PDE Practice Test 30 — 15 Questions

Question 1

A team deploys a Cloud Run service that processes user-uploaded files. Some requests time out after 60 minutes. They need to handle large files reliably without losing tasks. What is the best solution?

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

Accepted Answer

Containerize the processing logic and trigger it via Cloud Tasks.. Cloud Run has a maximum request timeout of 60 minutes (3600 seconds), so simply increasing the timeout is not possible. By containerizing the processing logic and triggering it via Cloud Tasks, you decouple the request from the synchronous HTTP timeout, allowing the task to run asynchronously for up to 24 hours. This ensures large files are processed reliably without losing tasks, as Cloud Tasks provides automatic retries and dead-letter queues.

Answer

Increase the request timeout to 3600 seconds.

Answer

Use Cloud Functions instead of Cloud Run.

Answer

Split the file into chunks and process them concurrently.

Question 2

A company deploys a new machine learning model for real-time predictions using Vertex AI. The model is stored in a Cloud Storage bucket and deployed to an endpoint. To ensure traceability and rollback capability, which practice should be followed?

Accepted Answer

Create a new model resource in Vertex AI for each version and deploy the specific version to an endpoint.. Creating a new model resource in Vertex AI for each version ensures that each model iteration is independently tracked, versioned, and can be deployed to an endpoint with full rollback capability. This practice aligns with Vertex AI's model versioning and endpoint deployment model, where each model resource has a unique ID and can be deployed or undeployed without affecting other versions, enabling precise traceability and rollback.

Answer

Deploy multiple versions of the model to the same endpoint using traffic splitting and set the primary version to 100% traffic.

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Use the same model name for all deployments and overwrite the existing model.

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Store the model in a Cloud Storage bucket with a fixed name and rely on Cloud Build for rollback.

Question 3

Your organization has a BigQuery flat-rate reservation with 2000 slots. During peak hours, query performance degrades because concurrent queries exceed the available slots. You want to handle these bursts without changing the base reservation. What should you do?

Accepted Answer

Purchase flex slots during peak hours to add capacity temporarily.. Flex slots allow you to temporarily add capacity to a BigQuery flat-rate reservation without committing to a permanent increase. This handles burst workloads during peak hours by adding slots on demand, and you only pay for the time they are used. The base reservation of 2000 slots remains unchanged, meeting the requirement.

Answer

Enable autoscaling on the reservation to automatically add slots up to a maximum.

Answer

Purchase committed use discounts to increase the base reservation to 3000 slots.

Answer

Change the pricing model to on-demand to allow unlimited slots.

Question 4

A company has a model that requires GPU for inference and has strict latency requirements. They deployed on Vertex AI Endpoint with autoscaling but observe cold start latency when scaling up. What is the best solution?

Accepted Answer

Set a higher min_replica_count to keep instances warm. Setting a higher min_replica_count ensures that a baseline number of GPU instances are always running and ready to serve inference requests, eliminating cold start latency because new instances do not need to be provisioned and loaded from scratch when traffic spikes. This directly addresses the autoscaling-induced cold start issue by maintaining a warm pool of replicas.

Answer

Pre-compile the model with TensorRT

Answer

Use a larger GPU instance

Answer

Switch to batch prediction

Question 5

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 6

You are a data engineer at a financial services company. You have deployed a credit risk model on Vertex AI Endpoints using a custom container with a TensorFlow SavedModel. The model expects input features as a JSON object. Recently, the model has been returning high prediction latency and occasional 503 errors. You have enabled autoscaling with minNodes=2 and maxNodes=10. The model is CPU-only and uses n1-standard-4 machines. Monitoring shows that during peak hours, CPU utilization reaches 90% and memory is at 80%. The number of prediction requests per second peaks at 100. You suspect that the model is not scaling fast enough. Which action will most effectively reduce latency and eliminate 503 errors?

Accepted Answer

Change the machine type to n1-standard-4 with a GPU (e.g., NVIDIA T4) and update the custom container to use GPU. The high CPU utilization (90%) indicates that the model's inference is compute-bound. Offloading the computation to a GPU (NVIDIA T4) significantly accelerates TensorFlow model inference, reducing per-request latency and allowing each replica to handle more requests per second. This directly addresses the root cause of the 503 errors (requests timing out due to slow inference) and reduces the need for rapid scaling.

Answer

Increase maxNodes to 20 to allow more replicas during peak

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Set minNodes to 5 to keep more replicas warm

Answer

Switch to n1-highmem-4 machines to provide more memory per node

Question 7

You are designing a streaming Dataflow pipeline that processes high-throughput data. Which two features can help minimize cost? (Choose TWO.)

Accepted Answer

Enable autoscaling based on CPU utilization. Enabling autoscaling based on CPU utilization allows the Dataflow pipeline to dynamically adjust the number of worker instances in response to the actual processing load. This prevents over-provisioning during low-throughput periods, directly reducing compute cost while maintaining performance during spikes.

Answer

Use batch loads to BigQuery for streaming inserts

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Use preemptible VMs for all workers

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Use a global window and batch output to BigQuery every hour

Question 8

An application needs to store user profile data in a document database with flexible schema. The data is accessed frequently from a mobile app. Which Google Cloud database is BEST suited?

