also accurately update results when late data arrives. You want to minimize overall processing cost. Which Dataflow windowing and trigger configuration should you use?

A data engineer configures the above lifecycle rule on a Cloud Storage bucket that stores daily log files. After 60 days, they notice that files older than 30 days have been transitioned to Nearline, but files older than 90 days are still present. What is the most likely cause?

Your company is building a real-time anomaly detection system for financial transactions. The system must process streams of transactions and flag anomalies within seconds. The volume is moderate (5000 transactions per second). You want a fully managed solution that integrates with BigQuery for historical analysis. Which service should you use for stream processing?

A marketing team needs to run ad-hoc SQL queries on terabytes of clickstream data stored in Parquet files in Cloud Storage. They want a serverless solution with no cluster management and the ability to query external data without loading. Which service should they use?

A company uses Dataproc for transient clusters. Which TWO actions can reduce costs?

A company needs a fully managed, PostgreSQL-compatible database that supports both transactional (OLTP) and analytical (OLAP) workloads with low latency. They want to minimize operational overhead. Which two Google Cloud services should they consider? (Choose two.)

Refer to the exhibit. This log entry was generated by Vertex AI Model Monitoring for a production model. What should the data engineer do to address this issue?

A data pipeline uses Cloud Composer (Airflow) to orchestrate Dataproc jobs. Each job submits a Spark application that reads from BigQuery and writes to Cloud Storage. The pipeline runs nightly and takes 6 hours. Management wants to reduce costs. Which approach is most effective?

A financial services company uses a custom container on Vertex AI Prediction to serve a fraud detection model. The container runs a Flask app that loads a large feature engineering library (~2 GB) at startup. The model is updated weekly. For the past two weeks, the new model version has been failing health checks and showing 'Container failed to start' errors in the logs. The previous versions worked fine. You inspect the container image and confirm it is built correctly using Cloud Build. The only change in the latest build is an updated version of the feature engineering library. What is the most likely cause and how should you fix it?

A team has set up a push subscription to an HTTPS endpoint. They notice that messages are not being acknowledged and are resent every 10 seconds. What is the most likely issue?

A company runs a Dataproc cluster with 10 worker nodes for a Spark streaming job that processes data from Pub/Sub (via Pub/Sub Lite) and writes to Cloud Storage. They observe that the job is producing many small files in Cloud Storage, leading to high costs and performance issues in downstream batch pipelines. The team wants to consolidate output files while maintaining low latency. What is the best solution?

A company runs a Dataflow streaming pipeline that processes financial transactions. They need to apply a new transformation that enriches the data with a lookup from Cloud Bigtable without stopping the pipeline. The pipeline must be updated in a way that minimises data loss and preserves exactly-once semantics. What is the recommended approach?

Match each Google Cloud IAM role to its description.

You are troubleshooting a Dataproc cluster that runs nightly Spark jobs. The jobs are failing with out-of-memory errors. You want to reduce costs while fixing the issue. Which combination of actions should you take? (Select the BEST answer.)

A Dataflow batch job frequently fails with 'OutOfMemoryError'. Which THREE are common causes? (Choose 3)

A data scientist is using Vertex AI to train a model and wants to ensure that the training code and environment are reproducible. Which approach should they take?

Refer to the exhibit. A Dataflow pipeline is failing intermittently with the shown error. Which step should the team take to ensure data quality and prevent such errors?

A company is using BigQuery for analytics and needs to ensure that certain columns containing PII are encrypted with a customer-managed key (CMEK). Which approach should they take?

A company deploys a machine learning model to Vertex AI for real-time predictions. After deployment, they notice that prediction latency spikes during peak traffic hours. Which approach should they take to reduce latency without sacrificing accuracy?

A company is designing a real-time clickstream analytics pipeline using Pub/Sub and Dataflow. The pipeline must handle late-arriving data (up to 1 hour) and ensure exactly-once processing. Which Dataflow feature should be configured to handle late data correctly?

A team needs to orchestrate a complex ETL workflow that includes conditional branching (if new data arrives, run transformation A, else run transformation B), error handling, and coordination across multiple services. Which service should they use?

A company is building a data lake on Cloud Storage. They need to organise data into zones for raw, curated, and processed layers. Which TWO practices should they follow? (Choose 2.)

A company uses Vertex AI Workbench notebooks for data exploration and model development. They want to ensure that the notebook environment can access BigQuery data using the same permissions as the user's Google Cloud account. What is the recommended setup?

A company wants to replicate a Cloud SQL (PostgreSQL) database to BigQuery in near real-time for analytics. The volume is about 10GB per day with frequent updates and deletes. They need to capture changes with low latency and ensure exactly-once delivery to BigQuery. Which approach should they use?

You are migrating an on-premises PostgreSQL database to Cloud SQL. You need to continuously replicate changes to BigQuery for real-time analytics with minimal latency. Which service should you use?

A data scientist has trained an XGBoost model on Vertex AI and wants to deploy it to an endpoint with automatic scaling based on traffic. What is the recommended deployment approach?

A model deployed on Vertex AI Endpoint is making predictions with high accuracy but the business team suspects bias against a certain demographic group. You need to analyze the model's predictions for fairness. What is the most effective approach?

A team wants to use Cloud Pub/Sub Lite for a high-throughput, low-cost messaging system. They need exactly-once delivery to subscribers. What should they know about Pub/Sub Lite's delivery guarantees?

A company has a model that requires GPU for inference and has strict latency requirements. They deployed on Vertex AI Endpoint with autoscaling but observe cold start latency when scaling up. What is the best solution?

A company is building a data lake on Cloud Storage for log analysis. Log files (CSV) arrive every 5 minutes from multiple sources. The files should be ingested into BigQuery for reporting within 15 minutes. Which approach best meets the requirements with minimal operational overhead?

Drag and drop the steps to deploy a Cloud Dataflow pipeline from a template into the correct order.

A data engineer needs to query data from BigQuery and another cloud provider's storage (AWS S3) using a single SQL query. The data must not be moved or copied to GCP. Which Google Cloud service should they use?

A company uses Dataproc to run daily Spark ML jobs. The jobs run for 2 hours each day. The team wants to reduce costs without changing job characteristics. Which strategy is MOST cost-effective?

An organization wants to enforce that data in a Cloud Storage bucket cannot be deleted or overwritten for 7 years due to regulatory compliance. Which Cloud Storage feature should they use?

A data engineer needs to design a batch processing pipeline using Cloud Data Fusion. The pipeline should read data from Cloud Storage, perform transformations (join, filter, aggregate), and write to BigQuery. What is the most efficient way to handle the transformations?

A company is designing a data lake on Google Cloud. They need to store raw data in multiple formats (CSV, Parquet, Avro) and allow various downstream processing frameworks. Which two storage solutions provide flexibility and scalability? (Choose two.)

A data engineer needs to load 10 TB of CSV files from Amazon S3 into Google BigQuery on a daily basis. Which service should they use to automate this transfer?

A healthcare company streams patient monitoring data to Cloud Pub/Sub. A Dataflow pipeline reads the stream, enriches with patient records from BigQuery, and writes to Bigtable for real-time queries. The BigQuery lookup is slow and causes pipeline lag. What is the best approach to improve performance?

You are responsible for deploying a PyTorch model for real-time inference. The model requires GPU acceleration. You want to minimize infrastructure management overhead. Which serving option should you choose?

Your company uses Pub/Sub to ingest clickstream data. Messages must be processed in order for the same user_id. How should you configure the Pub/Sub subscription to guarantee ordering?

A data engineer needs to store transactional data for an e-commerce application that requires ACID compliance, automatic failover, and point-in-time recovery. The expected throughput is a few thousand transactions per second. Which Google Cloud storage option should they choose?

A company is designing a real-time analytics pipeline using Pub/Sub and Dataflow. They need to ensure exactly-once processing and handle late-arriving data. Which two configurations should they implement? (Choose TWO.)

A company wants to use dbt (data build tool) to transform data in BigQuery. They have a Cloud Storage bucket containing raw CSV files that are loaded daily into BigQuery via an external table. Which dbt feature should they use to modularize the transformation logic and handle dependencies between models?

Your company runs a real-time recommendation system for a popular e-commerce website using a machine learning model deployed on Vertex AI Endpoints. The model takes user features and product catalog data as input and returns top-10 product recommendations. The system uses a feature store to serve user embeddings and product embeddings. Recently, the recommender team retrained the model with a new algorithm and deployed it as a new version. Since the deployment, the latency for recommendation requests has increased from 100ms to 500ms on average, exceeding the 200ms SLO. The model accuracy is acceptable, and there are no errors. The endpoint uses an n1-standard-8 machine with a single GPU. The new model is larger but still fits on the GPU. You investigate and find that the GPU utilization remains low (<20%), but CPU utilization is high (90%). What should you do to reduce latency while maintaining accuracy?

A Dataflow streaming pipeline uses stateful transformations with per-key state and timers. After a deployment, the team observes that the pipeline is reprocessing events from the last 30 minutes every time it restarts. The pipeline's checkpoint is configured to persist every 10 seconds. Which change should be made to prevent unnecessary reprocessing?

An organization is moving on-premises Hadoop workloads to Google Cloud. They need to minimize code changes and manage transient clusters for cost savings. Which two Google Cloud services should they consider? (Choose TWO.)

A company uses Pub/Sub to decouple services. They have a topic with two subscriptions: Subscription A is a push subscription that sends messages to a Cloud Function; Subscription B is a pull subscription used by a Dataflow job. They need to ensure that messages are processed in order for a specific device_id. Which TWO configurations should they apply?

A company runs a production Dataflow streaming pipeline that reads from Pub/Sub, groups events by customer ID, and writes to BigQuery. The pipeline uses global windows with triggers. After a recent code change, the pipeline started generating duplicate events in BigQuery for the same customer ID. The previous version did not have duplicates. The team reviews the code and sees that the trigger was changed from 'afterProcessingTime' to 'afterWatermark'. What is the most likely reason for duplicates?

You are a data engineer at a financial services company that uses Vertex AI to train and deploy models for credit risk assessment. The company has strict governance requirements: every model version must be approved by the risk committee before going to production. The approval process can take several days. Currently, the team trains a new model weekly and manually deploys it to a staging endpoint for review, then manually promotes to production after approval. This process is error-prone and slow. You want to automate the pipeline: training should trigger automatically when new data arrives, the model should be automatically deployed to a staging endpoint for review, and after manual approval, it should be promoted to production. Additionally, you need to ensure that if a model in staging performs poorly (e.g., low accuracy), it should not be promoted even if approved. What should you do?

Your organization has a BigQuery flat-rate reservation with 500 slots. During peak hours, queries are queued and you need additional capacity temporarily. You want to add slots for a burst of activity without committing to a long-term purchase. What should you do?

You are building a real-time fraud detection system using BigQuery streaming and a BQML logistic regression model. The model must be retrained every hour with new labeled data. What is the MOST cost-effective approach to serve predictions with low latency?

A data scientist has iterated on a model and produced a new version. The organization requires the ability to roll back to the previous version quickly if the new version performs poorly in production. Which approach should be used?

You are operating a streaming data pipeline that uses Cloud Pub/Sub and Dataflow. The data source sometimes emits events that are delayed by several minutes due to network issues. Your pipeline must produce accurate aggregations (e.g., counts per minute) even for late data, but you also need to avoid waiting for a long time before emitting results. Which approach should you use?

A company wants to build an event-driven application that processes images uploaded to a Cloud Storage bucket. The processing takes up to 10 minutes per image and should be automatically triggered. Which compute option should they use?

The exhibit shows a Cloud Logging query result. A data engineer sees this log for a streaming Dataflow job. What is the most likely cause?

A data pipeline uses Pub/Sub to ingest events, a Dataflow streaming pipeline to process them, and writes results to BigQuery. The pipeline must handle occasional duplicate events without causing duplicate rows in BigQuery. What is the best approach?

You are a machine learning engineer at a FinTech company. Your team has developed a credit risk model using XGBoost and deployed it on Vertex AI Prediction using a custom container. The model is used for real-time credit decisions, and the endpoint is configured with a single machine type (n1-standard-4) and min_replica_count = 2, max_replica_count = 10. Recently, the team observed that during a promotional campaign, the endpoint's prediction latency increased from 200ms to over 2 seconds, and some requests resulted in 503 errors. You check the Cloud Monitoring metrics and see that CPU utilization reached 100% on the existing replicas, but the number of replicas never scaled beyond the initial 2. The deployment uses a custom container that runs a TensorFlow Serving-like model server. The container image is stored in Artifact Registry. The Vertex AI endpoint is configured with a traffic split of 100% to this model version. What is the most likely cause of the scaling failure, and what step should you take to resolve it?

After deploying a model to Vertex AI Endpoints, the prediction responses include unexpected data. The model returns logits instead of probabilities. What is the most likely cause?

A streaming Dataflow pipeline ingests events from Cloud Pub/Sub and writes to BigQuery. The event schema evolves occasionally (new columns added). The pipeline fails when new columns appear. What is the best long-term solution?

You are using BigQuery ML to train a matrix factorization model for a recommendation system. The training data consists of user-item interactions. You notice that the model is overfitting. Which of the following hyperparameter changes would most likely reduce overfitting?

A company uses Cloud Build to deploy containerized applications. They want to ensure build and deployment quality. Which THREE steps should they include in their CI/CD pipeline? (Choose three.)

Which TWO factors should be considered when choosing between Cloud Dataflow and Dataproc for a batch processing pipeline?

A data engineer needs to build a real-time dashboard in Looker Studio that displays live sales data from BigQuery. The dashboard must refresh every minute. The underlying BigQuery table is updated continuously via streaming inserts. Which two approaches can reduce query cost and latency? (Choose TWO)

Your company wants to analyze real-time user clickstream data from a website. The data arrives as JSON messages via an HTTP endpoint. The pipeline should be able to handle spikes in traffic, provide low-latency insights, and store the raw data in a data lake for historical analysis. Which Google Cloud service should you use to ingest and process the streaming data?

A data engineer wants to create a data lake on Google Cloud for storing raw streaming data, then transform it into curated and processed zones for analytics. The data is in Avro format and will be queried by BigQuery. Which two services are MOST suitable as the primary storage and query interface?

A data science team deploys a TensorFlow image classification model to Vertex AI Prediction. The model performs well in offline evaluation but shows a 15% drop in accuracy in production. The production data distribution has shifted compared to the training data. The team needs to continuously monitor and retrain the model. Which solution is most appropriate for detecting drift and triggering retraining?

A Dataflow pipeline using Apache Beam processes unbounded data from Pub/Sub. The pipeline uses fixed windows of 1 minute and a trigger that fires early every 30 seconds and at watermark. The team observes that the output pane for window [10:00:00, 10:01:00) contains events with timestamps from 10:00:15 and 10:00:45, but also an event with timestamp 10:02:00. What is the most likely cause?

A company is migrating on-premises Apache Kafka workloads to Google Cloud. They want to minimize changes to existing producer and consumer applications while leveraging managed services. Which TWO services should they consider? (Choose 2)

Drag and drop the steps to create a Cloud Bigtable instance and table using the CLI into the correct order.

A team wants to enforce data quality rules on BigQuery tables using Dataplex. They need to run column-level checks for null values and row-level checks for value ranges on a schedule. Which Dataplex feature should they use?

A company wants to move data from an on-premises MySQL database to BigQuery for analytics. They need to capture all changes (inserts, updates, deletes) in near real-time and also perform an initial historical load. Which approach meets these requirements with minimal operational overhead?

A company wants to use Pub/Sub Lite to reduce costs for a high-throughput, low-latency streaming pipeline. However, they have a requirement to retain messages for up to 7 days for reprocessing. Which Pub/Sub Lite configuration supports this retention?

A data engineer needs to monitor a Pub/Sub-based streaming pipeline. Which two Cloud Monitoring metrics should be used to detect a backlog of unprocessed messages? (Choose two.)

A company is migrating their on-premises Apache Spark jobs to Google Cloud Dataproc. They want to minimize operational overhead and cost for jobs that run only a few times per day. Which TWO strategies should they adopt? (Choose TWO.)

Your team wants to continuously monitor a deployed model's performance in production. They need to detect when the model's predictions become unreliable due to changes in the real world (e.g., new customer behavior). Which Vertex AI service should they use?

A company uses Dataflow to process data with Apache Beam in Python. The pipeline reads from Pub/Sub, applies a ParDo that calls an external API for enrichment, and writes to BigQuery. The external API has rate limits and occasionally fails. To improve reliability, which THREE strategies should be implemented? (Choose 3)

A company wants to design a data pipeline for real-time fraud detection. The system must process streaming financial transactions, enrich them with user profiles from a lookup table, and flag suspicious activities within seconds. Which architecture pattern would be MOST suitable?

In the Vertex AI Pipeline component YAML exhibit, the component is designed to evaluate a model and produce metrics. If the threshold_accuracy is set to 0.85, what is the expected behavior of this component?

A company needs to process streaming sensor data from millions of devices with sub-second latency, apply transformations, and write results to BigQuery for real-time dashboards. The data volume varies, and they want to avoid managing servers. Which service should they use?

You need to run a one-time data transformation job on a small CSV file (100 MB) using a visual, code-free interface. Which Google Cloud service is designed for this?

Refer to the exhibit. A developer sees this log entry when trying to get a prediction. What is the most likely cause?

Refer to the exhibit. A subscriber is unable to pull messages from the topic. What is the most likely cause?

A company uses Cloud Pub/Sub with pull subscriptions to process orders. The application requires at-least-once delivery and the ability to process orders in order per customer_id. Which THREE features should they configure? (Choose three.)

A company is running a Cloud Dataflow streaming pipeline that aggregates events in 1-minute windows. They notice that the watermark is lagging significantly behind real-time. What is the most likely cause?

Your team wants to share a BigQuery dataset with another project while ensuring that users from that project can only query specific tables. Which BigQuery feature should you use?

A data scientist is using AutoML Tables to build a classification model for predicting customer churn. The dataset is highly imbalanced (only 1% churn). Which strategy should they use to handle the class imbalance within AutoML Tables?

A financial services company uses Cloud Composer to orchestrate daily batch jobs. One job extracts data from MongoDB to Cloud Storage, then loads into BigQuery, and finally runs a Dataflow pipeline for aggregations. The Dataflow job fails intermittently. They want to automatically restart only the failed Dataflow job without re-running the earlier extraction and load. Which Airflow operator configuration should they use?

A data engineer notices that Spark jobs on the Dataproc cluster shown often fail with executor lost errors. What is the most likely reason?

A team uses Vertex AI Feature Store for real-time features. They notice that features are frequently missing during prediction serving. What is the best practice to handle missing features?

A data engineer needs to process large CSV files (hundreds of GB) stored in Cloud Storage using Spark on a Dataproc cluster. The job performs a series of transformations and aggregations. Which configuration is most cost-effective and operationally efficient?

