PDE Practice Test 2 — 10 Questions

Question 1

Your company has a machine learning model that predicts customer churn. The model is deployed on Vertex AI Endpoints with autoscaling. After a marketing campaign, traffic to the endpoint increases by 10x. Some predictions start failing with 'HTTP 503 Service Unavailable' errors. What is the most likely cause?

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

Accepted Answer

The autoscaling configuration has insufficient maximum nodes to handle the traffic.. A 503 Service Unavailable error from Vertex AI Endpoints indicates that the endpoint is overwhelmed and cannot handle the incoming request volume. With a 10x traffic spike and autoscaling configured, the most likely cause is that the autoscaling configuration has insufficient maximum nodes, so the endpoint cannot scale out enough to handle the load, causing requests to be rejected.

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The model container has a memory leak.

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The model's accuracy has degraded due to data drift.

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The model is using an older version that is not supported.

Question 2

A user gets the above error when trying to get online predictions. The model was created and the endpoint exists. What is the most likely reason?

Accepted Answer

No version of the model is deployed to the endpoint.. The error 'No version of the model is deployed to the endpoint' occurs when the endpoint exists but has no active model version assigned to it. In Google Cloud AI Platform (Vertex AI), an endpoint must have at least one deployed model version to serve predictions. Without a deployed version, the endpoint cannot handle inference requests, even though the endpoint resource exists.

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The endpoint does not exist.

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The endpoint is in a different region than the model.

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The model does not exist.

Question 3

A data science team wants to deploy a model that requires a custom container with specific NVIDIA CUDA version. They build the image and push to Artifact Registry. When deploying to Vertex AI, the model fails to load with an error: 'Failed to start container: invalid ELF header'. What is the most likely cause?

Accepted Answer

The container image was built for a different CPU architecture (e.g., ARM64) than the Vertex AI machine (x86_64). The error 'invalid ELF header' indicates that the container image's executable format is not compatible with the host architecture. Vertex AI uses x86_64 machines, so if the image was built for ARM64 (e.g., on an Apple M1), it will fail to start. This is the most likely cause. Other options would produce different errors (e.g., CUDA incompatibility, permission denied, corrupted model file).

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The model file (saved as .pkl) is corrupted

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The CUDA version in the container is incompatible with the GPU on the machine

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The container does not have the necessary permissions to access the model file in Cloud Storage

Question 4

You are designing a Dataflow pipeline that reads from Pub/Sub and writes to BigQuery. Some incoming messages are malformed and fail to parse. How should you handle these messages to ensure the pipeline continues processing without data loss?

Accepted Answer

Write malformed messages to a dead-letter sink (e.g., Pub/Sub topic or GCS) and continue processing. Writing malformed messages to a dead-letter sink (Pub/Sub topic or GCS) preserves the data for later inspection while allowing the pipeline to continue processing valid messages. This is the standard Dataflow/Apache Beam pattern for handling unparseable records without halting the stream or silently discarding data. It satisfies both the 'no data loss' and 'continue processing' requirements.

Answer

Configure Pub/Sub to retry indefinitely until the message is processed

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Use a try-catch block in the pipeline and ignore malformed messages

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Set the pipeline to fail and alert the team via Cloud Monitoring

Question 5

In the Vertex AI Pipeline component YAML exhibit, the component is designed to evaluate a model and produce metrics. If the threshold_accuracy is set to 0.85, what is the expected behavior of this component?

Accepted Answer

It will output the evaluation metrics, and the pipeline can use them for conditional decisions. In Vertex AI Pipelines, a component's YAML definition specifies inputs, outputs, and implementation. Setting `threshold_accuracy` to 0.85 defines a parameter that the component can use internally, but by itself it does not trigger deployment or cause failure. The component's expected behavior is to output evaluation metrics, and the pipeline can then use those metrics in conditional logic (e.g., via `Condition` or `if/else` tasks) to decide subsequent steps, such as model deployment or retraining.

Answer

It will deploy the model if the accuracy meets the threshold

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It will ignore the threshold_accuracy input if not provided

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It will fail if the model accuracy is below 0.85

Question 6

A data engineer needs to automatically delete objects from a Cloud Storage bucket after 30 days and archive them to nearline storage after 7 days. Which configuration should they use?

