PDE Practice Test 15 — 15 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use Customer-Managed Encryption Keys (CMEK) and enable Cloud Audit Logs.. Customer-Managed Encryption Keys (CMEK) allow the team to control and manage the encryption keys used to protect data at rest in Cloud Storage and BigQuery, while enabling Cloud Audit Logs provides the necessary audit trail for access to both the data and the keys. This combination directly satisfies the requirements for encryption at rest and auditability.

Answer

Use Customer-Managed Encryption Keys (CMEK) and enable VPC Service Controls.

Answer

Use Default Encryption and enable Data Loss Prevention (DLP) API.

Answer

Use Customer-Supplied Encryption Keys (CSEK) and enable VPC Service Controls.

Question 2

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

Accepted Answer

If the new model passes evaluation, deploy it to a production endpoint.. Option A is correct because a continuous training pipeline must close the loop by promoting a model that passes evaluation to a production endpoint, typically via Vertex AI Model Registry and an Endpoint deployment, so the retrained model actually serves predictions. Option D is correct because continuous training requires an automated trigger, such as a Cloud Storage object-finalize event routed through Eventarc or Cloud Functions to launch the Vertex AI Pipeline when new training data lands. Option E is correct because the pipeline must include a model evaluation step that scores the new model against a held-out validation set (for example using Vertex AI Model Evaluation metrics) to gate promotion. Option B is not essential because manual approval breaks the continuous/automated nature of the pipeline, even if it can be added as an optional gate. Option C is incorrect because deploying the original model with auto-update is not a Vertex AI mechanism for retraining; continuous training is driven by pipelines and triggers, not by an endpoint auto-updating itself.

Answer

Manually approve each new model version before deployment.

Answer

Deploy the original model once and set it to auto-update.

Question 3

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?

Accepted Answer

Retrain the model with more recent data. Vertex AI Model Monitoring detected a drift in the 'age' feature, indicating that the production data distribution has shifted from the training data. Retraining the model with more recent data aligns the model with the current data distribution, mitigating the drift and maintaining prediction accuracy. This is the standard remediation for model drift in production ML systems.

Answer

Increase the drift threshold to 0.9 to suppress alerts

Answer

Deploy a new model version trained on the original dataset

Answer

Disable monitoring for the 'age' feature

Question 4

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?

Accepted Answer

Configure the Cloud SQL connector in Data Fusion to use retry logic and increase the connection timeout.. Cloud Data Fusion's Cloud SQL connector can be configured with retry logic and an increased connection timeout to handle transient network issues. This directly addresses the intermittent 'connection timeout' error without requiring architectural changes, as the error is likely due to brief network latency or resource contention, not a persistent connectivity problem.

Answer

Use Cloud NAT to provide a static IP for Data Fusion to whitelist.

Answer

Increase the number of Data Fusion nodes to distribute the load.

Answer

Migrate Cloud SQL to Cloud Spanner to handle higher concurrency.

Question 5

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?

Accepted Answer

Use fixed windows of 10 minutes with allowed lateness of 1 hour and a trigger that fires after watermark plus early firings. Fixed windows of 10 minutes match the required grouping interval, allowed lateness of 1 hour accommodates late-arriving data up to one hour, and a trigger that fires after the watermark plus early firings ensures results are emitted promptly while still accepting late data. This combination is the canonical Apache Beam pattern for windowed aggregation with late data on a streaming pipeline.

Answer

Use global windows with a trigger that fires every 10 minutes

Answer

Use sliding windows of 10 minutes with a 5-minute period and allowed lateness of 1 hour

Answer

Use fixed windows of 10 minutes with allowed lateness of 0 seconds

Question 6

A company is migrating an on-premises PostgreSQL database to Google Cloud. They need a fully managed database that is compatible with PostgreSQL and can handle both transactional and analytical workloads with high performance. Which two database services meet these requirements? (Choose TWO.)

Accepted Answer

Cloud SQL for PostgreSQL. Cloud SQL for PostgreSQL (B) is correct because it is a fully managed service that runs the native PostgreSQL engine, so it is directly compatible with the on-premises PostgreSQL database and supports transactional (OLTP) workloads. AlloyDB (D) is also correct because it is a fully managed, PostgreSQL-compatible database engine that is specifically designed for high-performance transactional workloads while also accelerating analytical queries through its columnar engine, making it well suited to mixed transactional and analytical use. Cloud Spanner (A) is not the right fit here because, although it is fully managed and highly scalable, it is not PostgreSQL-compatible in the native sense and is aimed at globally distributed OLTP rather than this migration scenario. BigQuery (C) is a fully managed analytical data warehouse optimized for OLAP, not a PostgreSQL-compatible transactional database. Firestore (E) is a fully managed NoSQL document database and is neither PostgreSQL-compatible nor designed for the transactional/analytical mix described.

Answer

Cloud Spanner

Answer

BigQuery

Answer

Firestore

Question 7

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?

Accepted Answer

Check Stackdriver logging for error messages.. Stackdriver (now Cloud Logging) is the first place to investigate when a Dataflow pipeline experiences high latency and data loss. Dataflow automatically logs errors, worker failures, and system messages to Cloud Logging, which can reveal root causes such as insufficient resources, stuck steps, or Pub/Sub subscription issues. Checking logs first avoids premature scaling or configuration changes that may not address the actual problem.

Answer

Disable exactly-once processing in Dataflow.

Answer

Increase the number of Dataflow workers.

Answer

Switch to BigQuery streaming inserts.

Question 8

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?

Accepted Answer

Eventarc. Eventarc is the correct choice because it is purpose-built to route events from Google Cloud sources (like Cloud Storage) to Cloud Run. It directly supports Cloud Storage audit logs and Pub/Sub event triggers, allowing you to react to object creation events without custom middleware. Eventarc handles the event routing, filtering, and delivery to your Cloud Run service automatically.