You are designing a Dataflow pipeline that needs to exactly-once process events from Pub/Sub and write to BigQuery using the Storage Write API. The pipeline may restart and could reprocess some messages. What setting ensures exactly-once semantics for the output?

A data engineer is designing a Dataflow pipeline that reads from a Kafka topic (using Pub/Sub for Kafka) and writes to BigQuery. The data schema may change over time, with new fields appearing. The engineer wants to handle schema drift automatically without failing the pipeline. Which approach should the engineer use?

A company is migrating their on-premises Hadoop workloads to Google Cloud. They want to use Dataproc for data processing and need to minimize costs for non-critical batch jobs that can tolerate interruptions. Which TWO configurations should they use?

A company is using Pub/Sub to ingest clickstream events. They need to ensure that events are delivered to a subscriber at least once, but duplicates can be tolerated. They also need to filter events by type before processing. Which subscription configuration should be used?

A machine learning team wants to deploy a new model version for canary testing, where only 5% of traffic is routed to the new version. Which Vertex AI endpoint configuration supports this?

A company has a BigQuery dataset containing sensitive customer data. They want to share a subset of this data with external partners, ensuring that partners can only see specific columns and rows. Which BigQuery feature should they use?

A data engineering team needs to build a data integration pipeline that involves connecting to multiple sources, performing data transformations with visual editing, and then running custom machine learning algorithms. The team has both data analysts and data scientists. Which approach is most suitable?

A data engineer is setting up CI/CD for a machine learning model using Cloud Build and Vertex AI. Which two components are essential? (Select 2)

A data engineer is using Vertex AI Workbench to develop a custom ML model. They want to store and version datasets, track experiments, and register models. Which three Vertex AI services should they use? (Choose THREE)

A company wants to stream data from Cloud Pub/Sub into BigQuery with minimal latency. They have a small team and limited operational resources. Which approach is best?

A company deploys a TensorFlow model on Vertex AI for online predictions. They want to monitor model performance in production to detect degradation. Which TWO practices should they implement? (Choose 2.)

A team needs to store transactional data for an e-commerce application that requires ACID transactions, automatic backups, and point-in-time recovery. The expected workload is under 10,000 QPS. Which database should they choose?

You are responsible for monitoring a production ML model on Vertex AI. The model predicts loan approval probability. The business team reports that the model's predictions are becoming less accurate over the last week. You check the model's monitoring dashboard and see that the prediction distribution has changed significantly. What is the most likely issue?

A data engineer needs to train a linear regression model in BigQuery ML using a table with 10 million rows. The model will predict sales based on features like advertising spend, seasonality, and store location. Which SQL statement should they use to create and train the model?

After deploying a model, the team notices that predictions are significantly different from training data distribution. What should they do?

An online retailer uses BigQuery for analytics. They have a time-series table with 5 billion rows and new data arrives every day. They want to optimize query performance and reduce costs by ensuring that queries scan only the partitions they need. Which table design should they use?

You need to monitor the health of a Pub/Sub subscription that feeds into a Dataflow pipeline. Which TWO Cloud Monitoring metrics are most relevant to detect if messages are not being acknowledged promptly? (Choose 2)

A data analyst wants to rank products by sales within each category. They need to assign a unique rank to each product, with no gaps in the ranking numbers (i.e., ties should have different ranks). Which window function should they use?

A team wants to transfer data from an on-premises Hadoop cluster to Cloud Storage for processing. The cluster is located in a remote area with limited bandwidth. They need to transfer 500 TB of data. Which service should they use?

An organization is implementing a data lake on Google Cloud using Cloud Storage. They need to process both batch and streaming data with a unified pipeline. The team has experience with Apache Beam. Which architecture should they use to minimize operational overhead?

A data engineer needs to create a BigQuery table that is partitioned by ingestion time and clustered by customer_id and transaction_date. They also want to limit access so that only users from a specific domain can query the table. Which approach should they use?

A financial services company needs to explain predictions from a complex ensemble model for regulatory compliance. Which Vertex AI service should they use?

A company needs to process high-throughput streaming data with low latency. They are considering Cloud Pub/Sub for ingestion and Cloud Dataflow for processing. However, they are concerned about cost. Which alternative to Cloud Pub/Sub would reduce costs while still meeting the throughput requirements?

A company wants to ingest data from an on-premises Oracle database into BigQuery in near real-time with minimal latency. The database has a high volume of inserts and updates. Which service should they use?

Refer to the exhibit. A data scientist notices that the evaluation component rarely passes the threshold, causing the pipeline to fail often. What should they do to improve efficiency?

A company uses BigQuery ML to create a classification model. The model is used for batch prediction on a weekly basis. After six months, the data distribution shifts, and model accuracy drops. Which approach should the company take to maintain model performance?

Which TWO are valid approaches to handle late-arriving data in a Cloud Dataflow streaming pipeline?

A data team needs to transfer 200 TB of data from Amazon S3 to GCS. The transfer must be incremental, and they need to monitor the transfer progress. Which THREE components should they use?

Which TWO actions should you take to ensure model reliability in a production Vertex AI Endpoint?

A data science team needs to ensure that a deployed Vertex AI model can handle varying traffic patterns with minimal latency and cost. What should they do?

A retail company uses Cloud Dataflow for a streaming pipeline that aggregates sales events from thousands of stores. The pipeline writes aggregated results to BigQuery every 5 minutes. Recently, the Dataflow job has been restarting multiple times a day with the error: 'Worker ran out of memory' in the logs. The streaming engine is enabled. The pipeline uses keyed state (ParDo with stateful processing) to maintain per-store counters. The average event size is 2KB, and the throughput is 2,000 events/sec. You need to resolve the out-of-memory issues without losing data. What should you do?

A retail company uses Vertex AI Pipelines to automate monthly retraining of a recommendation model. The pipeline consists of three steps: (1) extract data from BigQuery, (2) train a TensorFlow model on Vertex AI Training, (3) upload the model to Vertex AI Model Registry and deploy to an endpoint if performance metrics improve. Recently, the pipeline has been failing at step 2 with the error: 'The job was cancelled by the system because it exceeded the maximum training time of 3600 seconds.' You have confirmed that the training code is correct and the data size has not changed significantly. What should you do to fix this pipeline failure? A) Reconfigure the pipeline to use a larger machine type for training. B) Set the training timeout to 7200 seconds in the pipeline configuration. C) Reduce the training dataset size by sampling fewer rows. D) Switch from TensorFlow to a simpler model framework.

A company uses Vertex AI Feature Store for serving features to both training and prediction. The team notices that predictions made shortly after training use different feature values, causing a training-serving skew. What is the most effective way to prevent this skew?

Which BigQuery feature allows you to query data directly from Cloud Storage without loading it into BigQuery storage?

A company wants to migrate their on-premises Teradata data warehouse to BigQuery. They need an automated, one-time transfer of historical data (10 TB) and ongoing incremental daily syncs. Which Google Cloud service should they use?

A manufacturing company wants to detect anomalies in sensor data from thousands of IoT devices in real time. The data is streaming into Pub/Sub. The best solution should use a machine learning model served from AI Platform that scores sensor readings aggregated over 5-minute windows. Which pipeline design meets these requirements?

You are using Cloud Workflows to orchestrate a series of API calls. You need to handle errors and retries. Which THREE features of Cloud Workflows can you use? (Choose THREE.)

A data pipeline processes streaming data with Dataflow. The team notices occasional data duplication in BigQuery. What is the best approach to ensure exactly-once processing?

A Dataflow streaming pipeline that uses global windows and triggers every 5 seconds is experiencing increasing lag and high system latency. The pipeline reads from Pub/Sub, transforms data with a ParDo, and writes to BigQuery. Which action is most likely to reduce lag?

A company runs large batch prediction jobs on Vertex AI every day. They want to minimize costs while ensuring the jobs complete within a 4-hour window. The model requires significant memory. What is the most cost-effective approach?

A financial company needs to process batch trades data daily and ensure that if a transformation step fails, the entire daily run is retried from the beginning. Which design pattern is appropriate?

A company uses Cloud Composer (Airflow) to orchestrate a daily batch job that runs a custom Python script on a Compute Engine instance. The process is slow because the instance takes 2 minutes to boot. How can you reduce the total runtime?

A data pipeline uses Cloud Data Fusion to perform ETL jobs. The pipeline reads from BigQuery, transforms data using Wrangler, and writes to Cloud Storage. The team notices that the pipeline runs slower than expected. They suspect the Data Fusion instance is under-provisioned. Which action should be taken to improve performance?

A data scientist developed a model using custom training on Vertex AI. They want to automate the entire training-to-deployment process. Which service should they use?

A Dataflow pipeline is processing a high-volume streaming data stream. The job is lagging behind by 30 minutes, and the Dataflow monitoring UI shows high system latency with low CPU utilization. Which action should be taken to improve throughput?

A team uses Vertex AI Pipelines to automate retraining of a model every month. The pipeline includes data preprocessing, training, and deployment steps. After a recent update, the pipeline fails intermittently with a timeout error during the deployment step. What is the most likely cause?

Which Google Cloud service is a fully managed relational database for MySQL, PostgreSQL, and SQL Server, offering automatic replication and backups?

A company wants to build a data lake on Cloud Storage for raw, curated, and processed data zones. They need to enforce data governance including column-level security and row-level filtering for BigQuery queries. Which solution should they use?

A company uses Cloud Composer (Airflow) to orchestrate a data pipeline. One DAG has many tasks that run in parallel and dependencies that span multiple days. Recently, the DAG started failing with 'DagRun already exists' errors. What is the most likely cause?

A team has trained a model using AutoML Tables. They want to deploy it for batch predictions on a schedule. What is the simplest approach?

You are designing a batch data pipeline that runs daily to ingest data from an on-premises database into BigQuery. The ingestion volume is approximately 50 GB per day. The data must be available in BigQuery by 6 AM each day. The on-premises database supports change data capture (CDC) via logs. Which approach minimizes operational cost and complexity?

A company uses Cloud Data Fusion for ETL pipelines. They need to transform sensitive data (PII) by masking certain columns before writing to BigQuery. They also need to ensure the pipeline can be monitored and restarted from failure points. Which THREE features should they use?

Your company uses Kafka for event streaming. You want to run Kafka on Google Cloud with the ability to auto-scale clusters and use managed infrastructure. Which service should you choose?

A machine learning engineer needs to deploy a custom TensorFlow model for online predictions with low latency. The model is already trained and saved in SavedModel format. Which Vertex AI service should they use?

A financial services company runs a batch Dataflow pipeline daily to process transaction data. The pipeline reads from Cloud Storage, performs complex transformations, and writes to BigQuery. Recently, the pipeline has been failing intermittently with the error: 'Workflow failed. Causes: (9c3f7a2b1d4e): The worker missed 2000 data samples in the last 30 seconds. This can be caused by a variety of factors, including slow work items, network issues, or resource contention.' The team has already increased the number of workers and tried using e2-standard-8 machine types, but the issue persists. The pipeline processes approximately 500 GB of data per run and uses approximately 200 workers. The team suspects that the issue might be related to shuffle operations. What should the team do next to resolve the issue?

Your team uses dbt to transform data in BigQuery. You need to schedule dbt runs to refresh materialized tables and views every hour. The transformations include both full refreshes and incremental models. What is the most efficient way to orchestrate these dbt runs on Google Cloud?

A mobile app needs a NoSQL database that supports offline synchronization when the device goes offline and later reconnects. Which Google Cloud database should be used?

Which THREE of the following are best practices when designing a Cloud Dataflow pipeline for batch processing? (Choose three.)

A data engineer is building a Cloud Workflows workflow that orchestrates multiple Cloud Functions and API calls. The workflow should handle transient failures with retries and send a notification to a Pub/Sub topic if the workflow ultimately fails. Which THREE steps should the engineer include in the workflow definition?

A company uses Cloud Composer (Airflow) to orchestrate pipelines. They want to implement a pattern where a task polls for a file arrival in Cloud Storage and then triggers subsequent tasks. Which THREE Airflow concepts are essential? (Choose 3)

A company runs a Dataflow streaming pipeline that reads from Pub/Sub and writes to BigQuery. They experience a sudden spike in data volume causing BigQuery write throughput to be exceeded, resulting in errors. Which strategy should they implement to handle this gracefully?

You are designing a near-real-time CDC pipeline to replicate changes from an on-premises PostgreSQL database to BigQuery for analytics. The source database has high transaction volume and you must ensure minimal impact on the source. Which Google Cloud service should you use to ingest the change data?

A data engineer needs to migrate a schema from BigQuery where a column is currently REQUIRED and needs to become NULLABLE. Which TWO statements are correct? (Choose 2)

A data engineer needs to move 500 TB of archival data from an on-premises Hadoop cluster to Cloud Storage. The network bandwidth is limited to 100 Mbps, and the transfer must complete within 30 days. Which method is most cost-effective and reliable?

Your company is building a data processing system that ingests sensor data from millions of devices, processes it in near real-time to detect anomalies, and stores raw and processed data for long-term analytics. The system must meet a 99.9% uptime SLA and minimize data loss. Which THREE design choices are best? (Choose three.)

A data engineer deploys a TensorFlow model on Vertex AI using a custom container. After deployment, online prediction requests sometimes fail with a 500 error and the message 'Out of memory'. The model requires significant memory during inference. Which action should the engineer take to resolve this issue?

Your company deploys a classification model on Vertex AI for online predictions. The model is an XGBoost model trained on tabular data with 500 features. The endpoint uses a single n1-standard-4 node. After deployment, users report that predictions take 8-10 seconds on average, while the required SLA is under 2 seconds. You have already verified that the model is not large (under 100 MB) and the input data size is small. The endpoint does not scale automatically. Which action should you take to reduce latency to meet the SLA? A) Change the machine type to n1-highcpu-4 to prioritize compute over memory. B) Enable autoscaling by setting min replicas to 2 and max replicas to 5. C) Switch to a custom container that preloads the model into memory. D) Reduce the number of features by half.

A company uses BigQuery for analytics and needs to ensure that certain columns containing PII are encrypted at query time so that only authorized users can decrypt. What should they use?

A company is migrating its on-premises Apache Spark jobs to Dataproc. The jobs read from and write to Cloud Storage. After migration, the jobs are slower than expected. The Dataproc cluster uses standard worker machines with local SSDs. What is the most likely cause of the performance degradation?

A gaming company uses Cloud Pub/Sub to ingest player activity events. A Dataflow streaming pipeline consumes these events, performs stateful processing to compute session metrics, and writes results to Cloud Bigtable for low-latency queries. Recently, the pipeline's processing latency increased, and the Bigtable write throughput dropped. Monitoring shows that the pipeline is experiencing a high rate of 'out-of-order' messages and 'duplicate' events. The Pub/Sub subscription is configured with exactly-once delivery. The Dataflow job uses a GlobalWindow with a trigger that fires every 10 seconds. What is the most likely cause and solution?

An organization needs to transfer 50 TB of historical data from an on-premises Hadoop cluster to Google Cloud Storage. The network bandwidth is limited to 100 Mbps. Which transfer method is MOST cost-effective and time-efficient?

A company has deployed a classification model on Vertex AI. They want to detect data drift in real-time for the model's input features. Which service should they use?

A startup is building a mobile app that needs to sync user data across devices in real time. They expect millions of concurrent users and need a NoSQL database with offline support and automatic multi-region replication. Which Google Cloud service meets these requirements?

A data analyst needs to transform nested and repeated fields in BigQuery. They have a table with a column of type ARRAY<STRUCT<...>>. Which SQL function should they use to flatten the array into individual rows for analysis?

A data engineering team is operationalizing a machine learning model for real-time inference. They need to monitor the model's performance in production. Which THREE types of monitoring should they implement? (Choose three.)

You are designing a BigQuery data warehouse for a retail company. Queries frequently filter on order_date and customer_id. To optimize query performance and cost, which table design should you use?

A company wants to monitor the performance of a deployed model in production. Which metric indicates that the model's predictions are degrading?

You need to process a large volume of event data from Cloud Storage, apply complex transformations using Apache Spark, and then load the results into BigQuery. The data arrives in batches every hour. You want to minimize costs by using preemptible VMs. Which service should you use?

A company uses dbt on BigQuery to transform data. They want to run dbt models on a schedule and manage environments (dev, prod). Which GCP service should they use to run dbt jobs?

A data engineer is designing a streaming pipeline using Dataflow with Apache Beam. The pipeline reads from Pub/Sub, performs a stateful transformation (e.g., session windowing), and writes to BigQuery. The pipeline must handle late data and ensure exactly-once semantics. Which THREE configurations are required?

A company wants to transfer 500 TB of data from an on-premises Hadoop cluster to Google Cloud Storage (GCS) for processing with Dataproc. The on-premises network has a 1 Gbps dedicated link to Google Cloud. The data must be transferred as quickly as possible, minimizing network usage. Which transfer method should they use?

A company runs a real-time anomaly detection system on Google Cloud. Streaming data from IoT devices is ingested via Pub/Sub, processed by Dataflow (Apache Beam), and results are written to Bigtable for low-latency serving. Recently, the system has been experiencing increased latency and occasional data loss. The Dataflow pipeline shows high system lag and backlog in Pub/Sub. The Bigtable cluster has 3 nodes and is reporting high CPU utilization (over 90%). The team suspects the issue is with the pipeline configuration. They have already verified that there are no errors in the pipeline code and no network issues. Which action should they take to resolve the issue?

A company deploys a scikit-learn model on Vertex AI for online predictions. The model is packaged in a custom container with all dependencies. Users report high latency (over 5 seconds) for predictions. The model size is 2 GB. What is the most likely cause of the high latency?

A company uses Cloud Dataproc to run Spark ML jobs. The jobs are memory-intensive and often fail with OutOfMemory errors. Which action would most effectively reduce memory pressure without changing the Spark code?

A company is building a data lake on Cloud Storage with data from multiple sources. They need to apply schema-on-read and support ad-hoc SQL queries. Which architecture is most suitable?

A financial services company uses Cloud Composer to orchestrate a daily workflow that includes a Dataproc job for risk analysis. The workflow sometimes fails because the Dataproc cluster creation times out. The cluster creation typically takes 3 minutes, but occasionally takes over 10 minutes. What is the most effective way to handle this variability?

An engineer needs to create a reusable Dataflow pipeline that can be executed with different parameters without modifying code. Which Dataflow feature should they use?

Refer to the exhibit. A Dataflow pipeline writes to BigQuery table employee_records. The pipeline was working yesterday but fails today. What is the most likely cause?

You are designing a BigQuery data warehouse for a multi-tenant SaaS application. Each tenant's data must be isolated and queried only by that tenant. You need to minimise management overhead and allow tenants to be added dynamically. Which approach should you use?

Your team is running a Dataflow streaming pipeline that reads from Pub/Sub, transforms data, and writes to BigQuery. You notice that the pipeline's backlog is growing and the processing latency has increased from seconds to minutes. You need to diagnose and resolve the issue. Which TWO actions should you take? (Choose two.)

