PDE Practice Test 3 — 10 Questions

Question 1

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?

Accepted Answer

Use a Conditional in the pipeline to check evaluation metrics and only run the deployment step if metrics pass thresholds. Option C is correct because Vertex AI Pipelines supports conditional execution via the `Condition` component, which allows you to evaluate model performance metrics (e.g., accuracy, RMSE) and gate subsequent steps. By placing the deployment step inside a conditional branch that only executes when evaluation metrics meet predefined thresholds, the pipeline automatically stops and avoids deploying a poor-performing model. This approach aligns with MLOps best practices for automated gating in production pipelines.

Answer

Run the evaluation step after deployment and roll back if performance is low

Answer

Configure the evaluation step to retry up to 3 times on failure

Answer

Create a separate pipeline for deployment and trigger it manually after review

Question 2

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 automatically tracks feature drift and skew by comparing the serving data distribution against the training data distribution using statistical tests like the Kolmogorov-Smirnov test. This is a best practice for detecting data quality issues that can degrade model performance in production.

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 3

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?

Accepted Answer

Use Dataproc clusters with auto-scaling and preemptible VMs.. Option D is correct because Dataproc clusters with auto-scaling and preemptible VMs directly address the need to reduce operational overhead and minimize costs for on-premises Spark migrations. Auto-scaling dynamically adjusts cluster size based on workload, while preemptible VMs (which cost 60-80% less than standard VMs) handle fault-tolerant tasks, making this the most cost-effective and operationally efficient architecture for Spark on Dataproc.

Answer

Use Cloud Dataproc Serverless for all Spark jobs.

Answer

Migrate jobs to Cloud Dataflow.

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Run Spark on Compute Engine instances with startup scripts.

Question 4

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?

Accepted Answer

Package the model in a custom container and deploy to Vertex AI Endpoints. Option C is correct because Vertex AI Endpoints support custom containers, allowing you to package your scikit-learn model with its dependencies (e.g., a Flask or FastAPI inference server) and deploy it without rewriting any code. This approach directly meets the requirement to operationalize the existing model on Google Cloud without modification.

Answer

Export the model as a PMML file and use BigQuery ML

Answer

Use AI Platform Training to host the model directly

Answer

Convert the scikit-learn model to TensorFlow SavedModel format

Question 5

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.)

Accepted Answer

Use Cloud Dataproc with Spark and Hive components.. Option B is correct because Cloud Dataproc is a managed Spark and Hadoop service that supports the same Spark and Hive components used on-premises, allowing existing Spark jobs to run with minimal modification. It provides native integration with Cloud Storage, which can replace HDFS without changing job logic, and the Dataproc Jobs API enables programmatic job submission, preserving existing workflows.

Answer

Migrate to BigQuery for all analytics.

Answer

Rewrite Spark jobs as Dataflow pipelines.

Question 6

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.. Option A is correct because 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 7

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?

Accepted Answer

Use Customer-Managed Encryption Keys (CMEK) and VPC Service Controls.. Option A is correct because Customer-Managed Encryption Keys (CMEK) allow the company to control the encryption keys used to protect data at rest in Cloud Dataflow, while VPC Service Controls provide a security perimeter that prevents data exfiltration and ensures end-to-end encryption boundaries. Together, they address the compliance requirement for encryption control and audit logging by restricting data movement within a VPC service perimeter and using customer-managed keys for data encryption.

Answer

Use Data Loss Prevention API to redact sensitive data.

Answer

Enable Cloud Audit Logs and VPC Service Controls.

Answer

Enable default encryption at rest and in transit.

Question 8

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?

Accepted Answer

Cloud Dataflow. Cloud Dataflow (D) is correct because it provides exactly-once processing semantics via its distributed snapshot mechanism (based on the MillWheel paper) and supports both real-time streaming and batch processing for historical backtesting under a unified programming model. This allows the company to reprocess historical data using the same pipeline code, ensuring consistency across real-time and batch modes.

Answer

Cloud Pub/Sub

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Cloud Functions

Answer

Cloud Dataproc

Question 9

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

Accepted Answer

Set autoscaling policies based on YARN memory.. Option B is correct because autoscaling policies based on YARN memory allow the cluster to dynamically add or remove worker nodes in response to actual resource demand from running jobs. This prevents over-provisioning (reducing costs) and ensures sufficient resources for job completion (improving performance), as Cloud Dataproc directly monitors YARN memory metrics to trigger scaling actions.

Answer

Always use persistent disks for all nodes.

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Store intermediate data in HDFS.

Answer

Use the largest machine types for master nodes.

Question 10

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