PDE Practice Exam 51 — 20 Questions

Question 1

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?

Accepted Answer

Configure a lifecycle rule with SetStorageClass to Nearline after 30 days and Delete after 365 days.. Cloud Storage lifecycle management rules allow you to automatically transition objects to a lower-cost storage class (such as Nearline) after a specified number of days and then delete them after another period. This is the native, serverless way to manage object lifecycle without external scripts or compute resources.

Answer

Use Cloud Pub/Sub notifications to trigger a Cloud Function that moves objects.

Answer

Use gsutil rewrite command in a cron job.

Answer

Set a bucket retention policy with a retention period of 365 days.

Question 2

A team deployed a model to Vertex AI Endpoint and notices latency spikes during peak hours. What should they first investigate?

Accepted Answer

Check if autoscaling is enabled and configured correctly. Latency spikes during peak hours typically indicate that the serving infrastructure is unable to handle the increased request volume. The first step is to check if autoscaling is enabled and configured correctly on the Vertex AI Endpoint, as this determines whether additional compute nodes are automatically provisioned to match demand. Without proper autoscaling, the endpoint will be overwhelmed, leading to queuing delays and latency spikes.

Answer

Switch to batch prediction

Answer

Reduce number of features

Answer

Increase machine type

Question 3

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.)

Accepted Answer

Change the worker machine type to a higher CPU/memory configuration. Increasing the number of workers allows the pipeline to process more data in parallel. Using streaming engine can improve throughput and reduce latency by offloading state management. Adjusting the worker machine type to use more CPU/memory can help if the processing is compute-intensive. Adding more subscriptions would not help because the pipeline reads from a single subscription. Changing the window size affects business logic but not necessarily performance. Combining these three optimizations addresses common bottlenecks.

Answer

Decrease the window duration to reduce data per window

Answer

Add additional Pub/Sub subscriptions to the same topic

Question 4

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)

Accepted Answer

Use a dead letter sink to write malformed messages to Cloud Storage or Pub/Sub for later analysis. A dead letter sink (e.g., writing malformed messages to Cloud Storage or a separate Pub/Sub topic) allows the pipeline to continue processing valid data while preserving the problematic records for offline inspection, retries, or debugging. This pattern is a standard best practice in streaming pipelines to avoid data loss and enable recovery without blocking the main data flow.

Answer

Configure the pipeline to drop malformed messages silently

Answer

Use a side input to filter out malformed messages

Answer

Raise an exception in the DoFn to fail the pipeline immediately

Question 5

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. Selecting the appropriate machine type, such as n1-standard-16, directly impacts the cost-performance trade-off in batch prediction. Vertex AI batch prediction jobs run on Compute Engine instances, and choosing a machine type with more vCPUs and memory can increase throughput for large datasets, but also raises cost. The key is to match the machine type to the model's computational needs and the data volume, avoiding over-provisioning while ensuring the job completes within acceptable time.

Answer

Batch prediction automatically uses GPUs if the model framework requires them

Answer

Batch prediction requires a dedicated real-time endpoint

Question 6

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?

Accepted Answer

Use the gcloud dataflow jobs update command with the new Flex Template.. The `gcloud dataflow jobs update` command allows you to update a running streaming Dataflow pipeline with a new Flex Template without draining or stopping the job. This command performs an in-place update, preserving the job's state and checkpointing, so the pipeline continues processing with the new code. Since the original job was started from a template, using this command with the new Flex Template ensures the updated code is picked up seamlessly.

Answer

Stop the pipeline, update the template, and restart with the same job name.

Answer

Modify the original template and redeploy it as a new job with the same pipeline name.

Answer

Use the gcloud dataflow jobs drain command, then restart with the new template.

Question 7

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.)

Accepted Answer

BigQuery. BigQuery is correct because it can directly query raw data stored in Cloud Storage in formats like CSV, Parquet, and Avro using external tables or federated queries, without requiring data loading. This provides a flexible, serverless analytics layer that scales automatically and integrates with downstream processing frameworks like Apache Spark, Dataflow, and Dataproc.