A data engineer wants to train a linear regression model in BigQuery ML to predict sales. The training data includes a categorical feature with 1000+ unique values. Which method is most appropriate to handle this feature in the CREATE MODEL statement?

You are a data engineer at a global e-commerce company. Your team manages a real-time recommendation system that ingests user clickstream events from a Pub/Sub topic (topic-clickstream). The pipeline uses Dataflow to read events, join with user profile data from Cloud Bigtable, compute recommendations using a machine learning model hosted on Cloud Run, and write results to a BigQuery table for analytics. The pipeline has been running smoothly for months, but recently the Dataflow job started failing with the error: "Workflow failed. Causes: S01:ReadPubSub/Read+Transform/ParDo(ExtractUserID)+ ... (5a3b2c1d) The job failed because a worker encountered an out-of-memory error." The Dataflow job uses the Streaming Engine feature with a worker type of n2-standard-8 (8 vCPU, 32 GB memory) and autoscaling from 2 to 20 workers. The clickstream event rate has increased from 500 events/second to 5000 events/second over the past week. The user profile data in Bigtable has also grown, with average row size increasing from 1 KB to 10 KB due to additional fields. You need to resolve the out-of-memory errors without completely redesigning the pipeline. What should you do?

A company is migrating ML workflows to Vertex AI Pipelines. They want to ensure best practices for pipeline reproducibility and debugging. Which THREE actions should they take? (Choose three.)

A data engineer is designing a batch processing system using Cloud Dataproc. Which TWO practices improve performance and reduce costs? (Choose TWO.)

A company uses AutoML Tables to train a classification model. They want to improve model performance by engineering new features from existing timestamp columns. Which three techniques can they apply within AutoML Tables? (Choose 3)

Refer to the exhibit. A Cloud Build step fails when pushing a Docker image to Artifact Registry. What is the missing IAM role for the Cloud Build service account?

A data pipeline using Cloud Pub/Sub and Cloud Dataflow is experiencing duplicate messages. The source system publishes messages at least once. What Dataflow technique ensures exactly-once processing?

A data engineer needs to build a feature engineering pipeline using Vertex AI Pipelines. The pipeline should preprocess data, train a model, and deploy it. Which two components are required to define the pipeline? (Choose 2)

A data engineer is creating a Dataflow Flex Template for a batch pipeline that reads from BigQuery and writes to Cloud Storage. They need to pass a runtime parameter for the output bucket. How should they define this parameter?

A company runs a Dataproc cluster for nightly batch jobs. The cluster uses preemptible workers for cost savings. Recently, the jobs have been failing intermittently with 'Disk quota exceeded' errors on the persistent disks attached to the preemptible workers. The cluster is configured with a master node and 10 worker nodes, each with a 100 GB persistent disk. The preemptible workers are dynamically added and removed. What is the most likely cause and the best long-term solution?

You are building a real-time fraud detection system using Dataflow. Events from Pub/Sub need to be grouped by user_id within a 5-minute window to detect suspicious patterns. Some events may be delayed by up to 2 minutes. How should you configure the window and trigger to balance accuracy and latency?

A large e-commerce company is migrating its on-premise Hadoop cluster to Google Cloud using Dataproc for batch processing. The cluster processes daily sales data from multiple sources, generates aggregated reports, and performs ad-hoc analysis. The migration is complete, but users report that jobs are running 30% slower than on-premise. The data is stored in Cloud Storage as Parquet files partitioned by date. The Dataproc cluster uses preemptible VMs for worker nodes, and the master node uses a standard VM. The jobs heavily rely on shuffling data between stages. The cluster's autoscaling is enabled with a minimum of 10 and a maximum of 50 workers. During job execution, CPU utilization on workers is low, but disk I/O is high, especially on local SSDs. The network utilization is moderate. The team suspects that the shuffle operation is causing the slowdown. Which action should the team take to improve job performance?

You need to create a time-series forecast for inventory demand using BigQuery ML. The data includes daily sales for 5 years. Which model type should you use?

A company needs to grant analysts access to a BigQuery table that contains sensitive PII columns. The analysts should be able to run aggregate queries on the entire dataset but must not see individual PII values. Which approach should the team use?

A streaming Dataflow pipeline needs to be updated without draining the existing pipeline. Which update strategy should be used?

You have a batch prediction job on Vertex AI that processes millions of records. The job is failing with an out-of-memory error. What is the best way to resolve this?

A data engineer needs to build a LookML model in Looker to define business logic and relationships for a new dataset. They want to create an 'explore' that joins an 'orders' view with a 'customers' view. Where should they define this join?

A BigQuery query fails with the error shown in the exhibit. What is the most likely cause?

A retailer wants to use machine learning to predict customer churn based on transaction history and demographic data. The dataset has 500 features, many of which are correlated. The data is highly imbalanced: only 2% churn. They need to deploy a model that provides feature importance and is interpretable. Which model type should they use in BigQuery ML?

A data engineering team is building a CI/CD pipeline for machine learning models using Cloud Build and AI Platform. Which TWO practices are essential for ensuring reproducible and safe model deployments?

A company stores raw data files in Cloud Storage in a bucket named 'raw-data'. After processing, the files are moved to a 'processed' bucket. To reduce costs, they want to automatically delete raw data older than 30 days. What should they do?

A company uses Vertex AI to serve a model that requires GPU for inference. They want to minimize cost while handling variable traffic. Which strategy should they use?

A company uses BigQuery for analytics on petabyte-scale data. They want to improve query performance by denormalizing schemas and reducing joins. Which TWO BigQuery features should they use? (Choose 2)

A company wants to use BigQuery ML to train a time-series forecasting model on historical sales data. The data is recorded daily for 3 years. They need to evaluate model accuracy using time-series aware cross-validation. Which two options should they configure in the CREATE MODEL statement? (Choose TWO)

A data engineer needs to create a BigQuery ML model for predicting customer churn using a dataset with 10 million rows and 50 features. The dataset is highly imbalanced (5% churn). Which approach should the engineer use to handle class imbalance during model training?

Your company uses Vertex AI Pipelines to automate model retraining. The pipeline has three steps: data extraction from BigQuery, feature engineering using Dataflow, and model training using a custom container on Vertex AI Training. Recently, the pipeline has been failing intermittently at the Dataflow step with a 'The job encountered a transient error. Please retry.' message. You have enabled pipeline retries with 3 attempts. However, the pipeline still fails after 3 retries. You check the logs and find that the Dataflow job requires more resources than the default worker configuration provides. Which change should you make to reduce the failure rate?

A data science team has built a model using scikit-learn. They want to operationalize it on Google Cloud without rewriting the code. Which approach should they take?

You have deployed a TensorFlow model on Vertex AI Endpoints with autoscaling. The model receives high traffic during peak hours, but you notice that inference latency increases significantly during cold starts. Which strategy would best minimize cold-start latency without incurring unnecessary cost?

A data team is migrating an on-premises Hadoop cluster to Dataproc. The cluster runs a mix of long-running services (Hive, HBase) and transient Spark jobs. They want to minimize cost while maintaining performance. Which TWO strategies should they implement?

You are using Dataproc to run a Spark job that reads data from Cloud Storage, performs aggregations, and writes results back to Cloud Storage. The job is failing with out-of-memory errors on the shuffle. Which optimization should you apply?

A data scientist deploys a new version of a fraud detection model (model2) alongside the existing model (model1) on the same Vertex AI endpoint with a 70/30 traffic split. After 24 hours, the team notices that model2's predictions are significantly different from model1's, and the fraud detection rate has increased. What is the most likely explanation for the change in predictions?

You run `gcloud ai models describe` and get the error above. The model was created successfully from a training job that completed without errors. The model ID is correct. What is the most likely cause?

You are monitoring a streaming Dataflow pipeline that reads from Pub/Sub and writes to BigQuery. In Cloud Monitoring, you notice that the 'system_lag' metric is increasing over time and now exceeds 10 minutes. The 'data_watermark' metric shows a steady lag. What is the most likely cause of the increasing system lag?

A company uses Cloud Dataproc for large-scale Spark jobs. They notice that some jobs are failing due to insufficient memory on the worker nodes. They want to improve memory management without over-provisioning. Which three configurations should they apply? (Choose 3)

A data science team wants to deploy a model that requires a custom container with specific NVIDIA CUDA version. They build the image and push to Artifact Registry. When deploying to Vertex AI, the model fails to load with an error: 'Failed to start container: invalid ELF header'. What is the most likely cause?

A company uses Cloud Spanner for a global e-commerce platform. They have a table of orders and a table of order items. To optimize performance for queries that join these tables on order_id, which Spanner schema design feature should they use?

In Cloud Composer, a DAG has two tasks: task_A (runs an Apache Spark job on Dataproc) and task_B (loads data from Cloud Storage to BigQuery). task_B must start after task_A completes. The DAG is scheduled to run hourly. Sometimes task_B starts before task_A finishes because task_A's Dataproc job appears to complete in the Airflow metadata but the data is not yet available. What is the best way to ensure task_B only runs after the data is fully written?

Match each Google Cloud service to its data processing capability.

A data pipeline reading from Cloud Storage and writing to BigQuery using Dataflow is experiencing high cost. The data is CSV and needs schema inference. What change reduces cost?

A company has a BigQuery table that is partitioned by ingestion time and clustered by the 'customer_id' column. They notice that queries filtering on 'customer_id' are not benefiting from clustering as expected. What is the most likely cause?

You need to split a time-series dataset into training and evaluation sets for a forecasting model. The data is ordered by timestamp. Which splitting technique should you use?

A company is designing a CI/CD pipeline for their ML models using Cloud Build and Vertex AI. Which TWO practices should they adopt to ensure reliable and reproducible deployments?

Which BigQuery feature allows you to estimate the cost of a query before running it, by returning the number of bytes that would be processed?

A team runs a Dataflow streaming pipeline that reads from Pub/Sub, windows events by processing time, and writes to BigQuery. Some late-arriving events are being dropped. The requirement is to include all events that arrive within 10 minutes of the watermark. Which pipeline configuration should be used?

An e-commerce company uses Vertex AI to serve a real-time personalization model. The model is updated daily via a retraining pipeline that uploads a new version to the same endpoint. Recently, after a model update, the online prediction responses have been returning anomalous results (e.g., recommending irrelevant products). The previous version performed well. The team suspects that the new model is undercooked or has a bug. They have already checked the training code and the pipeline logs, which show no errors. The pipeline deploys the new model version to the endpoint by updating the traffic split to route 100% of traffic to the new version. Which course of action should the team take to quickly mitigate the issue while diagnosing the root cause? A) Roll back the endpoint to the previous model version by setting traffic split to 0% for the new version. B) Delete the current endpoint and recreate it with the previous model version. C) Tweak the training hyperparameters and retrain immediately. D) Increase the number of replicas on the endpoint to handle load.

A company runs a batch processing job on Dataproc that uses Apache Spark to process 500 GB of data daily. The job completes successfully but takes 4 hours. The team wants to reduce the runtime to under 2 hours without increasing cost. What should they do?

A company uses Cloud Storage to store IoT sensor data in JSON format. The data is ingested using a Cloud Function triggered by Cloud Storage events. They notice that when many files are uploaded simultaneously, some files are not processed and the Cloud Function logs show 'function execution timeout'. What is the most likely cause and solution?

A healthcare company must encrypt data in BigQuery with customer-managed keys (CMEK). They want to control the key lifecycle independently. Which approach should they take?

A healthcare company stores patient records as JSON files in Cloud Storage for analysis. They want to design a data lake that enables querying the data with BigQuery while minimizing storage costs and maintaining data security. Which two actions should they take? (Choose two.)

Your team runs a weekly batch ETL pipeline using Cloud Dataproc. The pipeline reads raw data from Cloud Storage, transforms it with Apache Spark, and writes results to BigQuery. Recently, the pipeline has been failing with the error 'Out of Memory' during the shuffle phase. The cluster uses standard worker nodes (n1-standard-4). What is the most effective way to resolve this without increasing total cost?

You are implementing a data pipeline that reads from Cloud Storage (parquet files), transforms data with Cloud Dataflow, and writes to BigQuery. The pipeline runs on a batch schedule every hour. You notice that the Dataflow job takes 10 minutes, but the overall pipeline latency is 15 minutes due to file availability and scheduling. The business requires latency under 5 minutes. Which change should you make?

During a Vertex AI training pipeline, the training job fails with an error: 'Out of memory: Killed process'. The model is a large deep learning model using TensorFlow. Which THREE steps should the team take to resolve this issue?

A team deploys a new version of a Cloud Function. After deployment, error rates increase significantly. What is the most efficient way to diagnose the cause?

A company processes real-time clickstream data from websites. They need to aggregate user sessions that may span multiple hours and handle events that arrive late due to network delays. The pipeline must avoid discarding late data. Which Dataflow feature should they configure?

A data engineer needs to implement data quality rules and governance policies across multiple data lakes in GCP. They want to automatically discover and catalog data assets, and enforce row-level security. Which two services should they use? (Select TWO)

A company uses Cloud Dataproc for ephemeral clusters to run batch jobs. They want to ensure job reliability and data quality. Which two configuration options should they use? (Choose two.)

A company wants to build a real-time dashboard for monitoring application logs. The logs are ingested via Pub/Sub and must be processed with low latency (sub-second). You need to enrich the logs with user metadata from Cloud SQL and store the results in BigQuery for analysis. Which TWO services should be used for the stream processing? (Choose two.)

A data engineer is building a batch pipeline that runs daily using Cloud Composer. The pipeline has three tasks: extract data from Cloud Storage, transform data using Dataflow, and load the transformed data into BigQuery. The engineer wants to ensure that the Dataflow job only starts after the extraction task completes successfully, and the load task only starts after the Dataflow job finishes. How should the engineer define the task dependencies in the Airflow DAG?

A startup needs a fully managed, serverless Spark service to run occasional data processing jobs without managing clusters. They want to pay only for the resources used during job execution. Which Google Cloud service should they use?

A logistics company uses Cloud Functions to process incoming tracking events from IoT devices. Events are sent via HTTP triggers. During peak hours, some events fail with 500 errors. What is the best strategy to handle this reliably?

A Dataflow pipeline as described in the exhibit has increasing lag. Which optimization is most likely to reduce the lag?

You have two versions of a classification model (v1 and v2) deployed on a Vertex AI Endpoint. You want to gradually roll out v2 to 10% of traffic, monitor performance, and if metrics are better, increase traffic to 100%. You have set up model monitoring for skew and drift. Which configuration should you use?

A data scientist wants to train a custom TensorFlow model on Vertex AI using a managed Jupyter notebook. Which Vertex AI service should they use to set up a notebook environment with pre-installed deep learning frameworks?

Which BigQuery SQL function can be used to get an approximate count of distinct values in a large column faster than COUNT(DISTINCT) with lower accuracy?

You have a Dataflow pipeline that processes streaming data with high throughput. You notice that the pipeline is experiencing high latency and the workers are underutilized. Which Dataflow feature can automatically optimize resource allocation?

A media company uses Cloud Dataflow to process video metadata from a Pub/Sub stream. The pipeline enriches metadata using a lookup table stored in Cloud Bigtable. Recently, they noticed increased latency and occasional 'Bigtable operation timeout' errors. The Bigtable instance has 3 nodes and the data is highly distributed. The Dataflow pipeline uses default settings. What is the most likely cause of the timeouts?

A company wants to trigger a Cloud Run service whenever a new file is uploaded to a specific Cloud Storage bucket. Which event-driven solution should they use?

You want to create a cost-efficient snapshot of a large BigQuery table that can be used by other teams for read-only analytics without incurring additional storage costs for the base table data. What should you use?

A company uses BigQuery's Storage Write API in committed mode to stream data. They notice that some writes are failing with 'DEADLINE_EXCEEDED' errors during peak traffic. The pipeline is a Dataflow job using the Beam SDK. What is the MOST likely cause and solution?

A company deploys a machine learning model on Vertex AI for online predictions. The model experiences intermittent spikes in traffic, causing latency increases. Which strategy should the company use to ensure consistent low latency during traffic spikes?

You want to train a custom TensorFlow model on Vertex AI using a managed Jupyter notebook environment. Which service should you use?

You need to analyze customer churn and want to understand the rank of each customer's churn probability within their subscription plan. Which BigQuery window function computes the relative ranking from 1 (highest probability) to N?

A company is migrating their on-premises Apache Spark jobs to Dataproc. They want to minimize code changes and take advantage of serverless infrastructure. Which Dataproc feature should they use?

A data engineer wants to quickly estimate the cost of running a BigQuery query before executing it. Which command-line tool or command should they use?

Which TWO best practices should be followed when managing multiple model versions on Vertex AI Endpoints for a production system?

A data engineer needs to design a schema in BigQuery for a dataset that contains customer orders. Each order has a header and multiple line items. Queries frequently need to retrieve the entire order including line items. Which schema design is MOST performant and cost-effective?

A financial services company uses a Dataflow streaming pipeline to process real-time stock trades. The pipeline reads from Pub/Sub, enriches with reference data from Cloud Bigtable, and writes to BigQuery. Recently, they noticed an increase in processing latency during market open hours. Investigation shows that the pipeline is data-skewed: a few stock symbols generate 90% of the traffic. The team wants to reduce latency without changing the pipeline structure. What should they do?

An MLOps team wants to implement continuous deployment of ML models using Cloud Build and Vertex AI. They have a GitHub repository with training code. What should they use?

A data engineer is designing a BigQuery table for a clickstream dataset with frequent queries aggregating over user sessions. Each user session has multiple events, and the engineer wants to avoid joins for performance. Which schema design pattern should they use?

A Dataproc cluster uses preemptible worker nodes to reduce costs. The cluster runs a long-running Spark job that occasionally experiences worker failures. How should the job be configured to handle preemptible worker failures gracefully?

Your company is building a real-time fraud detection system using Google Cloud. Transactions are streamed into Pub/Sub, and you need to process them with low latency (under 100ms per event) and aggregate data over sliding windows. Which Google Cloud service is best suited for this processing logic?

Your team manages a multi-model ensemble deployed on Vertex AI Endpoint. The ensemble consists of three models: a neural network (NN), a gradient boosted tree (GBT), and a logistic regression (LR). They are deployed as separate endpoints and traffic is split using a traffic split configuration. Recently, the overall accuracy dropped from 92% to 85%. Monitoring shows that the NN model's latency has increased significantly, causing it to miss timeouts and fall back to default predictions. The other two models are performing normally. The NN model is the most complex and handles the majority of the traffic. You need to restore accuracy quickly. What should you do first?

You are designing a Dataflow pipeline for processing real-time clickstream data. The pipeline must group events into 30-second windows and handle late data up to 5 minutes. You want to output partial results every 10 seconds for low-latency monitoring. Which THREE configurations should you use? (Choose three.)

