PDE Practice Test 44 — 15 Questions

Question 1

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?

Accepted Answer

Insufficient memory per worker causing OOM errors. The 'worker terminated unexpectedly' error in Cloud Dataflow typically indicates that a worker process ran out of memory (OOM) and was killed by the operating system. This occurs when the pipeline's memory requirements exceed the configured worker machine type's memory capacity, often due to large windowing accumulations, skewed data, or inefficient state handling.

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Incorrect VPC firewall rules blocking internal communication

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Staging location bucket lacks write permissions

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Pub/Sub subscription throughput quota exceeded

Question 2

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?

Accepted Answer

Use Vertex AI Endpoints with automatic scaling enabled. Vertex AI Endpoints with automatic scaling enabled is the recommended approach because it directly supports deploying trained models (including XGBoost) as online prediction endpoints with built-in autoscaling based on incoming traffic. This service manages the underlying infrastructure, load balancing, and scaling policies, aligning with the requirement for automatic scaling without additional containerization or serverless overhead.

Answer

Export the model to a container and deploy on Cloud Run

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Use AI Platform Prediction with batch prediction

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Deploy the model as an API on App Engine

Question 3

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?

Accepted Answer

Vertex AI Explainable AI. Vertex AI Explainable AI is the correct feature because it provides feature attributions and explanations for each prediction, enabling compliance with regulatory requirements that demand interpretability. It uses techniques like Shapley value approximations or integrated gradients to quantify the contribution of each input feature to the model's output, which is essential for auditing and transparency in deployed autoML models.

Answer

Vertex AI Model Monitoring

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Vertex AI Vizier

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Vertex AI Feature Store

Question 4

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?

Accepted Answer

Use the Vertex AI Endpoint 'traffic_split' parameter to assign 10% of traffic to v2 and 90% to v1.. The Vertex AI Endpoint 'traffic_split' parameter allows you to direct a percentage of inference requests to different model versions deployed on the same endpoint. Setting 10% to v2 and 90% to v1 enables a gradual rollout while monitoring skew and drift, and you can adjust the split as needed. This is the native, supported method for canary deployments in Vertex AI, avoiding the complexity and latency of external load balancers.

Answer

Deploy v2 to a separate endpoint and use a load balancer to route 10% of traffic.

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Create a new deployment with v2 on the same endpoint and set the 'min_replica_count' to 1 for both versions.

Answer

Enable Vertex AI Model Monitoring on the endpoint and set up alerting for performance drop.

Question 5

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?

Accepted Answer

Cloud Bigtable. Cloud Bigtable is a fully managed, scalable NoSQL database designed for large analytical and operational workloads, handling petabytes of data with consistent single-digit millisecond latency for high-throughput reads and writes. Its key-value model with timestamp-based versioning is ideal for time-series IoT sensor data, and it supports millions of reads per second via its HBase API and Bigtable's underlying tablet-based architecture.

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Firestore

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Cloud Spanner

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BigQuery

Question 6

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?

Accepted Answer

Use Datastream to backfill historical data and then stream CDC changes to BigQuery. Datastream is correct because it provides serverless change data capture (CDC) from MySQL to BigQuery, supporting both historical backfill and continuous replication of inserts, updates, and deletes with minimal operational overhead. It reads the MySQL binary log (binlog) to stream changes in near real-time and can write directly to BigQuery or Cloud Storage.

Answer

Use a Dataflow pipeline with a JDBC source to read the entire table periodically

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Use a one-time export to CSV and load into BigQuery, then set up a cron job to export incremental changes

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Use Cloud SQL as an intermediary and enable binary logging, then stream to Pub/Sub via a custom connector

Question 7

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?

Accepted Answer

DataflowRunner. DataflowRunner is the correct choice because it is the fully managed service runner for Apache Beam on Google Cloud, optimized for batch and streaming pipelines. It automatically handles scaling, resource management, and exactly-once processing semantics, which are essential for reliable daily runs and backfills with Avro files from Cloud Storage and BigQuery sinks.

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FlinkRunner

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SparkRunner

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DirectRunner

Question 8

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?

Accepted Answer

Fixed windows of 1 hour with allowed lateness of 2 hours and trigger every 5 minutes (early) and on watermark (late) with accumulating fired panes. Fixed windows of 1 hour with allowed lateness of 2 hours and triggers every 5 minutes (early) and on watermark (late) with accumulating fired panes is the correct configuration. This setup computes hourly aggregations, emits early results every 5 minutes, and updates results when late data arrives within the 2-hour allowed lateness. Accumulating panes ensure that late data updates the previous results. This minimizes cost by using fixed windows and appropriate triggers.

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Global window with triggers every 5 minutes

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Sliding windows of 1 hour with 30-minute offset

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Session windows with 10-minute gap duration

Question 9

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.)

Accepted Answer

Configure object lifecycle management to transition files older than 90 days to Nearline storage.. Option B is correct because configuring Object Lifecycle Management to transition objects older than 90 days to Nearline storage lowers storage costs for infrequently accessed historical patient records while keeping them queryable, since Nearline has a 30-day minimum storage duration and is cheaper than Standard. Option D is correct because BigLake external tables let BigQuery query JSON files directly in Cloud Storage without ingestion, and BigLake supports fine-grained security such as row-level security and access delegation (via BigQuery delegated access to the underlying Cloud Storage objects), satisfying the data security requirement. Option A is not required: partitioning by date in separate directories is a performance/cost optimization for query pruning, but it does not by itself minimize storage cost or provide security, and BigLake can query the data without this layout. Option C is not appropriate: converting JSON to CSV does not reliably reduce storage size and loses the nested schema, and it is not needed for BigQuery querying. Option E is not the best fit: Cloud KMS with CMEK encrypts data at rest but does not enable BigQuery querying or reduce storage costs, and Cloud Storage is already encrypted by default with Google-managed keys.