Answer

Cloud Filestore

Answer

Cloud Spanner

Answer

Cloud Bigtable

Question 8

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)

Accepted Answer

Configure a Cloud Storage object lifecycle rule with a Delete action. Cloud Storage object lifecycle management allows you to set rules that automatically delete objects after a specified age. By configuring a lifecycle rule with a Delete action and setting the condition to 'Age: 365 days', objects in the 'raw' zone will be automatically removed after 1 year, meeting the retention requirement without manual intervention.

Answer

Use a bucket retention policy with a retention period of 1 year

Answer

Set an IAM policy to prevent deletion of objects

Answer

Create a Cloud Function to check object age and delete them

Question 9

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?

Accepted Answer

Eventarc with Cloud Storage trigger and Cloud Run destination. Eventarc is the recommended service for routing events from Cloud Storage to Cloud Run because it provides a fully managed, event-driven architecture with built-in filtering and retry logic. When a new file is uploaded, Cloud Storage emits a notification that Eventarc captures and delivers directly to the Cloud Run service as an HTTP request, enabling serverless processing without polling or additional infrastructure.

Answer

Cloud Scheduler to periodically poll the bucket

Answer

Cloud Functions triggered by Cloud Storage

Answer

Pub/Sub with a push subscription to Cloud Run

Question 10

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?

Accepted Answer

The model is experiencing concept drift.. Concept drift occurs when the underlying relationship between input features and the target variable changes over time, causing the model's predictions to become less accurate even if the input data distribution remains stable. In this scenario, the prediction distribution has changed significantly, which is a hallmark of concept drift, as the model's learned decision boundary no longer reflects the current real-world patterns. Vertex AI's monitoring dashboard can track prediction distribution shifts, and this symptom points to concept drift rather than data drift.

Answer

The model is suffering from overfitting to the training data.

Answer

There is a bug in the model's preprocessing code.

Answer

There is data drift in the input features.

Question 11

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?

Accepted Answer

Use Vertex AI Batch Prediction with a scheduled pipeline. Vertex AI Batch Prediction is the simplest approach because it is a managed service that directly supports batch predictions on AutoML Tables models without requiring additional infrastructure. By wrapping it in a scheduled Vertex AI pipeline, you can automate the entire workflow—triggering predictions on a schedule, handling input/output to Cloud Storage, and managing compute resources—all within the Vertex AI ecosystem, minimizing operational overhead.

Answer

Write a Cloud Function triggered by Cloud Scheduler

Answer

Export model to Cloud Storage and use Dataflow

Answer

Deploy to App Engine

Question 12

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?

Accepted Answer

Cloud DLP. Cloud DLP (Data Loss Prevention) is the correct choice because it is purpose-built for inspecting, classifying, and de-identifying sensitive data such as PII. It integrates natively with BigQuery via inspection jobs and de-identification templates, allowing you to scan tables for over 150 built-in infoTypes (e.g., email, SSN) and apply transformations like masking, tokenization, or encryption before sharing data with analysts.

Answer

Dataplex

Answer

Cloud Data Catalog

Answer

Dataflow

Question 13

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?

Accepted Answer

Create a reservation with 500 baseline slots and enable autoscaling up to 1000 slots.. BigQuery reservations support baseline slots with autoscaling, which allows you to set a committed baseline of 500 slots and automatically scale up to 1000 slots during peak demand. You are billed only for the additional slots used beyond the baseline, which matches the requirement of paying only for slots used beyond the baseline.

Answer

Create a reservation with 500 committed slots and another with 500 flex slots.

Answer

Use on-demand pricing with a maximum query cost limit.

Answer

Purchase 1000 committed use slots to ensure consistent capacity.