You want to monitor the latency of messages in a Pub/Sub subscription. Which Cloud Monitoring metric should you use to see the age of the oldest unacknowledged message?

What is the most likely cause of this error?

You are building a data pipeline that runs daily batch jobs on Dataproc, then loads results into BigQuery. You want to orchestrate the entire workflow, including dependencies between steps, retries, and monitoring. Which Google Cloud service is most appropriate?

A data engineer is building a feature store for ML models using Vertex AI Feature Store. The features are computed daily from BigQuery and need to be available for both online predictions (low latency) and offline training. Which two actions must the engineer take? (Choose TWO)

A company is migrating an on-premises Hadoop cluster to Google Cloud. They need to run existing Spark jobs with minimal modification. Which THREE strategies should they consider? (Choose THREE.)

A company building a real-time analytics pipeline with Pub/Sub and Dataflow. Which THREE best practices should they follow?

A startup is building a real-time dashboard that shows aggregated metrics from social media feeds. They expect up to 10,000 events per second. The data must be near-real-time (< 30 seconds latency) and stored in BigQuery for historical analysis. They have limited experience managing infrastructure. The CTO suggests using Apache Kafka on Compute Engine for ingestion. However, the data engineer recommends a fully managed solution. Which approach should the team adopt?

Given the query plan, what is the most likely reason this query is efficient despite processing 10 billion rows?

You are using Looker to model data from BigQuery. You have a dimension that should be filtered by a user attribute (e.g., user's region). Which LookML concept allows you to apply dynamic row-level security based on user attributes?

Your Dataflow streaming pipeline is reading from Cloud Pub/Sub and writing to BigQuery. Users report occasional data duplication in the BigQuery table. You verify the pipeline uses exactly-once processing and idempotent writes. The Dataflow monitoring shows no errors, but the pipeline has occasional worker restarts. What is the most likely cause of the duplicates?

You need to create a Looker model that defines a 'sales' view based on a BigQuery table, with a measure for total revenue. Which LookML object defines the table and dimensions?

A data engineer is building a production ML pipeline on Vertex AI. The pipeline must preprocess features (e.g., scaling, encoding) and then train a model. The preprocessing logic must be reusable for serving predictions. Which Vertex AI component should they use?

A company wants to use Eventarc to trigger a Cloud Run service when new objects are created in a GCS bucket. They also need to filter events for a specific bucket and object prefix. Which THREE resources must exist or be created?

A company needs a messaging service for event-driven applications that require low cost for high-throughput, but can tolerate occasional message loss. Which Pub/Sub product should they choose?

Your organization has a BigQuery flat-rate reservation with 2000 slots. During peak hours, query performance degrades because concurrent queries exceed the available slots. You want to handle these bursts without changing the base reservation. What should you do?

An MLOps team manages a pipeline that retrains an XGBoost classifier weekly using BigQuery data. The pipeline is orchestrated with Cloud Composer and deploys the new model to Vertex AI Endpoint if validation metrics (AUC > 0.9) are met. Over the past month, the deployed model's AUC has dropped from 0.95 to 0.88, despite the training pipeline consistently reporting AUC > 0.9. Which THREE steps should the team take to diagnose and fix this issue?

You are designing a streaming Dataflow pipeline that processes high-throughput data. Which two features can help minimize cost? (Choose TWO.)

A company uses BigQuery partitioned tables with daily partitions for log data. They want to automatically delete partitions older than 90 days and ensure that current month data is in a specific dataset with a set expiration. Which TWO actions should they take? (Choose 2)

An application needs to store user profile data in a document database with flexible schema. The data is accessed frequently from a mobile app. Which Google Cloud database is BEST suited?

A data analyst frequently queries a BigQuery table that contains an array of structs representing product purchases. The query below runs slowly: SELECT customer_id, COUNT(purchase) as total_purchases FROM sales, UNNEST(purchases) as purchase GROUP BY customer_id What change would most improve query performance?

You are deploying a machine learning model to production using Vertex AI. The model requires GPU acceleration for low-latency predictions. You need to minimize costs while ensuring availability during a defined business hours window (8 AM to 6 PM). Which deployment strategy should you use?

A data team uses Cloud Dataproc to run nightly Spark jobs. The job volume has increased, and the cluster is often underutilized during the day. They want to reduce costs while ensuring jobs can scale when needed. Which strategy should they adopt?

A company trains a model using Cloud TPUs. The model is deployed to AI Platform Prediction using a custom container with TensorFlow. Which THREE considerations are most important when serving this model?

Which Google Cloud service is designed to replicate data from MySQL, PostgreSQL, and Oracle databases to BigQuery or Cloud Storage in near real-time?

A mobile app uses Firestore to store user profiles. The app allows offline data creation and syncing when connectivity resumes. Which Firestore feature should the developer enable?

You need to implement data quality rules on a Dataplex lake to ensure that critical columns are not null and meet certain constraints. Which two Dataplex features can you use? (Choose TWO)

A company wants to use Dataproc Metastore to manage metadata for their Spark jobs. Which TWO benefits does Dataproc Metastore provide?

You need to track the lineage of data in BigQuery, showing how tables are derived from other tables via queries. Which service provides this capability?

A multinational corporation needs a globally distributed database that supports strong consistency, SQL queries, and automatic failover across regions. They also want to optimize join performance for parent-child relationships. Which TWO features of Cloud Spanner should they use?

A data warehouse in BigQuery is experiencing performance issues. Which THREE techniques can improve performance without moving data to a different storage system?

Which Dataflow feature allows you to package a pipeline into a reusable template that can be deployed with different parameters at runtime?

A data engineer needs to design a streaming pipeline that ingests events from multiple sources, enriches them with a lookup table stored in BigQuery (updated every hour), and writes the results to a BigQuery table for real-time dashboards. The pipeline must handle late-arriving data up to 1 hour. Which Dataflow feature should be configured to manage late data?

A machine learning pipeline uses Vertex AI Pipelines. One component fails intermittently due to resource constraints. What is the best way to handle this?

You are configuring Dataplex data quality rules for a BigQuery table. Which three types of rules can be defined using Dataplex's SQL-based rule engine? (Choose three.)

A company wants to orchestrate a multi-step data processing workflow that includes calling a Cloud Run service, waiting for its completion, and then running a BigQuery query. The workflow should be serverless and integrate with Cloud Events. Which Google Cloud service should they use?

An engineer needs to create a Pub/Sub subscription that sends messages to an HTTPS endpoint. The endpoint must be able to acknowledge messages individually. Which type of subscription should they use?

A retail company uses a machine learning model to predict inventory demand. The model is retrained weekly using Vertex AI Pipelines. Recently, the model's accuracy has degraded because the data distribution has shifted. Which action should you take to monitor and detect this drift automatically?

Which THREE metrics should be monitored for a deployed machine learning model in production?

A team developed a microservice that writes logs to stdout. They want to centralize logs for analysis. Which GCP service should they use to automatically collect and store logs?

A data science team has deployed a custom TensorFlow model on Vertex AI Prediction. They notice increasing prediction latency and a growing number of 503 errors during peak traffic hours. The model is served using a single regional endpoint with min replica count of 2 and max replica count of 10. Which TWO actions should the team take to address these issues?

A data engineering team needs to ingest streaming data from an existing Kafka cluster (on-premises) into Google Cloud for real-time analytics. They want to minimize changes to the existing Kafka setup and avoid long-term operational overhead. Which TWO approaches should they consider?

A data engineer needs to implement data lineage tracking for a BigQuery data warehouse. They want to automatically capture column-level lineage from ETL jobs run by Dataform and from manual SQL queries executed in the BigQuery console. Which approach meets these requirements?

A company uses BigQuery Omni to query data stored in AWS S3. They need to join this data with data in BigQuery (GCP). The dataset in AWS is large (10 TB) and frequently updated. Which approach minimizes data movement and cost?

A company is building a data pipeline that ingests streaming data from Pub/Sub, transforms it with Dataflow, and loads it into BigQuery. They want to handle malformed messages that cannot be parsed. Which TWO actions should they implement for error handling? (Choose 2)

A company serves multiple models using Vertex AI endpoints. Each model has different latency and memory requirements. To minimize cost, the company wants to share underlying compute resources among models. Which approach should they use?

A retail company wants to trigger a Cloud Run service whenever a new CSV file is uploaded to a specific Cloud Storage bucket. Which THREE components are needed to set up this event-driven architecture? (Choose 3)

You need to inspect a BigQuery table for sensitive data such as credit card numbers and apply masking. Which GCP service should you use to identify and de-identify the data?

A media company uses Cloud Data Loss Prevention (DLP) API to inspect and de-identify sensitive data before loading into BigQuery. They want to reduce costs by sampling the data during inspection. Which configuration should they use?

A company has a batch ETL job that runs daily using Cloud Dataflow. The job reads from Cloud Storage, transforms data, and writes to BigQuery. Recently, the job started failing with 'Resources have been exhausted' errors. What is the most likely cause?

Your team uses Vertex AI Feature Store to serve features for online predictions. A feature value changes frequently (e.g., user session clicks). Which type of feature should you use to ensure low-latency writes and reads?

A data pipeline reads thousands of JSON files from Cloud Storage, processes them with Cloud Dataflow, and writes to BigQuery. The pipeline sometimes fails because of malformed JSON records. Which three steps should the data engineering team take to improve pipeline reliability? (Choose THREE.)

A team needs to migrate an existing on-premises Hadoop Hive workload to Google Cloud. They want to minimize code changes and use a managed service for transient clusters. Which service should they choose?

A data engineer is monitoring a Dataflow streaming pipeline and notices that the 'System Lag' metric is increasing. Which TWO actions should be taken to diagnose the issue?

A Dataflow streaming job is processing high-volume sensor data from thousands of IoT devices. The job uses global windows with a 10-minute processing time trigger. Recently, the job's CPU utilization is nearly 100% and it is falling behind. Which action is most likely to reduce CPU load while maintaining data freshness?

You deployed a model on Vertex AI Endpoints using a custom container. The model serves predictions but the latency is higher than expected. You suspect the container is not making full use of the CPU resources. What should you do to reduce latency?

A company uses Cloud Dataproc to run Spark ML training jobs. They want to persist the trained models and metadata in a Hive-compatible metastore. Which Dataproc feature should they use?

A company is using Cloud Storage to store raw logs. They want to use Cloud Data Fusion to transform and load the data into BigQuery on a daily schedule. The transformations are complex and involve joining multiple datasets. What is the most efficient way to run these pipelines?

You are a data engineer at a financial services company. You have deployed a credit risk model on Vertex AI Endpoints using a custom container with a TensorFlow SavedModel. The model expects input features as a JSON object. Recently, the model has been returning high prediction latency and occasional 503 errors. You have enabled autoscaling with minNodes=2 and maxNodes=10. The model is CPU-only and uses n1-standard-4 machines. Monitoring shows that during peak hours, CPU utilization reaches 90% and memory is at 80%. The number of prediction requests per second peaks at 100. You suspect that the model is not scaling fast enough. Which action will most effectively reduce latency and eliminate 503 errors?

A payment processing company needs to detect fraudulent transactions in real time. The system must have sub-second latency for high-value transactions and use a machine learning model. Which two components should be part of the architecture? (Choose TWO.)

A startup wants to build a data lake on Google Cloud using Cloud Storage. They need to store raw data in its original format for future analysis. Which storage class should they use to optimize for cost given that data will be accessed occasionally after the first month?

A data engineer is designing a batch data pipeline that reads Avro files from Cloud Storage, transforms data using Apache Beam, and writes to BigQuery. The pipeline must handle daily runs and backfills. Which runner should they use?

Which TWO are best practices for managing a Cloud Dataflow pipeline in production?

A financial services company uses Cloud Pub/Sub with ordering keys to process transactions in order. Some messages are failing processing and getting stuck. The team wants to ensure that if a message fails, it can be reprocessed later without blocking subsequent messages. What should they implement?

A data engineer needs to process data in a Dataflow pipeline that reads from a Pub/Sub topic. The pipeline must group events into 5-minute windows and compute the average value per key. Which Beam transform should they use after windowing?

An organization uses Cloud Dataproc to run Spark jobs that process sensitive data. They need to ensure data is encrypted at rest and that only specific service accounts can access the data on cluster disks. What should they do?

A company is deploying a machine learning model for fraud detection. The model is trained using TensorFlow and will be served on Vertex AI Prediction. The team wants to implement model monitoring to detect prediction drift. Which TWO actions should they take? (Choose 2)

A company uses Dataflow streaming pipelines to process real-time events. They notice increasing system lag over time. Which two Cloud Monitoring metrics should be examined to diagnose the cause?

A global fintech company needs a database that can serve transactional (OLTP) and analytical (OLAP) workloads with strong consistency. They require high availability and PostgreSQL compatibility. Which TWO Google Cloud databases meet these requirements? (Choose 2 correct options)

Which Google Cloud service provides a visual interface for building ETL pipelines using a drag-and-drop design and includes pre-built transforms from a marketplace?

A retail company processes real-time clickstream data using Cloud Pub/Sub and Dataflow. The pipeline aggregates events by user session and writes to Bigtable for low-latency queries. However, users report that session data is sometimes missing or duplicated. What is the most likely cause?

A data engineer wants to store archived log files in Cloud Storage with a retention policy that prevents deletion for 5 years. Which feature should they use?

Which TWO are benefits of using Vertex AI Endpoints for model serving?

A team is deploying a complex model with multiple preprocessing steps. They want to ensure consistent preprocessing during training and serving. Which three approaches can achieve this? (Select 3)

You need to ingest streaming data from a custom application into BigQuery with exactly-once semantics and low latency. The data volume is up to 10 MB/s. Which TWO services should you combine?

A data engineer is designing a BigQuery table for time-series data that will be queried frequently by time range and also by a customer_id. Which TWO design decisions will improve query performance and manage costs? (Choose two.)

You are designing a Dataflow pipeline that reads from Pub/Sub, aggregates events into 10-minute windows, and writes the results to BigQuery. The pipeline must reliably handle late-arriving data (up to 1 hour) and prevent duplicate aggregations. Which combination of pipeline options should you use?

A data engineer needs to load 10 GB of CSV files from Amazon S3 into BigQuery on a daily basis. The files arrive in a specific S3 bucket at 3 AM UTC each day. Which service should be used to automate this transfer?

You are building a time-series forecasting model with BigQuery ML. Which three steps should you perform to properly split the data and evaluate the model? (Choose THREE)

You need to preprocess tabular data for training a classification model using Vertex AI. The dataset has missing values in numerical columns and categorical columns with high cardinality. Which Vertex AI service provides automated feature engineering and preprocessing as part of the pipeline?

A company has a batch prediction job that runs daily using AI Platform Batch Prediction. The job uses a TensorFlow model and processes 10 GB of data. Recently, the job started failing with the error 'The replica worker 0 exited with a non-zero exit code: Out of memory'. Which action should the team take to resolve this without rewriting the model?

A company needs to process large files (100GB each) from Cloud Storage using Dataproc. They want to minimize job execution time. Which configuration is most appropriate?

A team is designing a data lake on Google Cloud using Cloud Storage and BigQuery. They need to ensure that sensitive data (e.g., PII) is encrypted at rest and have the ability to audit access. Which approach meets these requirements?

You are building a streaming pipeline to ingest real-time clickstream data from a website into BigQuery for immediate analysis. The data must be available in BigQuery within seconds and you need to handle late-arriving data (e.g., browser offline events) that may arrive hours later. Which approach should you use?

You are migrating a large on-premises data warehouse to BigQuery. The data includes sensitive PII columns that must be masked for certain users. Which BigQuery feature can automatically redact PII in query results based on user roles?

You need to process large-scale log files (hundreds of terabytes) using Apache Spark on Google Cloud. The job runs nightly and you want to minimise costs. Which Dataproc cluster configuration is MOST cost-effective?

You configured a model deployment monitor on your Vertex AI endpoint as shown. What will happen when the feature 'age' has a skew of 0.4?

Drag and drop the steps to migrate an on-premises MySQL database to Cloud SQL using Database Migration Service into the correct order.

A company uses Cloud Composer to orchestrate Dataproc and BigQuery jobs. They need to implement retry logic for transient failures. Which THREE features can help?

An e-commerce company runs a daily batch pipeline that processes clickstream data from Cloud Storage using Cloud Dataproc with Spark. The pipeline includes a join between a large fact table and a small dimension table. The dimension table is stored in Cloud Storage as a CSV file. The join is slow due to shuffling. The data engineer considers broadcasting the dimension table. However, the dimension table is updated daily and the pipeline reads the latest version. What is the best approach to implement this optimization?

A data engineer tries to grant a service account read access to a Cloud Storage bucket using the IAM policy above. The service account still cannot read objects. What is the most likely reason?

A company uses Cloud Monitoring to track application latency. They notice a spike in latency every 30 minutes. What is the best initial step to diagnose the issue?

A data engineer needs to store raw sensor data in Cloud Storage and automatically transition it to a lower-cost storage class after 30 days, then delete it after 365 days. What should they configure?

Which TWO statements are correct about designing a data pipeline using Cloud Dataflow for processing unbounded data?

A Cloud Build pipeline is set up to train a model on Vertex AI. The build fails with the error: 'ERROR: (gcloud.ai-platform.jobs.submit.training) NOT_FOUND: The parent project does not exist.' The project ID and the service account are correctly configured. What is the most likely cause?

You need to allow a data analyst to run queries on a BigQuery dataset but prevent them from modifying the data or deleting the dataset. Which IAM role should you grant?

A company has deployed a machine learning model to AI Platform Prediction. The model uses a custom container with a TensorFlow SavedModel. After deployment, the prediction latency is higher than expected. Which action is most likely to reduce latency without significantly impacting model accuracy?

A company needs to store petabytes of time-series IoT sensor data and query it with single-digit millisecond latency at millions of reads per second. The data has a simple key-value structure with timestamps. Which Google Cloud database is MOST appropriate?

Which THREE Google Cloud services are typically used together in a production ML pipeline?

A data engineer needs to ingest daily Salesforce reports into BigQuery without writing custom code. The reports are exported to an Amazon S3 bucket on a schedule. Which service should they use to automate the transfer?

A Dataflow pipeline reads log files from Cloud Storage, parses them into LogEvent objects, and writes to BigQuery. The pipeline fails with the above errors. What is the most likely cause?

You are designing a Dataflow pipeline that reads from Pub/Sub and writes to BigQuery. The pipeline must handle late-arriving data (up to 1 hour) and group events into 10-minute windows. Which configuration is correct?

A data team runs regular analytical queries on a BigQuery table that stores 2 years of sales data (approximately 10 TB). Queries frequently filter on a `sale_date` column and also group by `product_id`. To optimize cost and performance, which design approach is most effective?

Drag and drop the steps to create a Cloud Storage bucket with uniform bucket-level access into the correct order.

