PDE Practice Test 43 — 15 Questions

Question 1

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

Accepted Answer

Monitor prediction accuracy in production with logging and alerts. Option B is correct because monitoring prediction accuracy in production via request/response logging and Cloud Monitoring alerts is essential to detect model drift, data skew, and degradation, allowing timely retraining or rollback to maintain reliability. Option E is correct because gradually shifting traffic to new model versions using canary deployment on a Vertex AI Endpoint lets you validate the new model against live traffic and roll back quickly if metrics degrade, minimizing risk. Option A is not appropriate because batch predictions cannot serve real-time production traffic and do not by themselves ensure reliability. Option C is wrong because disabling request/response logging removes the observability needed to detect accuracy issues and violates reliability monitoring. Option D is wrong because routing all traffic to a single model endpoint without versioning or traffic splitting prevents safe canary testing and rollback.

Answer

Use only batch predictions to avoid real-time issues

Answer

Disable request/response logging to reduce latency

Answer

Use a single model endpoint for all traffic

Question 2

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?

Accepted Answer

Partition by order_date and cluster by customer_id. Partitioning by order_date and clustering by customer_id aligns the physical storage with the query access pattern: partition pruning eliminates irrelevant date ranges, and clustering sorts data within each partition by customer_id so filter and aggregation operations scan fewer blocks. This combination minimizes bytes scanned, which directly reduces both query latency and cost in BigQuery's on-demand pricing model.

Answer

Cluster by order_date and partition by customer_id

Answer

Partition by ingestion_time and cluster by order_date

Answer

Use a clustered table without partitioning

Question 3

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

Accepted Answer

Choosing the appropriate machine type (e.g., n1-standard-16) balances cost and throughput. Option C is correct because the machine type selected for a Vertex AI batch prediction job (for example n1-standard-16) directly determines the CPU, memory, and accelerator resources available, so it must be sized to balance cost against throughput for a large dataset. Option D is correct because Vertex AI batch prediction shards input across worker nodes, and splitting very large input files into multiple smaller files (e.g., many shards in Cloud Storage) increases parallelism and reduces per-file processing bottlenecks. Option E is correct because batch prediction reads input from Cloud Storage and requires a supported format such as JSONL, CSV, or TFRecord with the appropriate instance keys, so the data must be staged there in a compatible layout. Option A is not correct because batch prediction does not automatically attach GPUs based on the model framework; you must explicitly configure accelerators, and many frameworks run fine on CPU. Option B is not correct because batch prediction is an offline, asynchronous job that does not require or use a dedicated real-time endpoint, which is only needed for online prediction.

Answer

Batch prediction automatically uses GPUs if the model framework requires them

Answer

Batch prediction requires a dedicated real-time endpoint

Question 4

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?

Accepted Answer

Enable Vertex AI Model Monitoring for the endpoint and configure alerting on feature drift. Vertex AI Model Monitoring is purpose-built to automatically detect feature drift and prediction drift on deployed endpoints. By enabling it and configuring alerting on feature drift, you can proactively identify when the distribution of incoming features deviates from the training data, which directly addresses the root cause of accuracy degradation without manual intervention.

Answer

Set up alerts for when the model's mean absolute error exceeds a threshold on the evaluation dataset

Answer

Enable Cloud Logging for the prediction endpoint and search for error logs

Answer

Schedule a job to compare the distribution of incoming features with the training data using Cloud Dataflow

Question 5

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?

Accepted Answer

Cloud Dataflow. Cloud Dataflow is the correct choice because it provides a unified stream and batch processing model with exactly-once processing guarantees and sub-second latency via its Apache Beam SDK. It supports event-time processing, watermarks, and triggers to handle out-of-order data from IoT devices while ensuring each record is processed exactly once, even in the case of failures.

Answer

BigQuery

Answer

Cloud Dataproc

Answer

Cloud Pub/Sub

Question 6

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?

Accepted Answer

DataFrames or Datasets. DataFrames and Datasets are built on Spark SQL's Catalyst optimizer and Tungsten execution engine, which provide schema-aware encoding and off-heap memory management. This dramatically reduces serialization overhead compared to RDDs and enables optimizations like predicate pushdown and shuffle partitioning improvements, directly addressing excessive shuffling.

Answer

Spark SQL

Answer

Spark Streaming

Answer

RDDs

Question 7

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?

Accepted Answer

Use CREATE MODEL with model_type='tensorflow' and model_path='gs://bucket/model'.. BigQuery ML supports importing TensorFlow models via CREATE MODEL with model_type='tensorflow' and a model_path pointing to a Cloud Storage location containing the SavedModel. This allows batch prediction using ML.PREDICT directly in BigQuery without moving data to Vertex AI.

Answer

Use CREATE MODEL with model_type='imported_tensorflow' and model_path='gs://bucket/model'.

Answer

First upload the model to Vertex AI Model Registry, then reference it in BigQuery ML.

Answer

Use the ML.IMPORT_MODEL function to load the model into BigQuery.

Question 8

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?

Accepted Answer

Use retries with exponential backoff. Cloud Composer (Apache Airflow) tasks can fail due to transient issues like API rate limits or resource contention. Implementing retries with exponential backoff allows the DAG to automatically re-attempt failed tasks with increasing delays, reducing the impact of intermittent failures without manual intervention. This is a standard Airflow pattern for improving reliability in orchestrated pipelines.