Accepted Answer

Cloud Firestore. Cloud Firestore is a NoSQL document database designed for mobile and web app development, offering flexible schema, real-time data synchronization, and automatic scaling. It directly supports frequent reads from mobile apps through its client SDKs and offline persistence, making it the best fit for storing user profile data with varying attributes.

Answer

Cloud Bigtable

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BigQuery

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Cloud Spanner

Question 9

A data engineer wants to automatically detect when the distribution of input features to a production model has shifted significantly. Which Vertex AI feature should they enable?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is the correct service because it is specifically designed to continuously detect feature distribution drift and prediction skew in production models. It automatically compares the current input feature distribution against a baseline (e.g., training data) and triggers alerts when significant statistical shifts occur, enabling proactive retraining or investigation.

Answer

Vertex AI Vizier

Answer

Vertex AI Explainable AI

Answer

Vertex AI Feature Store

Question 10

A data team uses Cloud Dataproc to run nightly Spark jobs. The job volume has increased, and the cluster is often underutilized during the day. They want to reduce costs while ensuring jobs can scale when needed. Which strategy should they adopt?

Accepted Answer

Use a cluster with a small number of primary workers and a large pool of preemptible workers, and enable autoscaling.. It combines a small number of primary (non-preemptible) workers for reliability with a large pool of preemptible workers for cost-effective scaling, and enables autoscaling to dynamically adjust the cluster size based on workload. This minimizes cost during idle periods (preemptible instances are ~80% cheaper) while ensuring jobs can scale up quickly when needed, as autoscaling adds preemptible workers automatically. Preemptible workers are ideal for fault-tolerant Spark jobs that can handle node preemptions.

Answer

Use preemptible workers for both primary and secondary nodes to minimize cost.

Answer

Manually scale the cluster up before nightly jobs and down after.

Answer

Use custom machine types with local SSDs for primary workers to improve I/O.

Question 11

You are deploying a machine learning model to production using Vertex AI. The model requires GPU acceleration for low-latency predictions. You need to minimize costs while ensuring availability during a defined business hours window (8 AM to 6 PM). Which deployment strategy should you use?

Accepted Answer

Deploy to an endpoint with manual scaling, set min nodes to zero and max nodes to 10, and use a cron job to adjust during business hours.. It uses manual scaling with a cron job to set min nodes to zero outside business hours (8 AM–6 PM) and scale up to a maximum of 10 nodes during business hours, ensuring GPU availability when needed while minimizing costs by running zero instances when the model is not required. This approach directly addresses the requirement for low-latency GPU predictions during a defined window without paying for idle GPU resources outside that window.

Answer

Use a custom prediction routine (CPR) that dynamically requests GPUs from the cluster.

Answer

Deploy to a dedicated endpoint with a GPU machine and configure autoscaling.

Answer

Use Cloud Functions to invoke the model, and let Google Cloud manage the underlying GPU infrastructure.

Question 12

A company trains a model using Cloud TPUs. The model is deployed to AI Platform Prediction using a custom container with TensorFlow. Which THREE considerations are most important when serving this model?

Accepted Answer

The serving container must have the same TensorFlow version that was used during training to avoid compatibility issues.. TensorFlow models are tightly coupled to the specific version of TensorFlow used during training. Serving with a different version can lead to incompatibilities in graph serialization, op definitions, or checkpoint formats, causing runtime errors or silent prediction failures. AI Platform Prediction's custom container must therefore match the training environment's TensorFlow version to ensure the model loads and executes correctly.

Answer

The model should be retrained using GPU to ensure identical performance on serving hardware.

Answer

The model should be quantized to reduce memory footprint before deployment.

Question 13

A data analyst runs a complex SQL query in BigQuery that joins multiple large tables and receives the above error. Which action is most likely to resolve the issue?

Accepted Answer

Increase the number of slots allocated to the project.. The error indicates that the query exceeded the available slot resources in the BigQuery project. Increasing the number of slots allocated to the project (option D) directly addresses this by providing more compute capacity for parallel query execution, which is the correct action to resolve resource exhaustion in BigQuery's serverless architecture.

Answer

Use a larger number of workers in the query execution.

Answer

Use smaller tables by sampling data.

Answer

Add clustering on join columns.

Question 14

A company is deploying a machine learning model for fraud detection. The model is trained using TensorFlow and will be served on Vertex AI Prediction. The team wants to implement model monitoring to detect prediction drift. Which TWO actions should they take? (Choose 2)

Accepted Answer

Configure Vertex AI Model Monitoring to compare online prediction inputs against training data statistics.. Vertex AI Model Monitoring can be configured to compare online prediction inputs against training data statistics to detect skew, which is a form of drift. This is a standard capability of Vertex AI Model Monitoring, where you specify a baseline dataset (typically training data) and the service automatically computes statistics on incoming prediction requests to identify distribution shifts.

Answer

Collect ground truth labels for all predictions to measure accuracy drift.

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Set up a separate Cloud Monitoring alerting policy to watch for prediction errors.

Answer

Enable automatic model retraining in Vertex AI Model Monitoring when drift is detected.

Question 15

A company wants to use Dataproc Metastore to manage metadata for their Spark jobs. Which TWO benefits does Dataproc Metastore provide?

Accepted Answer

High availability with automatic failover. Dataproc Metastore offers a managed Hive metastore with high availability and compatibility.

Answer

Automatic scaling of compute resources

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Integration with BigQuery

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Built-in data lineage tracking