A company wants to use Cloud Data Fusion for ETL pipelines. They need to integrate with custom transformations not available in the marketplace. What should they do?

You need to ingest Google Ads performance data into BigQuery on a daily basis for reporting. Which service should you use?

A company is designing a data lake on Cloud Storage for analytics. They need to store data in various formats (Avro, Parquet, CSV) and enable efficient querying with BigQuery and Dataproc. Which THREE practices should they follow?

A company uses Cloud Functions to process events from Cloud Storage. They notice that occasionally functions are not triggered. What should they check first to ensure solution quality?

An organization wants to automate their batch data processing pipeline using Cloud Composer. The pipeline consists of multiple tasks: extract from Cloud Storage, transform with Dataflow, and load into BigQuery. Which Airflow operator should be used to run Dataflow jobs?

A data engineer needs to process streaming data from thousands of IoT devices and generate real-time dashboards. The data volume is low but requires exactly-once processing semantics. Which Google Cloud service combination should they use?

A data scientist wants to use Vertex AI Workbench for exploratory data analysis. Which TWO statements are true about Vertex AI Workbench?

A company runs a data pipeline that ingests clickstream events from multiple websites into Cloud Pub/Sub, then processed by Dataflow to generate user sessions, and written to BigQuery for analytics. The pipeline runs 24/7. Recently, the team noticed that some sessions are incomplete due to missing events, and data quality checks reveal that about 2% of sessions have gaps of more than 30 minutes. The pipeline uses fixed 30-minute windows for sessionization, with allowed lateness set to 10 minutes. They have Cloud Monitoring dashboards tracking system throughput and pipeline lag but do not have custom metrics tracking per-element delays or watermark progress. The team suspects two possible causes: (a) the Pub/Sub subscription accumulates backlog and some messages are delivered after the window end; (b) the Dataflow job has insufficient workers causing checkpoint failures. The team needs to determine the root cause and improve data quality. What is the best first course of action?

A BigQuery table contains streaming data from Cloud Pub/Sub. The table is partitioned by ingestion time. A user runs a query that accesses data from the last 5 minutes and gets correct results. After 90 minutes, the user runs the same query again but notices that some rows are missing. What is the most likely cause?

Drag and drop the steps to create a Cloud Composer environment for Apache Airflow into the correct order.

A team is using BigQuery to analyze petabyte-scale data. They notice that queries are slow and expensive due to full table scans. They have already partitioned by date. What additional optimization should they implement?

Your Vertex AI model deployed on an endpoint is experiencing high tail latency during online predictions. The model uses a large embedding layer, and the input size varies. You have enabled automatic scaling with a minimum of 2 replicas and maximum of 10. What is the most likely cause of the latency spikes and the best first step to diagnose?

A data engineer notices that BigQuery queries are slower than expected. They want to identify the most expensive stages in the query execution. Which tool or command should they use?

Which TWO approaches are recommended for handling late-arriving data in a streaming Dataflow pipeline?

A data pipeline ingests sensor data from IoT devices via Cloud Pub/Sub, processes it with Cloud Dataflow, and writes to BigQuery. The pipeline is failing with high latency and data loss. Which troubleshooting step should be taken first?

A company uses BigQuery to run reporting queries on a table that is partitioned by date and clustered by customer_id. Queries filtering by customer_id and a date range are performing poorly. What is the most likely cause?

A company is migrating their on-premises data warehouse to BigQuery. They have a mix of batch and streaming ingestion. The data team wants to optimize query costs. Which THREE practices should they adopt?

Which THREE components are typically part of a Vertex AI Pipeline for automated model retraining and deployment?

You need to react to changes in a GCS bucket (e.g., new object creation) and trigger a Cloud Run service to process the new file. Which Google Cloud service should you use to route the event?

Match each machine learning term to its description.

Which THREE best practices should be followed when designing a Dataflow pipeline for real-time data processing?

A company wants to train a machine learning model to predict customer churn using BigQuery ML. The dataset has a severe class imbalance (only 2% churn). Which approach should the data engineer take to handle this imbalance within BigQuery ML?

A data analyst wants to compute the rank of sales per region and also the difference in sales between consecutive months for each region. Which BigQuery analytic functions should they use? (Select TWO)

You want to optimize BigQuery costs for a large dataset that is frequently queried by time range. You also need to ensure that predictable workloads have dedicated slot capacity. Which TWO strategies should you combine? (Choose 2)

You are migrating an existing Kafka cluster to Google Cloud using Dataproc. The cluster handles high-throughput streaming data with strict ordering requirements per partition. Which choice of Dataproc configuration is most appropriate?

A company wants to use BigQuery ML to build a recommendation system for movies. The data includes user IDs, movie IDs, and ratings. Which BigQuery ML model types are suitable for this? (Select TWO)

Your company runs a Dataflow streaming pipeline that processes user activity from Pub/Sub and writes aggregated results to BigQuery. Lately, the pipeline is experiencing high latency and backlog growth during peak hours. You need to troubleshoot and improve performance. Which THREE actions should you take? (Choose 3.)

A streaming Dataflow job is processing messages from Cloud Pub/Sub. The job is underutilizing resources and the throughput is lower than expected. Which parameter should be adjusted to increase parallelism?

A healthcare startup is deploying a natural language processing (NLP) model for extracting medical entities from clinical notes. The model is a fine-tuned BERT model served on Vertex AI Prediction using a custom container. The team observes that prediction latency is around 500ms per request, but they need to handle up to 100 requests per second (QPS) with end-to-end latency under 200ms. The model currently runs on n1-standard-4 machines (4 vCPU, 15 GB memory). During load testing, CPU utilization reaches 90% and memory usage is 12 GB. The team is considering options to meet the requirements. Which action should they take?

A company wants to use dbt to transform data in BigQuery. Their source data is loaded daily into staging tables. They need to run dbt transformations on a schedule and only process tables that have changed. Which dbt feature should they use?

Your company uses Cloud Composer to orchestrate a data pipeline that includes Dataproc Spark jobs and BigQuery load operations. You need to pass the output file path from the Spark job to the next BigQuery task in the DAG. Which two mechanisms can you use to share data between tasks? (Choose TWO.)

A data engineering team needs to process a large volume of CSV files stored in Cloud Storage using Dataproc. The files are generated hourly and each contains millions of rows. They want to minimize the number of Dataproc cluster nodes to reduce cost while processing within an hour. Which configuration should they recommend?

A company uses Cloud Composer to orchestrate a daily ETL pipeline that includes multiple Dataproc jobs. The pipeline processes sensitive financial data. The security team requires that all data in transit be encrypted, and all Cloud Storage buckets used by the pipeline should have uniform bucket-level access enabled and VPC Service Controls. The pipeline currently uses a single Cloud Composer environment in us-east1. The Dataproc clusters are created using the standard image and use custom service accounts with minimal permissions. The pipeline runs successfully during testing, but in production, the Dataproc jobs fail with 'Access Denied' errors when trying to write to a Cloud Storage bucket. The bucket has uniform bucket-level access enabled and is inside a VPC Service Controls perimeter. The Dataproc service account has the Storage Object Admin role at the project level. What is the most likely cause of the access denied error?

Which TWO actions should be taken to optimize a Dataflow streaming pipeline that is experiencing high system lag and backpressure? (Choose two.)

A company wants to stream real-time clickstream data from a website into BigQuery for near-real-time analytics. They expect peaks of 10,000 events per second. Which combination of services is most suitable for ingestion?

Which Google Cloud service is a serverless, highly scalable data warehouse for analytical queries, supporting SQL and integration with BI tools?

A data engineer needs to alert when Pub/Sub subscription has messages older than 1 hour. Which Cloud Monitoring metric and filter should they use?

Drag and drop the steps to set up a Pub/Sub topic with a push subscription to an HTTPS endpoint into the correct order.

You are designing a streaming pipeline that must guarantee exactly-once processing. Which three services or features can help achieve this? (Choose THREE.)

Drag and drop the steps to set up Cloud IAP (Identity-Aware Proxy) for an App Engine app into the correct order.

A company processes CSV files that are uploaded to Cloud Storage by external partners. Each file is around 500 MB, and they need to be parsed and loaded into BigQuery. The processing must start as soon as the file arrives. What is the most efficient serverless architecture?

A company needs to process streaming sensor data and run both real-time analytics and batch reanalysis on historical data. They want to minimize infrastructure management. Which architecture and service combination is MOST suitable?

A company needs to store and analyze large amounts of unstructured data (images, videos) and structured data (CSV logs) in a cost-effective manner. The data should be accessible for analytics with BigQuery. Which two services should they use? (Choose TWO.)

An IoT application writes sensor readings to Cloud Bigtable with a row key of 'deviceID#timestamp'. The team notices high write latency and hotspots on a few nodes. Which row key design change would most likely improve performance?

You are monitoring a Dataproc cluster and notice that the cluster utilisation is high, but jobs are running slowly. The cluster uses preemptible workers for cost savings. What is the most likely cause of the performance degradation?

A company runs Apache Kafka on Dataproc for real-time event streaming. They want to archive the Kafka topics to Cloud Storage for long-term retention and later analysis in BigQuery. Which approach is the most cost-effective and operationally simple?

A gaming company uses Avro schemas for its streaming event data. They anticipate adding new optional fields to events over time. They need to ensure backward compatibility so that existing pipelines continue to work. Which strategy should they adopt?

Which THREE steps are essential for implementing a continuous training pipeline with Vertex AI?

A data pipeline ingests real-time events from Cloud Pub/Sub into BigQuery using Dataflow. The pipeline uses a sliding window of 5 minutes with a 1-minute period to aggregate event counts. Recently, the pipeline started failing with 'The worker failed to provide a heartbeat.' The Dataflow logs show high CPU usage on the workers. What is the best course of action to resolve the issue?

A team notices that the latency for online predictions from a Vertex AI endpoint has increased significantly over the past hour. The model is a large TensorFlow model deployed with automatic scaling (minReplicaCount=2, maxReplicaCount=10). The CPU utilization of the deployed instances is consistently above 85%. What is the most likely cause of the increased latency?

A company wants to implement a data lake on Google Cloud to store raw sensor data (unstructured binary files) and allow data scientists to run SQL queries on processed data. They expect to store terabytes of data and have different access patterns. Which combination of GCP services best meets these requirements?

A data engineer wants to automatically move objects from Standard storage class to Nearline after 30 days, and then to Archive after 365 days. Which Cloud Storage feature should they configure?

A company uses Cloud Composer to orchestrate data pipelines. One DAG fails intermittently with the error: 'Task received SIGTERM signal.' The task runs a long-running Dataproc job. What is the most likely cause?

Your Dataflow pipeline reads from Pub/Sub, performs transformations, and writes to BigQuery. You notice that the pipeline's autoscaling is not keeping up with sudden spikes in traffic, causing increased lag. The pipeline uses Classic Templates. Which change would most effectively improve autoscaling responsiveness?

You are designing a disaster recovery strategy for a critical streaming data processing pipeline. The pipeline reads from Cloud Pub/Sub, processes with Dataflow streaming, and writes to BigQuery. The required RPO is less than 1 minute, and RTO is less than 5 minutes. Which architecture should you implement?

A company runs a critical batch pipeline using Cloud Dataflow. The pipeline processes financial transactions and runs every hour. Recently, some runs have failed due to transient errors (e.g., network timeouts). The engineer wants to automatically retry failed runs without manual intervention. The pipeline is launched from a Cloud Composer DAG using DataflowPythonOperator. What is the BEST way to handle retries?

Which TWO actions are recommended to improve the reliability of a Cloud Dataflow streaming pipeline that processes event data from Pub/Sub?

You are designing a data pipeline that must process sensitive customer data with strict access controls. The data is ingested via Cloud Pub/Sub, processed by Cloud Dataflow, and stored in BigQuery. The security team requires that data is encrypted at rest and in transit, and that access is limited to specific service accounts. Which implementation strategy meets all requirements?

An organization uses BigQuery on-demand pricing. To control costs, they want to estimate the bytes processed by a query before running it. Which command or method should they use?

You need to create a BigQuery table that stores customer transaction data. The table will be queried frequently by a customer_id column to retrieve recent transactions (last 30 days). Which table design optimizes query performance and cost?

A company uses Vertex AI Feature Store for serving features. They have a high-throughput online serving requirement. Which configuration should they use?

You are designing a streaming pipeline that needs to handle sudden spikes in traffic without losing data. The pipeline uses Pub/Sub and Dataflow. Which configuration ensures data is not lost if Dataflow falls behind?

You have a BigQuery table 'orders' with columns order_id, customer_id, order_amount, and order_date. You need to rank customers by total spend per month, assigning the rank 1 to the highest spender. Which SQL function should you use in a window clause?

A company is migrating on-premises Apache Spark jobs to Google Cloud Dataproc. They want to reduce operational overhead and minimize costs. Which architecture is most appropriate?

You have a BigQuery table that is used by multiple teams. To save costs, you want to provide a consistent view of the data as of a specific point in time without creating full copies. Which BigQuery feature should you use?

What is the root cause of this error and the correct solution?

A company needs to continuously synchronize customer data changes from an on-premises Oracle database to BigQuery for near-real-time analytics. The Oracle database has Change Data Capture (CDC) enabled. Which Google Cloud service should be used to stream these changes with minimal latency and schema evolution support?

You need to deploy a reusable Dataflow pipeline that can be executed with different parameters from Cloud Composer. Which TWO components should you use? (Choose 2)

A data engineer needs to ingest on-premises Oracle CDC data into BigQuery in near real-time with minimal operational overhead. Which service should they use?

A financial analytics team uses Looker to explore BigQuery data. They need to allow business users to filter by a custom date range that is not tied to an existing dimension. The date range must be user-input at query time. What is the best approach in Looker?

An organization needs to trigger a Cloud Run service whenever a new file is uploaded to a specific Cloud Storage bucket. Which service should they use to set up this event-driven architecture?

A company is building a real-time anomaly detection pipeline using Dataflow. Events are ingested from Pub/Sub, and the pipeline must compute a sliding window average every minute over a 1-hour window. Which TWO configurations are required for this pipeline? (Choose 2)

A data engineer needs to build a Dataflow pipeline that reads JSON messages from Pub/Sub, transforms them (including filtering, grouping, and enrichment), and writes the results to BigQuery. The pipeline must handle schema evolution in the input messages and minimize data loss. Which THREE settings or features should the engineer use? (Choose THREE.)

Refer to the exhibit. A BigQuery dataset is shared with the group 'analysts@example.com' using the IAM policy shown. A user who is a member of this group reports that they cannot run queries on the dataset, though they can see the tables. What is the most likely reason?

A data engineering team is operationalizing a machine learning model for real-time fraud detection. The model must process transactions with sub-100ms latency and be highly available. Which TWO strategies should the team implement?

A company uses Google Ads and wants to automatically load their advertising data into BigQuery daily. They also need to transform the data with SQL and schedule a recurring query. Which combination of services meets these requirements with minimal operational overhead?

A team uses Vertex AI AutoML Tables to train a model. They need to deploy the model for real-time predictions with high availability. Which deployment configuration should they use?

A financial services company receives real-time stock trade data via Pub/Sub. They need to enrich each trade with reference data from a Cloud SQL table and write the results to BigQuery for real-time analytics. The enrichment must handle late-arriving data and ensure exactly-once processing. Which Dataflow streaming pipeline configuration should be used?

A team needs to optimize online prediction cost for a model that has unpredictable traffic spikes. Which TWO strategies are most effective?

Refer to the exhibit. A data scientist deploys a model using this configuration. Users report that after a few hours of inactivity, the first prediction request takes over 30 seconds. What is the most likely cause?

A data scientist is training a binary classification model on an imbalanced dataset (95% negative, 5% positive) using AutoML Tables. Which strategy should they use to handle the class imbalance?

Which TWO security best practices should be applied to secure data in transit for a Dataflow streaming pipeline that reads from Cloud Pub/Sub and writes to BigQuery? (Choose 2)

A team has multiple versions of a model and wants to manage them centrally, including tracking metadata and promoting versions to production. Which tool should they use?

A data pipeline uses Cloud Pub/Sub to ingest events, then a Dataflow job writes to Cloud Storage in Avro format. The Dataflow job uses Global windows with a 10-minute trigger. The data is later loaded into BigQuery. They notice duplicate rows in BigQuery because the trigger produced multiple panes. What should the Dataflow pipeline change to eliminate duplicates?

A data engineer needs to inspect a BigQuery table for sensitive data such as credit card numbers and email addresses before sharing it with a third party. The engineer also wants to de-identify the data by masking the sensitive columns. Which Google Cloud service should be used?

A data engineer needs to schedule a recurring batch load of CSV files from an on-premises SFTP server into BigQuery. The files are generated daily and need to be loaded into a partitioned table by date. Which THREE steps should the engineer take? (Choose THREE.)

Which TWO actions can help reduce prediction latency for a Vertex AI endpoint?

Your team uses Cloud Composer to run Apache Airflow DAGs. One DAG uses a BigQueryInsertJobOperator to run a query and then uses BigQueryCheckOperator to verify the results. The DAG is failing intermittently because the query result is not ready when the check operator runs. How should you modify the DAG to ensure the check operator runs only after the query completes successfully?

A company uses Cloud Storage as a data lake with raw, curated, and processed zones. Data in the raw zone should be automatically moved to a cheaper storage class after 30 days, and deleted after 1 year. What is the most efficient way to implement this?

A company wants to process large CSV files stored in Cloud Storage and load them into BigQuery. The files are generated daily and each file is about 10 GB. The data is not time-sensitive and can be processed within a 24-hour window. Which service is most cost-effective for this use case?

A company deploys an ML model using Vertex AI Pipelines. They want to ensure reproducibility and traceability. Which TWO practices should they implement?

A company uses BigQuery with partitioned tables by ingestion time. They notice that queries scanning recent partitions are fast but queries scanning older partitions are slow. What is the most likely cause?

A data engineer is building a data lake on Google Cloud and needs to separate raw ingested data, curated/cleaned data, and processed/aggregated data. Which Cloud Storage bucket structure is recommended?

The exhibit shows an IAM policy for a BigQuery dataset. A Dataflow job is failing with 'Access Denied: Table ... User does not have bigquery.tables.get permission'. Which additional role should be granted to the service account?

A company is building a real-time streaming pipeline to ingest clickstream events from web servers, enrich them with user profile data from Cloud Bigtable, and aggregate metrics into BigQuery. The expected throughput is 10,000 events per second with occasional spikes up to 50,000. The data must be processed with low latency (seconds) and exactly-once semantics. Which Google Cloud service should be the core processing engine?

A company uses Cloud Dataproc to run Spark jobs on ephemeral clusters. The input data is in Cloud Storage and output is also to Cloud Storage. The cluster is created and deleted daily. The cost is high due to spinning up nodes. Which change can reduce cost without sacrificing performance?