Answer

Switch to Cloud Functions for orchestration

Answer

Increase worker count in Dataflow

Answer

Use a simpler DAG with fewer dependencies

Question 9

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?

Accepted Answer

Use Pub/Sub, then a Dataflow pipeline that filters and transforms data, writing to Cloud Bigtable for real-time queries and to Cloud Storage for periodic BigQuery loads.. It uses Cloud Bigtable for sub-second latency on recent data, which is ideal for near real-time analytics on streaming IoT data. Dataflow provides the necessary stream processing, filtering, and transformation before writing to Bigtable for low-latency queries and to Cloud Storage for periodic batch loads into BigQuery for historical analysis. This architecture decouples real-time and historical paths, meeting both latency and storage requirements.

Answer

Use Pub/Sub to receive data, then stream directly into BigQuery using the streaming API, and use standard SQL queries for real-time analytics.

Answer

Use Pub/Sub to ingest data into a Dataproc Spark Streaming job that writes to both Bigtable and BigQuery.

Answer

Use Cloud SQL to store the latest data and periodically move historical data to BigQuery via cron jobs.

Question 10

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?

Accepted Answer

Cloud Bigtable. Cloud Bigtable is a fully managed, scalable NoSQL database designed for high-throughput, low-latency time-series data. It supports single-row key lookups and range scans, making it ideal for retrieving all readings for a specific device over a time range (e.g., last 24 hours) from millions of IoT devices generating 2 TB/month. Its row key design (e.g., device_id + timestamp) enables efficient time-range queries without full table scans, unlike object storage or analytical warehouses.

Answer

Cloud Storage (objects per device per time interval)

Answer

BigQuery

Answer

Cloud Spanner

Question 11

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?

Accepted Answer

Use Data Fusion Wrangler to visually design the transformations and then run the pipeline on a Dataproc cluster.. Cloud Data Fusion Wrangler provides a visual, no-code interface for designing transformations (join, filter, aggregate) that are then compiled into an Apache Spark or MapReduce program and executed on a Dataproc cluster. This approach leverages Data Fusion's native integration with Dataproc for efficient, scalable batch processing without manual coding, while keeping the pipeline fully managed within the Data Fusion ecosystem.

Answer

Use SQL queries in BigQuery to perform the transformations after loading raw data into staging tables.

Answer

Use custom Python scripts in a Cloud Function triggered after the files land in Cloud Storage.

Answer

Use Apache Spark on Dataproc to code the transformations manually, bypassing Data Fusion.

Question 12

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?

Accepted Answer

Deploy the new model to the same endpoint and set traffic split. Vertex AI Endpoints natively support traffic splitting between model versions deployed to the same endpoint. By deploying the new model version to the same endpoint and setting a traffic split of 10% to the new version and 90% to the current version, the company can perform a canary deployment without changing the application code or infrastructure.

Answer

Deploy to a new endpoint and update the application to call both

Answer

Use Cloud Load Balancing to route traffic

Answer

Deploy to Cloud Run and use gradual rollout

Question 13

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?

Accepted Answer

The user-provided record ID for deduplication in BigQuery's streaming inserts is not being set for all messages, leading to duplicate rows.. In Dataflow streaming pipelines with exactly-once semantics, BigQuery's streaming inserts use user-provided record IDs for deduplication. If the record ID is not set for all messages, BigQuery cannot identify duplicates, and retries or redeliveries from Pub/Sub can result in duplicate rows. This is the most common cause of duplicates in this scenario.

Answer

The pipeline is using the WriteResult method with WRITE_APPEND in batch mode, which can cause duplicates if retries happen.

Answer

The pipeline is experiencing the 'dataflow streaming log processing' bug, causing duplicate logs to be written.

Answer

The PubSubIO source is configured with a dead-letter queue and messages are being redelivered without proper deduplication.

Question 14

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?

Accepted Answer

Dataflow streaming with exactly-once processing. Dataflow streaming with exactly-once processing is the correct choice because it provides exactly-once semantics for Pub/Sub sources via checkpointing and idempotent sinks, and it meets the low-latency (seconds) requirement through its streaming engine that minimizes per-element overhead. Cloud Dataflow's integration with Pub/Sub ensures that each message is processed exactly once, even in the presence of failures, by using snapshots and consistent state management.

Answer

Cloud Run with Pub/Sub push

Answer

Cloud Functions triggered by Pub/Sub

Answer

Dataproc with Spark Streaming

Question 15

Refer to the exhibit. A BigQuery dataset has the IAM policy shown above. An analyst is trying to run a SELECT query on a table in this dataset but receives an 'Access Denied' error. What is the most likely reason?

Accepted Answer

The analyst only has the roles/bigquery.metadataviewer role, which does not allow reading table data.. The roles/bigquery.metadataviewer role grants permissions to view table and dataset metadata (e.g., table names, schemas) but does not include the bigquery.tables.getData permission required to read table rows. Therefore, when the analyst runs a SELECT query, BigQuery denies access because the role lacks the data-reading privilege. This is the most likely reason for the 'Access Denied' error.

Answer

The analyst does not have permission to list datasets in the project.

Answer

The table is in a different region than the dataset, and the analyst's query is not cross-region compatible.

Answer

The analyst has not been granted the 'bigquery.jobs.create' permission to run queries.