PDE Practice Test 5 — 10 Questions

Question 1

A data engineer is preparing a dataset for ML training in Vertex AI. The dataset includes a timestamp column, a categorical column with high cardinality (1000 distinct values), and a numerical column with outliers. Which two preprocessing steps should they apply? (Choose TWO)

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

Accepted Answer

Label encode the categorical column. One-hot encoding for high cardinality may be too sparse; label encoding (ordinal encoder) is more common. Winsorizing clips outliers.

Answer

Drop the timestamp column

Answer

Normalize the numerical column using Z-score

Answer

One-hot encode the categorical column

Question 2

A company runs a Dataflow pipeline that reads from Pub/Sub, aggregates events in a 10-minute fixed window, and writes to BigQuery. Recently, the pipeline has been failing with 'high uncommitted bytes' errors during periods of high traffic. What is the most likely cause and recommended action?

Accepted Answer

Use a global window with a trigger that fires early based on element count to reduce the number of open windows.. The 'high uncommitted bytes' error in Dataflow occurs when the system holds too much data in memory across many open windows, exceeding the default 200 MB limit. Using a global window with an early trigger based on element count reduces the number of simultaneous open windows and allows data to be committed more frequently, preventing memory pressure. This approach is recommended over reducing window size or scaling workers because the root cause is window fan-out, not throughput or parallelism.

Answer

Reduce the window size from 10 minutes to 1 minute to decrease the amount of data per window.

Answer

Increase the number of worker machines to handle higher throughput.

Answer

Set a maximum number of workers and use a Pub/Sub flow control setting to limit incoming messages.

Question 3

Refer to the exhibit. A team is trying to run a custom prediction container on Vertex AI Endpoint. They get this error when the container starts. What is the most likely cause?

Accepted Answer

The entry point is missing or incorrect. The error occurs when the container starts, which typically happens during the initial health check or readiness probe. Vertex AI Endpoints require a valid entry point (e.g., CMD or ENTRYPOINT in the Dockerfile) to start the prediction server. If the entry point is missing or incorrect, the container fails to launch, resulting in the observed error.

Answer

The container image is too large

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The container is built for a different CPU architecture

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The model file is missing from the container

Question 4

A data engineer is building a Cloud Workflows workflow that orchestrates multiple Cloud Functions and API calls. The workflow should handle transient failures with retries and send a notification to a Pub/Sub topic if the workflow ultimately fails. Which THREE steps should the engineer include in the workflow definition?

Accepted Answer

Use the 'googleapis.pubsub.v1.projects.topics.publish' connector to send a failure notification.. Workflows supports retry policies via 'retry' blocks, conditional steps using 'switch', and Pub/Sub publishing via the 'googleapis.pubsub.v1.projects.topics.publish' connector. The 'try/catch' is not a Workflows construct; instead, use 'step' with 'retry' and 'on_error' for failure handling. 'for' loops are for iteration, not error handling.

Answer

Use a 'for' loop to iterate over retries.

Answer

Use a 'try' / 'catch' block to handle exceptions and route to a failure step.

Question 5

A company uses Cloud Composer (Airflow) to orchestrate pipelines. They want to implement a pattern where a task polls for a file arrival in Cloud Storage and then triggers subsequent tasks. Which THREE Airflow concepts are essential? (Choose 3)

Accepted Answer

Sensors (e.g., GCSObjectExistenceSensor). A is correct because Sensors are a specialized type of operator designed to wait for a specific condition, such as file arrival in Cloud Storage. The `GCSObjectExistenceSensor` in Cloud Composer (Airflow) polls Google Cloud Storage at a configurable interval until the target file exists, making it the precise tool for this file-arrival polling pattern.

Answer

Operators (e.g., PythonOperator)

Answer

SubDAGs for grouping tasks

Question 6

Your company uses Vertex AI Pipelines to automate model retraining. The pipeline has three steps: data extraction from BigQuery, feature engineering using Dataflow, and model training using a custom container on Vertex AI Training. Recently, the pipeline has been failing intermittently at the Dataflow step with a 'The job encountered a transient error. Please retry.' message. You have enabled pipeline retries with 3 attempts. However, the pipeline still fails after 3 retries. You check the logs and find that the Dataflow job requires more resources than the default worker configuration provides. Which change should you make to reduce the failure rate?

Accepted Answer

Increase the Dataflow worker machine type to have more memory and CPU in the pipeline step configuration. The pipeline fails due to insufficient resources (memory and CPU) in the default Dataflow worker configuration. By increasing the worker machine type (e.g., using a custom machine type with more vCPUs and memory), the Dataflow job can handle the feature engineering workload without hitting resource limits, reducing transient failures. This directly addresses the root cause identified in the logs, unlike retries or parallelism changes.

Answer

Increase the number of Dataflow workers to improve parallelism

Answer

Increase the number of retries in the pipeline to 5

Answer

Replace Dataflow with Dataproc to run the feature engineering step

Question 7

A production model deployed on Vertex AI Endpoint is experiencing high latency during traffic spikes. The current configuration uses a single replica. What is the most efficient solution?

Accepted Answer

Enable autoscaling with minReplicaCount=1 and maxReplicaCount=10. Enabling autoscaling with minReplicaCount=1 and maxReplicaCount=10 allows Vertex AI Endpoint to dynamically add replicas during traffic spikes, distributing inference requests across multiple instances and reducing latency. This is the most efficient solution as it scales resources up only when needed, avoiding over-provisioning and minimizing cost during low traffic periods.

Answer

Set a higher min replica count (e.g., 3)

Answer

Use a larger machine type (e.g., n1-highmem-8)

Answer

Switch to batch prediction to handle spikes

Question 8

Which THREE of the following are best practices when designing a Cloud Dataflow pipeline for batch processing? (Choose three.)

Accepted Answer

Use side inputs to hold a large lookup table that is read in every element.. Side inputs in Cloud Dataflow are designed to efficiently broadcast a read-only dataset (like a lookup table) to all parallel workers. When the side input is a large but static dataset, Dataflow can cache it in memory or on disk across workers, avoiding repeated external lookups and reducing per-element processing overhead. This pattern is especially effective for batch processing where the side input is read once and reused across all elements.

Answer

Use mutable state within ParDo to track running totals.

Answer

Always insert a Reshuffle transform after every GroupByKey to redistribute data.

Question 9

Which TWO best practices should be followed when managing multiple model versions on Vertex AI Endpoints for a production system?

Accepted Answer

Use traffic splitting to gradually shift traffic to a new version while monitoring performance.. Vertex AI Endpoints support traffic splitting, allowing you to route a percentage of inference requests to a new model version while the rest goes to the existing version. This enables gradual rollout, monitoring of performance metrics (e.g., latency, error rate), and safe rollback without downtime. It is a best practice for production systems to validate a new version before fully cutting over.

Answer

Always keep all historical versions deployed to enable fast rollback.

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If two versions share the same endpoint, they must have exactly the same machine type.

Answer

Use the same endpoint for multiple versions and adjust min_replica_count, max_replica_count for each version.

Question 10

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

Answer

Set a lifecycle rule to delete objects after 7 days only

Answer

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