A company uses Looker Studio to create dashboards from BigQuery data. They notice that dashboard queries take several seconds to load. They want to improve performance without changing the underlying data or creating materialized views. Which option should they use?

A healthcare company uses Vertex AI to deploy a medical image classification model. The model is deployed on a private endpoint with automatic scaling (minReplicaCount=2, maxReplicaCount=10). The model uses a custom container with a GPU for inference. Recently, during peak business hours (9 AM - 5 PM), users report that prediction requests frequently time out after 60 seconds, and the error rate increases. The team checks Cloud Monitoring and observes that CPU utilization averages 40%, GPU utilization averages 30%, and the number of replicas stays at 2. There are no errors in the container logs. The model serves a few hundred requests per second during peak. The team suspects the issue is not resource saturation but something else. What should they do to resolve the problem?

A startup is deploying a machine learning model for real-time fraud detection. They need low latency and automatic scaling during peak hours. Which Google Cloud service should they use?

A company wants to use BigQuery to query data stored in Cloud Storage as Parquet files without loading the data into BigQuery storage. Which feature should they use?

Your company uses Vertex AI Pipelines to automate the ML lifecycle. The pipeline includes training, evaluation, and deployment steps. You want to ensure that if a pipeline run fails due to a transient error (e.g., resource quota shortage), it automatically retries before marking the run as failed. What is the best way to implement this?

An organization needs to prevent data exfiltration from BigQuery by ensuring all traffic to BigQuery APIs goes through VPC boundaries and is restricted to a specific service perimeter. Which Google Cloud security control should they use?

A large retail company processes point-of-sale transactions from thousands of stores daily. The current batch pipeline runs on Cloud Dataproc using Spark and takes 3 hours to complete. The business wants to reduce processing time to under 30 minutes. The pipeline reads from Cloud Storage, joins with inventory data from BigQuery, performs aggregations, and writes to Cloud SQL for reporting. What is the most effective optimization?

A company uses Cloud Pub/Sub for a real-time data pipeline. The subscription has a backlog of millions of messages that are not being processed quickly enough. In Cloud Monitoring, you observe that the 'subscription/num_undelivered_messages' metric is high and growing, while 'subscription/oldest_unacked_message_age' is also increasing. Which action is MOST likely to reduce the backlog?

A company wants to share a large BigQuery dataset with a partner for analysis. The partner needs read-only access to a specific snapshot of the data as of a certain point in time, and the company wants to avoid additional storage costs for the partner. What is the most cost-effective approach?

Your data engineering team needs to process a continuous stream of clickstream events from a website and update a real-time dashboard showing user activity over the last hour. The pipeline should have minimal operational overhead and support exactly-once processing semantics. Which Google Cloud service should you use?

An e-commerce company processes real-time clickstream data using Pub/Sub and Dataflow. They want to ensure that if a Dataflow worker fails, the pipeline can resume processing from the point of failure without data loss. Which feature should they enable?

Your team uses Cloud Dataproc to run a Spark ML training job. The job is failing with an error: 'Container killed by YARN for exceeding memory limits.' What should you do to fix this?

A data science team uses AI Platform Training with hyperparameter tuning. They observe that some trials fail due to transient errors. To improve solution quality and reduce costs, what should they do?

You want to query data across Google Cloud and AWS using a single SQL interface without moving data. Which TWO services can you use?

A data engineer needs to set up a Dataplex data quality scan to run weekly on a BigQuery table. The scan should check that: (1) the 'email' column is not null, (2) the 'age' column is between 0 and 120, and (3) the 'country_code' column matches a list of valid ISO codes. Which TWO Dataplex features should the engineer use?

Your team needs to store time-series data from millions of IoT devices. Each device sends a reading every 5 minutes, and the total data volume is about 2 TB per month. The most common query pattern is retrieving all readings for a specific device over a time range (e.g., last 24 hours). Which storage service should you choose?

A user named Charlie needs to deploy a model to a Vertex AI Endpoint and also create training jobs. Which role should be assigned to Charlie?

You are designing a Dataflow pipeline that reads from Pub/Sub, performs transformations, and writes to BigQuery. The pipeline must handle schema changes in the incoming data (e.g., new fields appearing). The BigQuery schema should evolve automatically to accept new fields without failing. Which approach should you use?

A company runs a nightly Dataproc batch job to process large log files. The job is idempotent and can tolerate node failures if restarted. Minimizing cost is critical. What is the most cost-effective cluster design?

Your organization uses Vertex AI Feature Store to serve features for a real-time fraud detection model. The model is deployed on a Vertex AI endpoint. After a data pipeline update, the model's online predictions became inconsistent. What is the most likely cause?

A company runs a batch ETL pipeline on Cloud Dataproc. During peak hours, the job takes longer than expected. The pipeline reads from Cloud Storage, transforms data, and writes to BigQuery. What is the most cost-effective way to improve performance without redesigning the pipeline?

Which Google Cloud service provides a serverless Spark environment where you can run Spark jobs without provisioning or managing a cluster?

You are designing a Dataflow pipeline to process streaming data. The pipeline may encounter malformed records. You need to handle these errors without failing the entire pipeline and store the bad records for later analysis. What is the best practice?

A large enterprise is migrating its data warehouse from Teradata to BigQuery. They need to transfer historical data (100 TB) and set up ongoing daily incremental loads. They also need to transform the data using dbt. Which THREE Google Cloud services should they use?

You need to create a reusable Dataflow pipeline for transforming CSV files in Cloud Storage into Avro files in another bucket. The pipeline should be configurable via runtime parameters (e.g., input and output paths). Which approach should you use?

A Dataflow batch job fails consistently with the error shown. The job uses a custom container image and runs in a VPC with a private IP. What should the engineer do to resolve the issue?

You manage a large-scale machine learning system that recommends products to users. The model is a deep neural network trained on TensorFlow and deployed on Vertex AI Endpoint with global load balancing. The model receives over 10,000 requests per second. Recently, the team added a new feature: the user's current geographic location (latitude/longitude). After deploying the updated model, you notice that the average prediction latency has doubled, and the error rate has increased, particularly for requests from regions far from the model's primary training data (North America). You suspect the location feature is causing issues. What should you do to diagnose and mitigate the problem?

A company needs to process streaming data from IoT devices with sub-second latency and exactly-once processing guarantees. Which Google Cloud service should they use?

A company is building a continuous training pipeline that retrains a model daily using new data from a feature store. The training data must include features computed up to the timestamp of each training run. Which architecture should be used to ensure time-consistent feature values without label leakage?

A financial services firm uses Cloud Pub/Sub to ingest real-time market data. The data is processed by a Cloud Dataflow streaming pipeline that aggregates trades per symbol and writes to BigQuery. The pipeline currently uses a single global window with a trigger that fires every minute. The firm now needs to support late data up to 5 minutes and also wants to reduce the number of writes to BigQuery to avoid hitting the table limit of 1,500 inserts per second. The current pipeline writes every minute, which is acceptable for inserts per second, but after adding late data handling, the number of writes doubles. How can you redesign the pipeline to handle late data while keeping write volume low?

A data team needs to run complex analytical queries on a dataset that is frequently updated with new rows. They want to minimize query costs and avoid scanning old data that is rarely queried. Which BigQuery feature should they use?

A data engineer needs to store quarterly financial data that must remain immutable for 7 years to meet regulatory compliance. The data is accessed infrequently after the first year. Which Cloud Storage feature should be used to enforce immutability?

You are optimizing a BigQuery query that scans 1 TB of data every day. The query joins a large fact table (partitioned by date) with a small dimension table. You notice that the query always scans the entire fact table, even though you only need the last 7 days of data. Which optimization will MOST reduce the bytes scanned?

A company runs a Dataflow streaming pipeline that reads from Cloud Pub/Sub and writes to BigQuery. The pipeline uses a side input that is a large lookup table (10 GB) stored in Cloud Storage. The side input is updated hourly. The pipeline experiences high latency and OOM errors on workers. What is the best approach to resolve this?

An e-commerce application uses Cloud SQL (MySQL) for transaction processing. To improve read performance for reporting queries, the team wants to offload read traffic to a separate database instance that stays in sync with the primary. Which Cloud SQL feature should they use?

A company is evaluating BigQuery for a data warehouse migration. They have a mix of reporting queries and ad-hoc analytical queries. They want to control query costs and prevent runaway queries. Which THREE strategies should they implement?

A company is designing a streaming pipeline using Dataflow to process real-time clickstream data. The pipeline reads from Pub/Sub, performs user sessionization using Apache Beam's Session window, and writes to BigQuery. The team notices that the pipeline's lag is growing and the worker utilization is low. What is the most likely cause and recommended fix?

A company wants to migrate 500 TB of on-premises archival data to Cloud Storage. The data is stored on a SAN and the network link is limited to 1 Gbps. The migration must complete within 10 days. What is the MOST cost-effective approach?

Your team is using Cloud Data Fusion to build batch ETL pipelines that load data from Cloud Storage into BigQuery. You have several pipelines that run daily. Recently, one pipeline started failing with a 'Permission denied' error when trying to read a new CSV file uploaded to a specific Cloud Storage bucket. Other pipelines using the same bucket succeed. The failing pipeline has a Cloud Storage source plugin that uses a service account with the roles/storage.objectViewer role. The bucket has uniform bucket-level access enabled. What is likely causing the issue?

A data pipeline is built with Cloud Dataflow that reads from Pub/Sub, applies transformations, and writes to BigQuery. The pipeline is experiencing high latency and occasional data loss during worker failures. The engineer wants to improve reliability and performance. Which two actions should they take?

A company runs a Cloud Dataflow streaming pipeline that reads from Cloud Pub/Sub, performs a fixed window of 10 seconds, joins with a slowly-changing dimension table stored in Cloud Bigtable, and writes results to BigQuery. The pipeline has been running for months but recently started exhibiting increasing latency and occasional data loss. The pipeline uses default settings with autoscaling enabled (min 2, max 20 workers). The Bigtable cluster has 3 nodes. The dimensions are updated infrequently. The latency has grown from seconds to minutes. Examining the Dataflow monitoring UI, you see that the 'System Lag' metric is increasing, and some windows are not being emitted. The CPU utilization on Bigtable nodes is below 50%. There are no errors in the logs. Which action is most likely to resolve the issue?

A data engineer is using Spark on Dataproc to process a large dataset. They notice the job is slow due to excessive shuffling. They want to optimize the job by using a more efficient data structure that reduces serialization overhead and provides better memory management. Which Spark API should they use?

Your Dataflow streaming pipeline is processing financial transactions and writing results to BigQuery. You need to monitor the pipeline for data freshness (end-to-end latency) and alert if it exceeds 5 minutes. The pipeline uses fixed windows of 1 minute. Which metrics should you use for alerting?

A company stores IoT sensor readings in BigQuery. The table is partitioned by day and clustered by sensor_id. Query performance has degraded as data grows; many queries filter by a date range and a single sensor_id. Which optimization should be applied first?

A company wants to use BigQuery materialized views to accelerate queries on a table that is updated every hour. Which statement about materialized views is true?

The query above runs slowly on the 10 TB table. Which optimization would most improve performance?

A data engineer needs to design a BigQuery dataset for a multi-team environment. Each team should have read access only to specific tables, and the data must be protected from accidental deletion. Which THREE steps should they take?

A team is using Kubeflow Pipelines on Google Kubernetes Engine to orchestrate ML workflows. They need to track parameters, metrics, and artifacts for each run. Which tool should they integrate?

You are designing a streaming data pipeline that must guarantee exactly-once processing semantics for financial transactions. The pipeline reads from Cloud Pub/Sub and writes to Cloud Bigtable. Each transaction has a unique transaction ID. Which features do you need to implement to ensure exactly-once semantics end-to-end?

A company is using Pub/Sub to ingest clickstream events and Dataflow to write to BigQuery. They observe that some events are malformed and cause the pipeline to fail. They need a solution that captures malformed events without blocking the pipeline and allows reprocessing later. Which Dataflow pattern should they implement?

A data engineer needs to process a large dataset (500 TB) stored in Cloud Storage using Dataproc. The processing job requires reading the entire dataset and writing results back to Cloud Storage. The job is expected to run for 6 hours. Which configuration minimizes cost?

You are running a streaming pipeline with Dataflow that reads from Pub/Sub and writes to BigQuery. You notice that the system lag metric is increasing over time, indicating that messages are taking longer to process. What is the most likely cause and how should you address it?

A financial services company operates a real-time fraud detection pipeline using Apache Beam running on Google Cloud Dataflow. The pipeline reads transactions from Pub/Sub, enriches them with customer data from Bigtable, runs a machine learning model with side inputs from a Redis cluster, and writes results to BigQuery for downstream reporting. The data must be processed with exactly-once semantics to avoid duplicate fraud alerts or missing transactions. The pipeline currently uses a global window with 5-minute accumulation, but the team is experiencing high latency and occasional duplicates when the model side input is updated (triggered every 15 minutes via a WatchTransform). Additionally, the pipeline has a dead letter queue that outputs failed records to a separate Pub/Sub topic, but these records are never reprocessed. The team needs to ensure high reliability and data quality. Which course of action should the team take to improve solution quality?

A retail company needs to generate product recommendations for millions of users every few hours. The model is a small scikit-learn model. Which prediction method should be used to minimize infrastructure cost while meeting the latency requirements?

A company runs batch jobs on Dataproc. They need to ensure that if a job fails, it automatically retries with exponential backoff. What is the recommended approach?

A team is setting up a Dataflow pipeline for a time-sensitive ETL job that must complete within a specific time window. Which monitoring metric should they use to determine if the pipeline is on track to finish on time?

A data engineer needs to create a BigQuery table that is optimized for queries that filter on a 'customer_id' column and sort by 'transaction_date'. The table will be used for interactive analysis. Which combination of table features should be used?

A data engineer needs to query data across BigQuery (in Google Cloud) and Snowflake (in AWS) without moving the data. Which service should they use?

A streaming pipeline ingests events from Pub/Sub, enriches them via a slow REST API call, and writes the result to BigQuery. The API has a limit of 10 requests per second per client. The pipeline processes 1000 messages per second. Which approach minimizes latency while respecting API limits?

You need to choose a messaging service for a real-time streaming application that requires low cost and can tolerate occasional message loss. Which service is MOST suitable?

The exhibit shows a Spark job submitted to Dataproc that fails with an out-of-memory error. Which change should be made to the submission command to resolve the issue?

A company needs a fully managed, globally distributed relational database with strong consistency, external consistency, and 99.999% SLA for a financial transaction processing system. Which Google Cloud service should they use?

A financial services company uses Vertex AI to serve a fraud detection model. The model was trained on historical data that is updated daily. The team wants to automate retraining when data drift is detected. Which approach best operationalizes this requirement with minimal manual intervention?

A data engineer is responsible for a batch ETL pipeline that runs daily using Cloud Composer and Dataproc. The pipeline extracts data from Cloud SQL, transforms it with Spark, and loads to BigQuery. Last night, the pipeline failed because the Spark job ran out of memory. The team needs a solution that prevents future failures without manual intervention. Options: A. Enable Dataproc autoscaling and configure memory-based scaling. B. Use Cloud Functions to retry the job. C. Use a larger machine type for Dataproc. D. Split the Spark job into multiple stages.

Your organization has a data lake on Cloud Storage with millions of small files (average 10 KB). You need to build a batch processing pipeline using Cloud Dataproc that runs a Spark job to transform the data and output results to BigQuery. The pipeline currently takes 4 hours to run because Spark spends a large amount of time listing files and managing tasks. You want to reduce the run time without changing the cluster size. Which action should you take?

Your company has a Dataproc cluster that runs Spark jobs. You need to choose between RDDs, DataFrames, and Datasets for a new job that performs complex aggregations on structured data. Which TWO statements are correct regarding performance and ease of use?

A Dataflow streaming pipeline reads from Pub/Sub, applies a ParDo that uses a side input from a BigQuery table (refreshed hourly), and writes to BigQuery. The side input is large and causes increased latency and worker OOM errors. Which design change solves this?

A team is designing an event-driven data pipeline. They need to process messages from Cloud Pub/Sub, transform them, and write to BigQuery. The messages have variable volume and spikes. What is the best serverless compute option for this workload?

A company wants to use AutoML Tables to build a classification model on a dataset with 100 features and 500,000 rows. They need to deploy the model for online predictions with low latency (<100 ms). Which deployment option should they choose?

Based on the exhibit, what is the most likely cause of duplicate rows despite using the same event_id as insertId?

A team wants to use Cloud Storage to build a data lake with separate zones for raw, curated, and processed data. They need to automatically move objects older than 30 days from the raw zone to a cheaper storage class. How can they achieve this?

A team deploys a Cloud Run service that processes user-uploaded files. Some requests time out after 60 minutes. They need to handle large files reliably without losing tasks. What is the best solution?

Your team is responsible for operationalizing a series of machine learning models that are trained and deployed using Vertex AI Pipelines. The pipeline consists of several steps including data preprocessing, training with hyperparameter tuning, model evaluation, and deployment to an endpoint. Recently, the pipeline has been failing intermittently at the model evaluation step with an error indicating insufficient memory. The evaluation step uses a custom container with a memory limit of 4 GB. The training step uses 8 GB and completes successfully. You need to resolve the failure without drastically increasing costs. What should you do?

An organization needs to restrict access to BigQuery and Cloud Storage so that data can only be accessed from within a specific VPC network and cannot be exfiltrated. Which Google Cloud feature should they use?

You are designing a Cloud Spanner schema for a global e-commerce application. The database will include a Customers table and an Orders table. To optimise performance for queries that join Customers with their Orders, which THREE design choices are recommended? (Choose 3.)

Which THREE practices are recommended when designing a Cloud Data Fusion pipeline to ensure efficient execution and monitoring? (Choose three.)

A data pipeline uses Cloud Composer to orchestrate Dataflow and BigQuery jobs. The pipeline fails intermittently with dependency errors. Which design change can improve reliability?

A data engineer needs to orchestrate a complex data pipeline that involves multiple steps including data extraction from Cloud Storage, transformation using Dataflow, and loading into BigQuery. The pipeline has dependencies between tasks and requires monitoring and retries. Which Google Cloud service should be used for orchestration?

A financial institution needs to deploy a TensorFlow model for fraud detection with strict latency requirements (<100ms). The model uses custom ops that are not available in standard TF Serving. What is the most appropriate serving solution?

A Dataflow pipeline reads from Cloud Pub/Sub and writes to Cloud Storage. The pipeline needs to guarantee exactly-once processing despite worker failures. Which configuration ensures exactly-once semantics?

A data engineer needs to design a data processing system that ingests large volumes of sensor data from IoT devices. The data should be stored in a schema-less format and allow for real-time analytics. Which Google Cloud service is most appropriate?

You are designing a streaming Dataflow pipeline that reads from Cloud Pub/Sub. Some data may arrive late due to network delays. You need to ensure that late-arriving data is still processed, but after a certain point, it should be discarded to avoid unbounded state. What is the best practice?