Question 14

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

Accepted Answer

Integrated monitoring for prediction latency and error rates.. Vertex AI Endpoints provide integrated monitoring for prediction latency and error rates out of the box, enabling you to track model performance and detect anomalies without additional instrumentation. This is a core operational feature that helps maintain service-level objectives (SLOs) and quickly identify degradation in production.

Answer

Batch prediction support out of the box.

Answer

Automatic model retraining when drift is detected.

Answer

Built-in support for A/B testing without any additional configuration.

Question 15

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?

Accepted Answer

AlloyDB. AlloyDB is the correct choice because it is a fully managed PostgreSQL-compatible database service specifically designed for demanding transactional and analytical workloads. It combines the PostgreSQL ecosystem with a columnar engine and adaptive caching to accelerate analytical queries by up to 100x over standard PostgreSQL, all without requiring application changes. This makes it ideal for mixed OLTP and complex analytical queries while maintaining PostgreSQL compatibility.

Answer

BigQuery

Answer

Cloud Spanner

Answer

Cloud SQL for PostgreSQL

Question 16

Match each Google Cloud data service to its primary use case.

Accepted Answer

Cloud SQL: Fully managed relational database for MySQL, PostgreSQL, and SQL Server. The correct matches are: Cloud SQL for standard relational databases, Cloud Spanner for globally distributed relational data with strong consistency, Bigtable for analytical NoSQL workloads, Firestore for mobile/web apps with real-time sync, and BigQuery for large-scale analytics. Common confusions include mixing up Cloud SQL and Cloud Spanner due to both being relational, or Bigtable and Firestore due to both being NoSQL.

Answer

Cloud SQL: Fully managed relational database but not recommended for global distribution

Question 17

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?

Accepted Answer

The DAG has max_active_runs_per_dag set to a low number, causing overlapping runs to be rejected.. The 'DagRun already exists' error occurs when Airflow attempts to create a new DAG run for a logical date that already has an active or completed run, and the DAG's concurrency settings prevent overlapping runs. Setting max_active_runs_per_dag to a low number (e.g., 1) restricts the number of concurrent runs, so if a previous run hasn't finished or been cleared, a new run for the same or overlapping schedule interval is rejected with this error. This is the most likely cause given the DAG has dependencies spanning multiple days, which can cause runs to overlap if not properly configured.

Answer

The DAG has a large number of tasks, overwhelming the Airflow scheduler.

Answer

The DAG's schedule interval is too short, causing task instances to be created with duplicate run IDs.

Answer

The DAG has a depends_on_past set to True, causing upstream failures to block new runs.

Question 18

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?

Accepted Answer

Use BigQuery load jobs with schema auto-detection. BigQuery load jobs with schema auto-detection can directly ingest CSV files from Cloud Storage without the need for a Dataflow pipeline, eliminating the compute cost associated with Dataflow. Schema auto-detection infers column names and types from the CSV header and data, matching the requirement for schema inference while being a serverless, no-cost-for-compute operation (you only pay for storage and querying). This reduces cost by removing the Dataflow processing step entirely.

Answer

Use Dataproc instead of Dataflow

Answer

Use Cloud Functions to transform data

Answer

Use BigQuery Data Transfer Service

Question 19

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?

Accepted Answer

The Dataproc cluster is not in the VPC Service Controls perimeter.. The Dataproc cluster is created outside the VPC Service Controls perimeter, so even though the service account has the Storage Object Admin role at the project level, requests from the cluster are blocked by the perimeter's ingress/egress rules. VPC Service Controls enforce a security boundary that prevents resources outside the perimeter from accessing protected services like Cloud Storage, regardless of IAM permissions. The 'Access Denied' error in production, despite successful testing, strongly indicates a perimeter configuration mismatch.

Answer

The service account does not have the Storage Object Admin role on the bucket.

Answer

Data in transit encryption is not enabled for the Cloud Storage bucket.

Answer

Uniform bucket-level access prevents writes from service accounts.

Question 20

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?

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.