Accepted Answer

Set a lifecycle rule to SetStorageClass to nearline after 7 days and delete after 30 days. It implements a lifecycle rule that first transitions objects to Nearline storage after 7 days (reducing costs for infrequently accessed data) and then deletes them after 30 days. This matches the requirement to archive after 7 days and delete after 30 days, using the `SetStorageClass` and `Delete` actions in the correct chronological order.

Answer

Set a lifecycle rule to SetStorageClass to nearline after 30 days only

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Set a lifecycle rule to delete objects after 7 days only

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Set a lifecycle rule to delete objects after 7 days and SetStorageClass to nearline after 30 days

Question 7

A data pipeline processes JSON files from Cloud Storage, transforms them using Apache Beam, and writes the output to BigQuery. Some records are malformed and cause the pipeline to fail. How should the engineer handle these errors to ensure the pipeline continues processing while preserving the malformed records for analysis?

Accepted Answer

Catch exceptions in a DoFn and write the malformed records to a separate Cloud Storage bucket using a FileIO sink.. It allows the pipeline to continue processing by catching exceptions within a DoFn and writing malformed records to a separate Cloud Storage bucket using a FileIO sink. This preserves the malformed records for later analysis without blocking the main data flow, which is a standard pattern in Apache Beam for handling dead-letter records. The approach ensures fault tolerance while maintaining data integrity for debugging.

Answer

Set the pipeline to retry malformed records indefinitely until they succeed.

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Use a side input to send malformed records to a dead letter queue in Pub/Sub for later reprocessing.

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Log the malformed records to Stackdriver and skip them in the pipeline.

Question 8

A company wants to use AutoML Tables to build a classification model on a dataset with 100 features and 500,000 rows. They need to deploy the model for online predictions with low latency (<100 ms). Which deployment option should they choose?

Accepted Answer

Deploy the model to an endpoint in Vertex AI using the AutoML endpoint service. AutoML Tables models are natively served through Vertex AI endpoints, which provide a managed, low-latency online prediction service. Deploying to a Vertex AI endpoint using the AutoML endpoint service preserves the AutoML model format and gives the sub-100 ms latency required for online predictions.

Answer

Export the model as a TF SavedModel and deploy on Cloud Run

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Deploy the model on AI Platform Prediction

Answer

Use batch prediction in Vertex AI

Question 9

A company uses Cloud Dataproc for ephemeral clusters to run batch jobs. They want to ensure job reliability and data quality. Which two configuration options should they use? (Choose two.)

Accepted Answer

Use initialization actions for cluster setup.. Option B is correct because initialization actions let you run scripts on every node at cluster creation, so you can install monitoring/validation agents, configure logging, and enforce consistent setup that supports job reliability and data quality across ephemeral clusters. Option E is correct because graceful decommissioning (set via yarn:yarn.resourcemanager.decommissioning.timeout or Dataproc's gracefulDecommissionTimeout) lets running tasks finish before workers are removed, preventing partial job failures and data corruption when nodes are scaled down or preempted. Option A is not appropriate here because preemptible VMs reduce cost but can be reclaimed at any time, which undermines reliability rather than improving it. Option C is not appropriate because idle timeout only deletes idle clusters to save cost; it does not improve job reliability or data quality. Option D is not appropriate because custom machine types tune CPU/memory performance but do not by themselves ensure reliable job completion or data quality.

Answer

Enable preemptible VMs for cost savings.

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Enable idle timeout to automatically delete clusters.

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Use custom machine types for better performance.

Question 10

You are designing a data processing architecture on Google Cloud. You need to ingest data from multiple sources, including streaming events and batch files, and process them to produce a unified dataset for analytics. The solution must support both real-time and historical processing with the same codebase, and be able to handle late-arriving data. Which two Google Cloud services should you use together to achieve this? (Choose two.)

Accepted Answer

Cloud Pub/Sub. To achieve unified batch and stream processing with the same codebase and handle late data, you need a processing engine that supports Apache Beam, such as Cloud Dataflow, and a scalable ingestion service for streaming data, such as Cloud Pub/Sub. Dataflow's Beam model allows you to write one pipeline that works for both batch and streaming, with built-in support for event-time windowing and late data. Pub/Sub provides reliable, scalable ingestion for streaming events.

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Cloud Composer

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Cloud Dataproc

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BigQuery