You need to stream real-time user click events from your application into BigQuery for immediate analysis. The events must be available for query within seconds. Which approach is recommended?

A Cloud SQL instance for PostgreSQL is experiencing heavy read traffic. The team wants to offload read queries while maintaining data consistency. Which solution meets their needs?

A company is migrating an on-premises PostgreSQL database to Google Cloud. The database runs complex analytical queries mixed with OLTP workloads. They need PostgreSQL compatibility and want to improve analytical query performance without changing the application. Which database should they choose?

You are building a multi-cloud analytics solution to join data from Google Cloud and AWS S3. You need to query the S3 data using BigQuery without moving it. Which Google Cloud service should you use?

You have a BigQuery table with sales data and want to pivot product categories into columns. Which SQL clause should you use?

You are migrating on-premises Hadoop jobs to Google Cloud. The existing jobs use Spark for ETL and Hive for querying. You want to minimize changes to the existing code and maintain the ability to use Hive queries with the same metastore across multiple clusters. Which service combination should you use?

A data engineer is running a Dataproc cluster for a batch ETL job that needs to process 10 TB of data. The job is memory-intensive. The cluster currently uses n1-standard-4 workers. Performance is poor. What is the most cost-effective change to improve performance?

You need to transform and clean messy CSV data using a visual interface without writing code. The transformation should be scheduled to run weekly. Which Google Cloud service should you use?

A company uses Cloud Dataflow to process financial transactions from Pub/Sub to BigQuery. The pipeline must ensure exactly-once semantics. Recently, they noticed duplicate rows in BigQuery. The source publishes with at-least-once. The Dataflow pipeline uses idempotent writes. What is the most likely cause? Options: A. The pipeline uses GlobalWindows. B. The pipeline has autoscaling enabled. C. The pipeline uses file loads as a sink. D. The pipeline's watermark is misconfigured.

You need to select two BigQuery features that improve query performance by reducing the amount of data read. Which two options accomplish this? (Choose TWO)

A data engineering team uses Cloud Data Fusion to build ETL pipelines. They have a pipeline that reads from Cloud SQL, transforms data using Wrangler, and writes to BigQuery. The pipeline fails intermittently with a 'connection timeout' error from Cloud SQL. What is the best way to handle this?

A company uses Dataplex to manage data lakes on Google Cloud. They want to enforce data quality rules on a BigQuery table, such as ensuring that a 'email' column is not null and matches a regex pattern. Which Dataplex feature should they use?

A company wants to ensure high availability for their Cloud SQL instance. Which TWO actions are most appropriate? (Choose two.)

Which TWO actions can help reduce the latency of a Vertex AI endpoint serving a large neural network model?

A data engineer is designing a Bigtable row key for a time-series application that records temperature sensor readings every second. To avoid hotspotting, they want to distribute writes across all nodes. Which row key design is best?

A retail company is building a recommendation engine that requires processing customer clickstream data in near real-time. The data is ingested via Pub/Sub, and must be joined with a lookup table of product details (updated daily) before being used for model inference. Which design pattern should they use?

A media company ingests video files from partners via a REST API. Files are stored in Cloud Storage, and metadata is written to Firestore. A Cloud Function is triggered on object finalize to transcode video using Transcoder API. Sometimes, the function fails because the file is still being uploaded when triggered. How should this be fixed?

A multinational e-commerce company runs a real-time recommendation system. The architecture: user click events are sent via HTTP to a Cloud Run service, which publishes them to a Cloud Pub/Sub topic. A Dataflow streaming pipeline reads from the subscription, joins with user profile data from Firestore, computes recommendations using a TensorFlow model (loaded as a side input), and writes results to a Redis cache (Memorystore) for low-latency serving. The pipeline is deployed in us-central1. Recently, the team noticed that recommendation latency has increased from 50ms to 500ms, and the pipeline's backlog is growing. The Dataflow monitoring shows high CPU utilization on workers, and the SystemLag metric is 2 minutes and increasing. The Redis cluster shows no performance issues. The Firestore queries are within normal latency. The team suspects the TensorFlow model inference is the bottleneck. The model is a large neural network (500MB) loaded in each worker's memory. The pipeline uses 10 n1-standard-4 workers. The pipeline is using Dataflow's streaming engine. The team wants to reduce latency without increasing cost significantly. What should they do?

Match each data storage term to its characteristic.

A data scientist wants to import a pre-trained TensorFlow model into BigQuery ML for batch predictions. The model is stored in a Cloud Storage bucket. Which statement is correct?

Your company runs a batch data processing pipeline using Cloud Dataproc and Cloud Composer. The pipeline processes hundreds of terabytes of data daily. Recently, the pipeline has been failing intermittently due to Dataproc cluster creation errors: 'Insufficient resources to create cluster in zone us-central1-f.' The project has a global quota of 1000 vCPUs for Compute Engine. The team usually uses n2-standard-8 (8 vCPU) worker nodes. You notice that the error occurs during peak usage times. You need to ensure the pipeline runs reliably without increasing the global quota. Which action should you take?

You are designing a system to serve predictions from a large language model (LLM) with a latency SLO of 500ms. The model does not fit on a single GPU and requires model parallelism. You are considering using Vertex AI Endpoints with a custom container. What additional setup is required to achieve the latency target?

A company uses Cloud Pub/Sub for event ingestion. They want to ensure that if a subscriber fails to process a message after 5 attempts, the message is sent to a dead letter topic for analysis. Which TWO configurations are needed?

A company wants to use Cloud DLP to inspect data in BigQuery for sensitive information and de-identify it by masking credit card numbers. They want to perform this on a schedule. Which approach should they take?

You are building a data pipeline that ingests data from on-premises into Cloud Storage, then processes it with Dataproc, and finally loads into BigQuery. You need to schedule the pipeline to run daily. The pipeline must handle occasional failures gracefully. Which THREE Google Cloud services should you use together to achieve this? (Choose 3)

A data engineer needs to query a BigQuery table that contains an array of structs. They want to expand the array into separate rows for each element. Which SQL function should they use?

You are designing a row key for Cloud Bigtable to store user activity logs. Each log entry has a timestamp (millisecond precision) and a user ID. There will be millions of writes per second from many users. To avoid hotspotting, which row key design is BEST?

A company uses BigQuery to store event data. They need to load data from multiple sources with different schemas and expect frequent schema changes. Which approach provides the most flexibility for schema evolution while minimizing load failures and performance impact?

Match each Google Cloud monitoring/logging service to its function.

A financial services company has a BigQuery dataset containing sensitive customer data. They need to share a subset of this data (excluding PII columns) with an external analytics partner. The partner should be able to query the data using their own BigQuery account, but the company must maintain full control over the underlying table and ensure the partner cannot see or access the original table. Which approach should they use?

A data pipeline uses Cloud Pub/Sub to ingest events and Cloud Functions to transform and write to BigQuery. The system is experiencing data loss during Pub/Sub subscription outages. Which design change improves reliability?

A company stores sensitive customer data in BigQuery. They need to implement column-level security to restrict access to personally identifiable information (PII) columns. Which two BigQuery features can they use together? (Choose TWO)

An organization runs periodic Apache Spark jobs on Dataproc to process data from Cloud Storage. They want to reduce costs by using preemptible instances for worker nodes. What is a key consideration when using preemptible instances in Dataproc?

A company needs to deploy a trained model for real-time predictions with low latency. Which Vertex AI resource should they use?

Match each data encryption concept to its description.

Your company is evaluating managed messaging services for a new event-driven application. The application requires pub/sub semantics, high throughput (millions of messages per second), and integration with Google Cloud services like Cloud Functions and Dataflow. Which TWO services should you consider? (Choose two.)

A company runs a Dataproc cluster for ETL jobs that process data nightly. They want to reduce costs while maintaining performance. Which strategy is MOST effective?

A company uses Dataplex to manage data quality across multiple BigQuery datasets. They need to define data quality rules that check for null values in critical columns and enforce uniqueness constraints. Which two Dataplex features should they use? (Choose TWO)

You need to analyse streaming data from thousands of IoT devices, each sending temperature readings every second. You want to calculate the average temperature per device over the last 5 minutes, updating every minute. Which windowing strategy should you use in Dataflow?

You are a data engineer at a financial services company. You manage a batch pipeline that processes daily trade settlement reports. The pipeline runs on Cloud Dataproc using PySpark jobs triggered by Cloud Composer (Airflow). Recent trades have increased by 3x, and the pipeline now frequently fails with 'OutOfMemoryError' in the executor logs. You have already increased the executor memory from 4g to 8g, but the problem persists. The cluster uses standard worker nodes (n1-standard-4) with 15 GB RAM per node. You need to make the pipeline stable and cost-efficient. What should you do?

A Dataflow pipeline reads events from Pub/Sub and transforms them. Some events contain invalid product IDs that should be filtered out. The list of valid product IDs is stored in a frequently updated BigQuery table. What is the best approach to filter out invalid events?

A company needs to process sensitive data in BigQuery with column-level security. They want to allow analysts to see aggregated data but not individual records. What approach?

A team is deploying a model on AI Platform Prediction. They want to monitor for data drift to maintain model quality. Which service should they use?

A company needs to stream data from a fleet of IoT devices to BigQuery for near-real-time analytics. The data volume is unpredictable and can spike during certain events. Which Google Cloud service should be used as the ingestion point to handle variable throughput with minimal operational overhead?

Based on the exhibit, what is the most likely cause of the out-of-memory error?

A Dataflow pipeline reads from Pub/Sub, applies a keyed stateful ParDo that uses state variables to deduplicate events based on event ID, and writes to BigQuery. During a pipeline update, some events are duplicated in BigQuery. The state is not preserved across updates. Which configuration ensures exactly-once semantics during updates?

A company stores IoT sensor data in BigQuery. Queries that filter on a timestamp column and a device_id column are slow even though the table is partitioned by day. What should the data engineer do to improve query performance?

A data scientist needs to provide explanations for each prediction made by a deployed autoML model to comply with regulatory requirements. Which Vertex AI feature should they use?

You need to automate retraining of a model when new training data becomes available every week. The training pipeline runs on Vertex AI Pipelines and is triggered by Cloud Composer. After retraining, you want to evaluate the new model against a golden dataset. If the model's accuracy improves by at least 1%, it should be automatically deployed to the staging endpoint. What is the best way to implement the decision logic?

A company wants to implement model monitoring for a deployed classification model. Which three types of monitoring should they set up? (Select 3)

A data team uses Cloud Composer to orchestrate Airflow DAGs. They need to ensure that a downstream task runs only if at least two out of three upstream sensor tasks succeed. Which TWO configurations should they combine?

A data engineer is designing a Cloud Bigtable schema for high-volume time-series data. Which TWO practices should they follow to avoid performance issues?

A company uses Dataproc Serverless for Spark batch jobs. They notice that some jobs are failing due to out-of-memory (OOM) errors. Which configuration parameter should they adjust to allocate more memory per executor?

A company uses Cloud Composer for pipeline orchestration. They need to define task dependencies where Task B and Task C can run in parallel after Task A, and Task D must run after both B and C complete. How should they define the DAG?

You are designing a streaming pipeline that ingests events from Pub/Sub, enriches them with a machine learning model, and writes the results to BigQuery. The ML model is deployed on Cloud Run and has a high latency (500ms per request). You need to minimize the impact of slow ML inference on the overall pipeline throughput. Which approach should you take?

A data engineer needs to create a Dataflow pipeline that reads from Pub/Sub, applies a Python transformation, and writes to BigQuery. The pipeline should be reusable across environments with different parameters. Which deployment method is most appropriate?

A company wants to use BigQuery for analytics. They need to meet compliance requirements by encrypting data at rest with a key they control. Which TWO actions should they take? (Choose 2.)

A data science team uses Vertex AI Pipelines to automate retraining. They want to ensure that only models with performance above a threshold are deployed. Which component should they add to the pipeline?

The push endpoint is returning 500 errors. What is the most likely cause?

You are designing a Cloud Composer workflow that loads data from Cloud Storage into BigQuery, runs a Dataflow job to transform the data, and then triggers a Dataproc Spark job. After each step, you need to conditionally branch based on success or failure. Which Airflow feature allows you to pass messages between tasks to enable dynamic branching?

Your company uses Cloud Data Fusion to build ETL pipelines. You have a pipeline that reads from Cloud Storage, transforms data using a custom Wrangler recipe, and writes to BigQuery. The pipeline is failing with an error indicating that the Wrangler directive is invalid. You have verified the recipe works in the Cloud Data Fusion Studio. What is the most likely cause of the failure?

A financial services company deploys a regression model to predict loan default risk. The model is served using Vertex AI Endpoints with autoscaling. After deployment, latency increases significantly during peak hours, causing timeouts. The model uses scikit-learn and has a large feature set. Which action should the team take to reduce latency while maintaining prediction accuracy?

Your company uses Cloud Dataflow to process streaming data from Pub/Sub. The pipeline occasionally fails with a 'worker terminated unexpectedly' error. What is the most likely cause of this error?

A company trains a custom model using TensorFlow and wants to deploy it to Vertex AI for low-latency predictions. The model is large (2 GB). Which deployment option should they choose?

A company runs a real-time fraud detection model using Cloud Dataflow for streaming inference. The model is updated every hour with new training data. The team wants to minimize downtime and ensure that both old and new model versions are available during the update. Which deployment strategy should they use?

A developer wants to create a BigQuery table that automatically expires data older than 30 days to reduce storage costs. Which table design feature should be used?

A company uses BigQuery for analytics. They have a table that is queried frequently by date range. To reduce costs, they want to ensure queries only scan the relevant partitions. They also want to improve performance for queries filtering on a specific customer_id. Which table design should they use?

A company is developing a streaming Dataflow pipeline to process real-time sensor data. To ensure data quality, the team wants to detect malformed records and late data. Which two practices should they implement? (Choose two.)

A company processes financial transactions using Cloud Dataflow. They need to ensure that late-arriving data is handled correctly for fraud detection. The pipeline uses event time processing. Which approach should they use to handle late data?

An e-commerce company deploys a recommendation model on Vertex AI Endpoints. The endpoint receives a high volume of requests with a large payload. They notice high latency and occasional timeouts. Which action should they take to improve performance without sacrificing accuracy?

A company uses Kafka on Dataproc to ingest streaming data. They want to process the data with Spark Structured Streaming and write results to BigQuery. The team is using Dataproc clusters. Which approach minimizes cost while maintaining performance?

A data engineer needs to automatically delete objects from a Cloud Storage bucket after 30 days and archive them to nearline storage after 7 days. Which configuration should they use?

A company wants to automate model retraining and deployment whenever new training data becomes available. Which service should be used to orchestrate the end-to-end workflow?

You are optimizing a BigQuery query that runs on a large table (hundreds of TB). The table is partitioned by date and frequently queried with filters on a specific customer_id column and date range. Queries are slow even after partitioning. Which optimization should you apply?

A data pipeline ingests streaming events into Pub/Sub. You need to guarantee that each event is processed exactly once downstream in Dataflow. Which combination of Pub/Sub and Dataflow configurations should you use?

A company uses Dataflow to process streaming data from Pub/Sub. They notice increased processing latency. What is the most likely cause?

A data engineer needs to split time-series data for training a forecasting model. The data is sorted by timestamp. The engineer wants to avoid leakage where future data influences training. Which data splitting approach should they use?

A data team is building a near-real-time dashboard that displays aggregated metrics from Kafka topics. They want to use Pub/Sub as a managed messaging service and Dataflow for stream processing. They need to ingest data from Kafka into Pub/Sub with minimal custom code. Which THREE Google Cloud services should they use together? (Choose three.)

A company runs a critical real-time data pipeline using Dataflow that ingests events from Cloud Pub/Sub, performs aggregations using sliding windows, and writes results to BigQuery. The pipeline is deployed in us-central1. The pipeline's latency has increased recently, and the Dataflow monitoring shows that the 'system lag' metric is consistently above 5 minutes. The pipeline is using Streaming Engine and has 10 workers with 4 vCPUs each. The pipeline processes approximately 100,000 events per second. The team has verified that the source Pub/Sub topic has sufficient publish throughput and the BigQuery table has no quota issues. The pipeline logs show that some workers are experiencing GC overhead limit exceeded errors. The pipeline code uses stateful processing with a custom keyed state for deduplication. What is the most likely cause of the increased latency?

A company is deploying a large-scale streaming application on Google Kubernetes Engine. They need to ensure the application can handle sudden traffic spikes without dropping data. Which architectural pattern is most appropriate?

A company uses Looker to define business logic in LookML. They need to create a new measure that calculates the average order value, defined as total revenue divided by number of orders. Which LookML syntax should they use?

A company's Dataflow pipeline uses the PubSubIO source to read messages and writes to BigQuery via the BigQueryIO sink. The pipeline is running in Streaming mode with exactly-once semantics enabled. Occasionally, duplicate rows appear in BigQuery. What is the most likely reason?

A company has a trained model stored in Vertex AI Model Registry. They want to automate retraining when new training data arrives in Cloud Storage. Which approach is most efficient?

Which THREE actions reduce the cost of a Cloud Composer environment?

You have a BigQuery table 'events' with a TIMESTAMP column 'event_time'. You need to compute, for each event, the difference in seconds from the previous event of the same user. Which window function should you use?

A data engineer is designing a real-time fraud detection system using Dataflow. The system must detect patterns across events from multiple users within a sliding window of 10 minutes. Events arrive on Pub/Sub topics per user. Which approach should they use to join the streams?

Your team is migrating a legacy batch processing system that uses Apache Spark on-premises. The migration must be completed with minimal code changes and support both batch and streaming in the future. You want to use a fully managed service. Which Google Cloud service is most appropriate?

A healthcare company processes patient data using a Dataflow pipeline that reads from Cloud Storage, transforms data, and writes to BigQuery. They need to ensure that the processing is idempotent to handle failures and retries without duplicating records. The data arrives in daily batches and may be re-delivered if earlier processing failed. What approach should they take to guarantee exactly-once processing in BigQuery?

A Dataflow streaming pipeline is experiencing high latency and frequent OOM errors when processing variable-sized JSON messages from Pub/Sub. The team suspects that the autoscaling is not effective. Which feature should they enable to improve resource utilization?

What is the most likely cause of data duplication after this command?

A data engineer needs to transfer 500 TB of on-premises data to Google Cloud Storage. The data is stored on NAS devices and the network bandwidth is limited to 100 Mbps. What is the most cost-effective and timely transfer method?

A retail company uses Dataflow to process real-time clickstream data. They need to enrich each event with customer profile data from Cloud Bigtable and session metadata from Cloud Spanner. Which two Dataflow features should they use?

A data engineer is designing a batch ETL pipeline that reads CSV files from Cloud Storage, transforms them using Dataproc, and writes the results to BigQuery. The data volume is expected to grow 10x in the next year. Which design approach best balances cost and performance?