Answer

Partition the data by date and store in separate directories for each partition.

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Convert all JSON files to CSV to reduce storage size.

Answer

Enable Cloud KMS to encrypt the data with customer-managed encryption keys.

Question 10

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)

Accepted Answer

Set a lifecycle rule to change storage class from Standard to Nearline after 30 days for the 'raw' bucket.. Option A is correct because a lifecycle rule that transitions objects in the 'raw' bucket from Standard to Nearline after 30 days is a valid cost-reduction strategy for raw data that is accessed infrequently but may still be needed; Nearline is designed for data accessed less than once a month. Option D is correct because setting a lifecycle rule to delete objects older than 365 days in the 'curated' and 'processed' buckets removes stale data that is no longer needed, directly reducing storage costs. Option B is incorrect because enabling object versioning does not automatically delete older versions; versioning preserves them and typically increases storage costs unless combined with a separate lifecycle deletion rule. Option C is incorrect because partition expiration applies to BigQuery table partitions, not to objects in a Cloud Storage bucket, and the scenario is about Cloud Storage lifecycle management. Option E is incorrect because transitioning raw data to Archive after only 30 days is overly aggressive and would incur early-deletion charges and retrieval costs if the data is still needed, making it a poor fit compared with Nearline.

Answer

Enable object versioning on all buckets to automatically delete older versions.

Answer

Set a partition expiration on BigQuery tables that reference data in the 'processed' bucket.

Answer

Set a lifecycle rule to change storage class from Standard to Archive after 30 days for the 'raw' bucket.

Question 11

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?

Accepted Answer

Pull subscription with flow control settings. A pull subscription with flow control settings allows the consumer to control the rate of message delivery, preventing overload during traffic spikes. Pull subscriptions are ideal for high-throughput, low-latency processing because the consumer can batch and process messages at its own pace. Flow control settings (e.g., max outstanding messages, max bytes) help avoid overwhelming the consumer, ensuring no data loss while minimizing costs.

Answer

Push subscription with a load balancer

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Push subscription with endpoint on Cloud Run

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Pull subscription with exactly-once delivery disabled

Question 12

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)

Accepted Answer

Vertex AI Model Registry. Vertex AI Model Registry (A) is correct because it provides a central repository to manage the lifecycle of ML models, including versioning, tracking lineage, and deploying models to endpoints, which directly satisfies the requirement to register models. Vertex AI Dataset (B) is correct because it lets the engineer create managed, versioned datasets from tabular, image, text, or video data, fulfilling the need to store and version datasets. Vertex AI Experiments (E) is correct because it records and compares experiment runs, including parameters, metrics, and artifacts, which satisfies the requirement to track experiments. Vertex AI Feature Store (C) is not appropriate here because it is designed for serving and sharing online/offline feature values, not for dataset versioning, experiment tracking, or model registration. Vertex AI Matching Engine (D) is also not appropriate because it is a vector similarity search service for embeddings, unrelated to the requested dataset, experiment, and model management tasks.

Answer

Vertex AI Feature Store

Answer

Vertex AI Matching Engine

Question 13

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)

Accepted Answer

Dataflow Flex Template. Option B (Dataflow Flex Template) is correct because Flex Templates package the pipeline as a Docker image plus a template spec file, allowing the same pipeline to be reused and launched with different runtime parameters (such as input/output locations) via the templates launch API. Option C (Cloud Composer with DataflowStartFlexTemplateOperator) is correct because this operator is purpose-built to submit a Flex Template job from an Airflow/Composer DAG, passing the required parameters and letting Composer orchestrate the pipeline execution. Option A (Direct runner) is incorrect because it runs the pipeline locally for testing rather than deploying a reusable job on Dataflow. Option D (Dataflow Classic Template) is not the best fit here since Classic Templates are less flexible for parameterization and dependency packaging compared with Flex Templates. Option E (Cloud Scheduler) is incorrect because it only triggers jobs on a schedule and does not deploy or parameterize a Dataflow pipeline.

Answer

Direct runner

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Dataflow Classic Template

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Cloud Scheduler

Question 14

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?

Accepted Answer

Create the table with partitioning and clustering, then create an authorized view on the table and grant the view access to the domain users. Authorized views allow sharing query results with specific users/groups without giving direct table access. Clustering and partitioning are defined at table creation. IAM roles at dataset level are too broad. Row-level security filters rows but doesn't restrict domain.

Answer

Create the table with partitioning only, then use a materialized view to restrict access

Answer

Create the table without clustering, use row-level security to filter by domain, and grant access to the table

Answer

Create the table with partitioning and clustering, then grant bigquery.dataViewer to the domain via IAM at the dataset level

Question 15

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?

Accepted Answer

roles/bigquery.dataEditor. The error indicates the service account lacks the `bigquery.tables.get` permission, which is required to read table metadata. `roles/bigquery.dataEditor` includes this permission along with `bigquery.tables.get`, `bigquery.tables.update`, and `bigquery.tables.export`, making it the minimal role that resolves the access denied error for a Dataflow job reading from a BigQuery table.

Answer

roles/bigquery.admin

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roles/bigquery.user

Answer

roles/bigquery.jobUser