PDE Timed Practice Test 2 — 10 Questions

Question 1

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

Question 2

A company has deployed a machine learning model on Vertex AI Prediction that serves real-time predictions for a customer-facing application. The model was trained using a custom container and is hosted on a single endpoint with a minimum number of nodes. Recently, the team noticed that during peak traffic, prediction latency increases significantly and some requests time out. The endpoint is configured with a baseline traffic split of 100% on the current model version. Which action should the team take to reduce latency and improve reliability?

Accepted Answer

Configure horizontal autoscaling with a higher maximum number of nodes and set a CPU utilization target.. Option C is correct because configuring horizontal autoscaling with a higher maximum number of nodes and a CPU utilization target allows Vertex AI Prediction to automatically add more nodes during peak traffic, distributing the inference load and reducing latency. This directly addresses the root cause—insufficient compute resources under high demand—without requiring architectural changes or sacrificing availability.

Answer

Reduce the minimum number of nodes to zero to allow scale-to-zero during low traffic.

Answer

Place a Google Cloud Load Balancer in front of the Vertex AI endpoint to distribute requests across multiple endpoints.

Answer

Implement A/B testing by splitting traffic between two model versions to distribute load.

Question 3

A data engineer is designing a batch ETL pipeline that reads CSV files from Cloud Storage, transforms them using Dataproc, and writes the results to BigQuery. The data volume is expected to grow 10x in the next year. Which design approach best balances cost and performance?

Accepted Answer

Use a Dataproc cluster with preemptible worker nodes and autoscaling enabled.. Option C is correct because preemptible worker nodes significantly reduce cost (up to 80% discount) while autoscaling dynamically adjusts cluster size to match the growing workload, ensuring performance without over-provisioning. This combination handles the 10x data growth efficiently by scaling out during peak loads and scaling in during lulls, using preemptible instances for fault-tolerant tasks like transformation.

Answer

Create a single large persistent Dataproc cluster to handle the peak load.

Answer

Use Cloud Data Fusion to visually design the pipeline and run it on Dataproc.

Answer

Migrate the pipeline to Dataflow with Apache Beam and use flexRS for cost savings.

Question 4

A financial services company deploys a regression model to predict loan default risk. The model is served using Vertex AI Endpoints with autoscaling. After deployment, latency increases significantly during peak hours, causing timeouts. The model uses scikit-learn and has a large feature set. Which action should the team take to reduce latency while maintaining prediction accuracy?

Accepted Answer

Apply feature selection to reduce the number of input features.. Option D is correct because the latency spike is caused by the large feature set, which increases the time for preprocessing and inference in the scikit-learn model. Reducing the number of input features via feature selection directly decreases the computational load per request, lowering latency without sacrificing accuracy if the selected features retain predictive power. This addresses the root cause, unlike scaling or resource changes that only mask the symptom.

Answer

Switch to batch prediction for all requests.

Answer

Increase the minimum number of replicas in the endpoint to handle peak load.

Answer

Increase the memory allocation for the serving container.

Question 5

Which TWO actions are recommended to improve the reliability of a Cloud Dataflow streaming pipeline that processes event data from Pub/Sub?

Accepted Answer

Enable Dataflow Streaming Engine.. Option B is correct because enabling Dataflow Streaming Engine moves state and computation from worker VMs to the backend service, reducing the impact of worker scaling and preemption. This improves reliability by providing consistent performance and fault tolerance for streaming pipelines, especially those with high throughput or stateful processing.

Answer

Use a pull subscription with a 10-second acknowledgment deadline.

Answer

Disable autoscaling to prevent worker churn.

Answer

Use micro-batch processing with a small batch size.

Question 6

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.. Option B is correct because 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 7

A company is building a real-time streaming pipeline using Pub/Sub and Dataflow to process clickstream data. The pipeline writes aggregated metrics to BigQuery every 10 seconds using a fixed window. During peak traffic, some windows produce duplicate rows in BigQuery. What is the most likely cause?

Accepted Answer

Dataflow is retrying BigQuery streaming inserts after a timeout, and the retries succeed even though the original insert succeeded.. Option A is correct because Dataflow uses at-least-once semantics for streaming inserts into BigQuery. When a streaming insert times out, Dataflow retries the insert, and if the original insert actually succeeded but the acknowledgment was lost, the retry produces a duplicate row. This is a known behavior of BigQuery streaming inserts with retry logic.

Answer

The pipeline uses default triggers instead of after-watermark triggers.

Answer

The fixed window duration is too short, causing overlapping windows.

Answer

The pipeline is using too many Dataflow workers, causing load balancing issues.

Question 8

A data engineering team is operationalizing a machine learning model for real-time fraud detection. The model must process transactions with sub-100ms latency and be highly available. Which TWO strategies should the team implement?

Accepted Answer

Deploy the model to multiple Google Cloud regions for failover.. Deploying the model to multiple Google Cloud regions ensures high availability and failover capability. If one region becomes unavailable, traffic can be routed to another region, maintaining sub-100ms latency by using regional load balancing and Cloud DNS. This aligns with the requirement for a highly available, low-latency fraud detection system.

Answer

Deploy the model to a single zone to minimize cross-zone latency.

Answer

Use Cloud Batch for asynchronous prediction.

Answer

Store the model in Cloud Storage and load it on each request.

Question 9

You have a batch prediction job on Vertex AI that processes millions of records. The job is failing with an out-of-memory error. What is the best way to resolve this?

Accepted Answer

Use a machine type with more memory for the batch prediction job. Option D is correct because a batch prediction job on Vertex AI runs on a single machine (or a cluster of machines) and an out-of-memory (OOM) error indicates that the model or data processing exceeds the available RAM of the chosen machine type. Increasing the machine's memory directly addresses the root cause by providing more heap space for loading the model and processing large batches of predictions, without altering the job's parallelism or data partitioning.

Answer

Increase the minNodes and maxNodes for the batch prediction job

Answer

Split the input data into smaller files and run multiple batch prediction jobs

Answer

Enable autoscaling on the batch prediction job

Question 10

A financial services company uses Cloud Composer to orchestrate a daily workflow that includes a Dataproc job for risk analysis. The workflow sometimes fails because the Dataproc cluster creation times out. The cluster creation typically takes 3 minutes, but occasionally takes over 10 minutes. What is the most effective way to handle this variability?

Accepted Answer

Create a long-running Dataproc cluster that remains idle and reuse it for each workflow.. Option A is correct because creating a long-running Dataproc cluster and reusing it eliminates the variable cluster creation time that causes timeouts. Cloud Composer (Airflow) can manage cluster lifecycle separately from the workflow, ensuring the cluster is always available when the Dataproc job runs. This approach decouples cluster provisioning from job execution, making the workflow resilient to creation delays.

Answer

Implement a retry loop with exponential backoff in the DAG.

Answer

Use preemptible VMs for the cluster to reduce cost and improve creation speed.

Answer

Increase the cluster creation timeout in the Airflow configuration.