Which Google Cloud service provides a fully managed, serverless Spark environment without requiring cluster provisioning?

A company wants to implement a robust MLOps lifecycle on Google Cloud. Which THREE components are essential?

A healthcare company deploys a model for diagnosing medical images on Vertex AI using a custom container with a TensorFlow model. The model uses a mixture of GPUs (NVIDIA T4) and CPUs. After deployment, you notice that prediction latency is highly variable: sometimes under 100ms, sometimes over 10 seconds. Investigation shows that the variability correlates with the number of concurrent requests. The endpoint has a min replicas of 1 and max replicas of 3, with target CPU utilization set to 80%. You also observe that GPU utilization remains low (<20%) even during high load. What is the most likely cause of the latency variability? A) The model is not fully utilizing GPUs due to inefficient data loading from CPU. B) The autoscaling metric (CPU utilization) is not appropriate for a GPU-bound workload; the endpoint does not scale based on GPU utilization. C) The GPU machine type is too small for the model. D) The container is not configured to use the GPU correctly.

You need to process a large Spark ML training job on a Dataproc cluster. The job is fault-tolerant and can handle occasional node failures. To reduce costs, which type of worker nodes should you use?

A financial services company needs to process high-frequency trading data with strict ordering guarantees. They use Pub/Sub with ordering keys and Dataflow. The pipeline occasionally produces out-of-order results. What is the most likely cause?

A data engineer is using Apache Spark on Dataproc to process a large dataset. They need to perform complex aggregation and transformation with high performance. The dataset has a known schema and they want to take advantage of Catalyst optimizer. Which Spark API should they use?

Which BigQuery feature allows you to read data directly from Cloud Storage without loading it into BigQuery storage?

Your company has deployed a machine learning model on Vertex AI Endpoint to serve real-time predictions for a mobile application. The model was trained using TensorFlow and the prediction requests include raw images that are preprocessed by the client before sending. Recently, the application developers reported that the predictions are becoming less accurate over time. They suspect the issue is related to changes in the client-side preprocessing code. You need to verify this hypothesis and monitor for future regressions. What should you do?

A company is ingesting real-time sensor data from thousands of devices into Cloud Pub/Sub. They need to process this data with low latency (seconds) and exactly-once semantics. Which data processing service should they use?

A data engineering team uses Cloud Pub/Sub to ingest clickstream events and Cloud Dataflow to process them. They need to maintain strict event ordering per user session, and the processing output must be written to a BigQuery table with exactly-once semantics. Which configuration should the team implement?

A data engineer needs to create a Dataflow pipeline template that can be reused across multiple environments (dev, staging, prod) with different parameters (e.g., input Pub/Sub topic, output BigQuery table). Which template type should they use?

An organization uses Cloud Storage to store backup files. They want to automatically delete files older than 90 days, and after deletion, move remaining files to Nearline storage if not accessed for 30 days. Which Cloud Storage feature should they configure?

Which TWO roles are required to allow a service account to run a Dataflow job and write results to BigQuery? (Choose two.)

You need to orchestrate a simple, linear workflow that calls several Cloud Functions and API endpoints sequentially with conditional logic. The workflow should be defined as code and have minimal overhead. Which GCP service should you use?

A data engineer is building a Dataflow pipeline that reads from BigQuery, transforms data using Apache Beam, and writes results to Cloud Storage in Avro format. They need to ensure the pipeline can be easily redeployed with different parameters without modifying code. Which deployment method should they use?

A data engineer uses Cloud Composer to orchestrate a daily batch pipeline. A downstream task should only start after an upstream BigQuery load job finishes successfully and a specific file appears in Cloud Storage. Which combination of operators should the engineer use in the Airflow DAG?

A company deploys a new machine learning model for real-time predictions using Vertex AI. The model is stored in a Cloud Storage bucket and deployed to an endpoint. To ensure traceability and rollback capability, which practice should be followed?

You are designing a Dataflow pipeline that joins two unbounded PCollections from different sources. Which transform should you use?

A company wants to use BigQuery's PIVOT operator to transform their sales data. They have a table with columns: 'year', 'quarter', 'revenue'. They want to create a report where each row is a year and each column is a quarter (Q1, Q2, Q3, Q4) showing revenue. Which SQL statement is correct?

A company wants to ingest IoT sensor data from thousands of devices into BigQuery for near-real-time analytics. The data volume is approximately 10 GB per hour. Which combination of Google Cloud services should they use for a cost-effective and scalable solution?

Which THREE considerations are important when designing a data lake on Google Cloud using Cloud Storage?

You are designing a Dataflow pipeline that reads from Pub/Sub and writes to BigQuery. Some incoming messages are malformed and fail to parse. How should you handle these messages to ensure the pipeline continues processing without data loss?

Which THREE Google Cloud services are considered fully managed serverless data processing services? (Choose THREE.)

A company is designing a data lake on Google Cloud. The data lake will store raw, curated, and analytics-ready data. Security requirements include: data must be encrypted at rest and in transit, access must be controlled based on data sensitivity (public, internal, confidential), and all access to sensitive data must be audited. The company also wants to minimize data transfer costs for frequently accessed curated datasets. Which combination of services and configurations best meets these requirements?

A company wants to analyze server logs stored in Cloud Storage using SQL. They need to get results in seconds without setting up any clusters. Which service should they use?

You are designing a streaming pipeline using Cloud Dataflow with exactly-once semantics. The source is Pub/Sub and the sink is Cloud Bigtable. The pipeline must handle late data up to 10 minutes. You need to minimize cost while maintaining correctness. Which configuration should you use?

A data scientist uses Vertex AI Workbench notebooks for model development. They want to share the environment with team members while maintaining version control. Which approach should they use?

A data pipeline uses Dataflow to read from Pub/Sub, window messages into 1-minute fixed windows, and write to BigQuery. The pipeline occasionally has late-arriving data. How should they configure the pipeline to allow late data up to 5 minutes and then trigger a final pane?

A team monitors a deployed Vertex AI model and notices an increasing number of prediction errors with status code 413 (Request Entity Too Large). Which TWO actions should they consider to resolve this issue?

A company needs to process data from a legacy system that outputs CSV files daily. They want to visually build transformations without writing code. Which Google Cloud service should they use?

A company needs to process sensitive healthcare data with strict compliance requirements. They want to use Cloud Dataflow but must ensure data is encrypted end-to-end and audit logs are retained. Which combination of features should they enable?

A company is running a Dataflow streaming pipeline that reads from Pub/Sub and writes to BigQuery. They notice that the number of workers is not scaling up to handle increased throughput, causing latency spikes. The pipeline uses a GlobalWindow with default triggering. What is the most likely cause of the under-scaling?

Refer to the exhibit. What is the cause of this error?

A company deploys a model to Vertex AI Endpoint. They want to run a canary deployment to test a new model version with 10% of traffic. How should they configure this?

Which BigQuery feature allows you to share query results with specific users without giving them direct access to the underlying tables?

A company stores data in a Cloud Storage bucket with versioning enabled. They want to automatically delete objects that are noncurrent (i.e., previous versions) after 30 days, and also delete the current version if it is older than 365 days. Which three Object Lifecycle Management conditions can be used together? (Choose three.)

You are building a machine learning pipeline for credit risk assessment. The dataset has a severe class imbalance (1% default rate). You want to use AutoML Tables on Vertex AI. Which strategy should you incorporate to handle imbalance?

A data engineer wants to automatically detect when the distribution of input features to a production model has shifted significantly. Which Vertex AI feature should they enable?

A data engineer needs to perform a one-time migration of 10 TB of data from on-premises Hadoop HDFS to Cloud Storage. The network link is 1 Gbps. Which TWO services or tools should they consider? (Choose 2)

You manage a team that deploys multiple versions of a computer vision model for A/B testing on Vertex AI Endpoints. You need to route a small percentage of traffic to a canary version while the rest goes to the stable version. You also need to gradually increase the canary traffic over time based on performance metrics. Which approach should you take?

A company has a Dataflow pipeline that reads from Pub/Sub, applies transformations, and writes to BigQuery. The pipeline is failing with 'deadline exceeded' errors during peak hours. The team suspects that the pipeline cannot keep up with the incoming data rate. They also notice that the autoscaling algorithm sets maxNumWorkers to 10, but the pipeline only scales to 5 workers. What is the most likely cause of the inadequate scaling?

A BigQuery table has a REQUIRED column 'user_id' that now needs to accept NULL values due to upstream data changes. You want to alter the schema with minimal downtime and no data loss. What should you do?

A company uses Workflows to orchestrate a multi-step data pipeline. One step calls an HTTP endpoint that may take up to 10 minutes, but the default Workflows timeout is too short. They also need to handle transient errors with retries. Which TWO configurations should they apply? (Choose 2)

A team has trained a scikit-learn model and wants to deploy it to AI Platform Prediction for online predictions. What is the required format for the model artifact?

A company is designing a data processing pipeline for real-time sensor data. They want to ensure low latency and exactly-once processing semantics. Which two Google services should they combine to achieve this? (Choose 2)

A Dataflow streaming pipeline reads from Pub/Sub, processes events with a fixed window of 1 minute, and writes to BigQuery. Some events arrive late due to network issues. You need to ensure late events are still included in the correct window but the pipeline must not wait indefinitely. What configuration should you use?

A data engineer is designing a batch ETL pipeline using Cloud Composer and Dataflow. The pipeline must be self-healing and retry on failures. Which Composer feature should they configure?

Match each BigQuery feature to its description.

A company uses Cloud Dataflow to process streaming data. They notice that the pipeline's throughput is lower than expected and the system is experiencing high latency. What is the most likely cause?

A team deployed a model to Vertex AI Endpoint and notices latency spikes during peak hours. What should they first investigate?

A data team needs to share a BigQuery dataset with another business unit. They want to provide a point-in-time snapshot of the data without incurring additional storage costs for the copy. Which BigQuery feature should they use?

Match each Google Cloud data service to its primary use case.

You train a BigQuery ML linear regression model to predict house prices. The model has high bias during evaluation. Which action BEST reduces bias?

Refer to the exhibit. A team uses this Cloud Build configuration to deploy a service to Cloud Run. The deployment step fails with a 'Permission denied' error. What is the most likely cause?

A healthcare analytics company runs a nightly Dataproc workflow that reads radiology reports from Cloud Storage (CSV files), transforms them using PySpark, and writes results to BigQuery. The workflow is orchestrated by Cloud Composer. Recently, the job has started failing with 'Disk quota exceeded' errors on the worker nodes. The data volume has grown 5x over the past month. Currently, the cluster uses 5 n1-standard-4 workers (each 10GB persistent disk). The PySpark jobs heavily use intermediate shuffles. You need a cost-effective solution that avoids future failures as data grows. What should you do?

A company uses Cloud Composer to orchestrate data pipelines. They have a DAG that runs hourly and processes files from Cloud Storage. The DAG is triggered by a Pub/Sub message sent from a Cloud Storage bucket notification. Recently, some DAG runs are not starting even though the Pub/Sub messages are published. Which two likely causes should the team investigate? (Choose TWO.)

A retail company uses BigQuery to store sales data and wants to forecast weekly demand for the next 8 weeks using historical data from the past 2 years. They need to account for seasonality and holidays. Which BigQuery ML model type and configuration is most appropriate?

Drag and drop the steps to set up a BigQuery dataset with a scheduled query into the correct order.

You need to create a Cloud Storage bucket for a data lake that will store raw ingested data. The data must be immutable and cannot be deleted or overwritten for a compliance period of 5 years. Which feature should you enable?

A data team uses Looker Studio to create a report that combines data from two different BigQuery tables: one with sales transactions and another with customer demographics. They need to join these tables in the report without writing SQL. Which feature should they use?

A company uses BigQuery for real-time analytics. They stream data from IoT devices into a BigQuery table. After a few hours, some of the recent data becomes visible in the table although it was streamed less than 10 minutes ago. The data team confirms that no one ran any manual queries. What is the most likely reason for the data visibility?

A data engineer is planning a time-series forecasting model using BigQuery ML ARIMA+ on a dataset with daily sales data spanning 3 years. Which TWO actions are required to prepare the data for ARIMA+? (Choose 2.)

Which TWO are best practices for monitoring a deployed machine learning model in production on Vertex AI?

Your company stores sensitive customer data in Cloud Storage. You need to inspect the data for personally identifiable information (PII) and de-identify it before sharing with a third party. Which Google Cloud service should you use?

Your team is using Vertex AI Pipelines to orchestrate a model retraining workflow. The pipeline includes a data validation step, a training step, and a model evaluation step. You want to ensure that if the evaluation step fails due to low model performance, the pipeline stops and does not deploy the model. Which approach should you use?

A company runs a batch data processing workload using Dataproc clusters that are auto-scaled based on YARN memory utilization. During peak times, jobs take much longer than expected. Analysis shows the cluster is not scaling up despite high YARN memory utilization. What is the most likely cause?

A company wants to use BigQuery to query data stored in Parquet files in Cloud Storage without loading the data into BigQuery. Which BigQuery feature should they use?

A data engineer needs to design a Bigtable row key for a time-series IoT application where each device sends data every second. The query pattern is to retrieve all data for a specific device over a time range. Which row key design minimizes hotspots?

A data warehouse team uses Cloud BigQuery for analytics. They want to optimize query performance and reduce costs. Which three actions should they take? (Choose 3)

Which TWO steps are required to deploy a custom scikit-learn model to Vertex AI for online predictions?

A data pipeline using Cloud Dataflow reads from a Pub/Sub subscription that has a dead letter topic configured. Some messages are being sent to the dead letter topic. Upon investigation, the engineer finds that the messages contain valid data but are malformed according to the schema. What is the most likely reason for the messages being dead-lettered?

A company is migrating their on-premises Hadoop/Spark workloads to Google Cloud. They need a fully managed service that supports existing Spark jobs with minimal code changes, allows autoscaling, and provides integration with Cloud Storage and BigQuery. The team also wants to avoid managing cluster infrastructure and pay only for what they use. Which TWO services meet these requirements? (Choose two.)

A company stores sensitive customer data in BigQuery and Cloud Storage. They want to encrypt the data with customer-managed encryption keys (CMEK) and ensure that access to the key material is restricted to only approved networks. Which additional Google Cloud control should they implement to enforce network-based access to the encryption keys?

A data scientist wants to automate retraining of a classification model when new labeled data arrives. The model is deployed on AI Platform Prediction. Which Google Cloud service should be used to orchestrate the retraining pipeline?

A company has a production machine learning model deployed on Vertex AI Endpoint that predicts customer churn. The model is retrained weekly using a Vertex AI Pipeline that pulls new data from BigQuery. Recently, the model's accuracy has been declining. The data science team suspects data drift but is unsure. They have enabled Vertex AI Model Monitoring but have not set up any alerts. The team wants to diagnose and address the issue quickly. The pipeline runs successfully, and no errors are reported. The model endpoint is serving predictions with average latency of 200ms. What should the team do first?

A financial services company deploys a fraud detection model on Vertex AI using a custom prediction container that runs a PyTorch model. The model requires GPU acceleration. The deployment succeeds but predictions return an error: 'CUDA error: out of memory'. What should the team do to resolve this issue?

Your company has a machine learning model that predicts customer churn. The model is deployed on Vertex AI Endpoints with autoscaling. After a marketing campaign, traffic to the endpoint increases by 10x. Some predictions start failing with 'HTTP 503 Service Unavailable' errors. What is the most likely cause?

A company runs a Dataflow pipeline that reads from Pub/Sub, aggregates events in a 10-minute fixed window, and writes to BigQuery. Recently, the pipeline has been failing with 'high uncommitted bytes' errors during periods of high traffic. What is the most likely cause and recommended action?

Your company ingests millions of events per second into a Pub/Sub topic. The downstream consumer must process events with minimal latency and high throughput. However, the consumer occasionally falls behind during traffic spikes, and you need to ensure no data loss while minimizing costs. Which subscription type and configuration should you choose?

A company is using Dataflow to stream data from Cloud Pub/Sub to BigQuery. The pipeline includes a custom ParDo transformation that enriches the data with external API calls. The pipeline is experiencing high latency and occasional failures due to API timeouts. What strategy should be employed to improve reliability and performance?

A user gets the above error when trying to get online predictions. The model was created and the endpoint exists. What is the most likely reason?

A data engineer is preparing a dataset for ML training in Vertex AI. The dataset includes a timestamp column, a categorical column with high cardinality (1000 distinct values), and a numerical column with outliers. Which two preprocessing steps should they apply? (Choose TWO)

Refer to the exhibit. A team is trying to run a custom prediction container on Vertex AI Endpoint. They get this error when the container starts. What is the most likely cause?

Your team is using Cloud Dataprep to clean and transform a dataset. Which TWO features of Cloud Dataprep help you understand data quality issues before running the pipeline? (Choose 2.)

A company wants to use Cloud Data Fusion to build ETL pipelines. They need to connect to a legacy on-premises database using JDBC and also want to use prebuilt transforms from the Hub. Which two features should they use?

Which Google Cloud service would you use to create a unified data catalog that automatically captures lineage from BigQuery, Cloud Storage, and other sources?

A company uses BigQuery flat-rate pricing with 500 slots purchased as a committed use discount. During peak hours, they need additional capacity but do not want to buy more committed slots. They have a secondary project used for ad-hoc queries by analysts. How can they provide burst capacity to the primary project during peak times without increasing committed spend?

A data engineer needs to run an existing Spark job on Google Cloud with minimal code changes. The job requires Hive metastore access. Which Dataproc feature should they use to provide a managed Hive metastore?

You are moving an on-premises Hadoop workload to Google Cloud. The workload uses Hive for metadata and HDFS for storage. Which services should you use to minimise reconfiguration?

Which TWO options can help reduce costs for a Dataflow batch pipeline that processes 100 GB of data daily from Cloud Storage? (Choose 2)

A data team wants to use Approximate Aggregation Functions in BigQuery to get faster query results. Which two functions can they use? (Choose 2)

A company uses a custom container image for model serving. The image is large (10 GB). During deployment, they get timeouts. What should they do?

A company is planning to migrate a legacy batch ETL pipeline to Google Cloud. The pipeline involves reading from a relational database, transforming data, and writing to a data warehouse. Which three Google Cloud services can be used as the orchestration layer? (Choose three.)

A data scientist needs to perform feature engineering for a machine learning model using Vertex AI. They want to preprocess data using a pipeline that includes scaling, one-hot encoding, and handling missing values. Which TWO services can they use to define and execute this preprocessing pipeline? (Choose 2.)

A company is building a real-time streaming pipeline using Pub/Sub and Dataflow to process clickstream data. The pipeline writes aggregated metrics to BigQuery every 10 seconds using a fixed window. During peak traffic, some windows produce duplicate rows in BigQuery. What is the most likely cause?