Answer

Pub/Sub directly with a Cloud Run subscription

Answer

Cloud Tasks

Answer

Cloud Scheduler

Question 9

Which TWO are best practices for monitoring a deployed machine learning model in production on Vertex AI?

Accepted Answer

Enable Vertex AI Model Monitoring to track feature drift and skew. Option B is correct because Vertex AI Model Monitoring is the purpose-built service for detecting training-serving skew and prediction drift on deployed models by comparing incoming request features against the training baseline, which is exactly the production monitoring best practice. Option D is correct because tracking the distribution of predictions over time is the standard way to detect concept drift, since a shift in the relationship between inputs and outputs shows up as changing output distributions even when input features look stable. Option A is not a monitoring best practice because a fixed calendar-schedule retraining pipeline is a maintenance strategy, not monitoring, and it can miss sudden drift between runs. Option C is wrong because training job duration is a build-time metric, not a runtime signal of model quality in production. Option E is wrong because model file size is a static artifact property and has no bearing on prediction quality or drift.

Answer

Set up a weekly retraining pipeline triggered by calendar schedule

Answer

Monitor the training job duration to detect anomalies

Answer

Monitor the model's file size to ensure it hasn't changed

Question 10

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?

Accepted Answer

BigLake tables over Cloud Storage. BigLake tables provide a unified governance layer over Cloud Storage data, enabling fine-grained access control such as column-level security and row-level filtering directly on BigQuery queries. This is achieved by integrating BigQuery's access control policies with the external data stored in GCS, without needing to move data into BigQuery native storage. The other options either lack these granular security features or require complex workarounds.

Answer

BigQuery external tables reading from GCS

Answer

Dataproc with Spark SQL

Answer

Cloud Storage with IAM and VPC Service Controls

Question 11

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

Accepted Answer

Inefficient GroupByKey with hot keys. Option B is correct because an inefficient GroupByKey with hot keys forces a single worker to hold all values for a given key in memory during shuffling, causing OutOfMemoryError; using Combine.perKey or pre-aggregation mitigates this. Option C is correct because each side input is loaded into memory on every worker, so too many (or too large) side inputs exhaust worker memory. Option D is correct because in streaming mode, large window accumulation retains all elements for the window in memory until the window fires, and oversized or long windows can exceed worker memory. Option A is not a cause—more parallel workers distribute load and reduce per-worker memory pressure. Option E is not a cause—Dataflow Shuffle offloads shuffle data to a remote service, reducing worker memory usage rather than causing OOM.

Answer

Too many parallel workers

Answer

Using Dataflow Shuffle

Question 12

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

Accepted Answer

Create a Pub/Sub topic, then create a push subscription with the HTTPS endpoint URL. The correct order to set up a Pub/Sub topic with a push subscription to an HTTPS endpoint is to first create the topic, then create a push subscription specifying the HTTPS endpoint URL. This ensures the topic exists before the subscription references it, and the push endpoint is configured at subscription creation time. Common mistakes include creating the subscription before the topic, omitting the endpoint, or attempting to convert a pull subscription to push, which is not supported.

Answer

Create a push subscription without an endpoint, then create the topic, then update the subscription with the endpoint

Answer

Configure the HTTPS endpoint to accept messages, then create the topic, then create the subscription

Answer

Create a topic, then create a pull subscription, then change it to a push subscription

Question 13

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?

Accepted Answer

Set retries=2 on the Dataflow operator. Setting retries=2 on the Dataflow operator instructs Airflow to automatically restart only that specific task upon failure, without affecting upstream tasks (MongoDB extraction, BigQuery load). This isolates the retry to the Dataflow job, preserving the earlier completed work and avoiding redundant data movement.

Answer

Implement a SlaMiss sensor

Answer

Use a DAG with depends_on_past=True

Answer

Set trigger_rule='one_success' for downstream tasks

Question 14

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)

Accepted Answer

Cloud Storage. Cloud Storage (A) is correct because the scenario explicitly requires ingesting on-premises data into Cloud Storage as the landing/staging layer before processing. Dataproc (B) is correct because the pipeline is specified to process the data with Dataproc, Google Cloud's managed Hadoop/Spark service. Cloud Composer (C) is correct because it is the managed Apache Airflow service that provides daily scheduling, dependency orchestration, and retry/alerting logic so occasional failures are handled gracefully. Dataflow (D) is not needed since the processing engine in this pipeline is Dataproc, not a Beam-based Dataflow job. Pub/Sub (E) is not required because the scenario describes batch ingestion into Cloud Storage rather than real-time streaming messaging.

Answer

Dataflow

Answer

Pub/Sub

Question 15

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.)

Accepted Answer

Column histograms. Option B, column histograms, is correct because Cloud Dataprep generates interactive histograms for each column that visually reveal value distributions, outliers, nulls, and anomalies, letting you spot data quality issues before executing a pipeline. Option E, data quality profiling, is correct because Cloud Dataprep's profiling feature automatically scans the dataset and reports metrics such as missing values, distinct counts, mismatched types, and invalid values, which is exactly the pre-pipeline assessment of data quality described. Option A, scheduling data quality jobs, is not correct because scheduling controls when transformation jobs run, not how you inspect data quality beforehand. Option C, joining datasets, is not correct because joins are transformation operations that combine data rather than diagnose quality issues. Option D, recipe steps, is not correct because recipe steps define the transformations to apply, not the profiling or histogram analysis used to understand data quality first.

Answer

Scheduling data quality jobs

Answer

Joining datasets

Answer

Recipe steps