PDE Practice Test 31 — 15 Questions

Question 1

Your organization has a BigQuery flat-rate reservation with 500 slots. During peak hours, queries are queued and you need additional capacity temporarily. You want to add slots for a burst of activity without committing to a long-term purchase. What should you do?

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

Accepted Answer

Use flex slots. Flex slots are a BigQuery feature that allows you to purchase slots for a short, fixed duration (e.g., 1 hour, 1 day, 1 week) without a long-term commitment. They are designed exactly for temporary bursts of query activity, such as peak-hour spikes, and can be added to an existing flat-rate reservation. By using flex slots, you temporarily increase the slot capacity of your reservation, reducing queuing, and then the slots automatically expire after the chosen period, avoiding ongoing costs.

Answer

Switch to on-demand pricing

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Create a secondary reservation with autoscaling

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Purchase additional committed use reservations

Question 2

A data scientist has iterated on a model and produced a new version. The organization requires the ability to roll back to the previous version quickly if the new version performs poorly in production. Which approach should be used?

Accepted Answer

Upload both versions to Vertex AI Model Registry and use endpoint traffic splitting to route 100% to the safe version if needed.. Vertex AI Model Registry allows you to deploy multiple model versions and use endpoint traffic splitting to gradually shift traffic or instantly route 100% to a specific version. This enables immediate rollback by setting the traffic split to 100% for the previous model version without redeploying or changing infrastructure.

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Store each model version in a separate Cloud Storage bucket.

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Keep the previous model in a container image and redeploy via Cloud Run.

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Use Cloud Source Repositories to tag model versions.

Question 3

A data team needs to run complex analytical queries on a dataset that is frequently updated with new rows. They want to minimize query costs and avoid scanning old data that is rarely queried. Which BigQuery feature should they use?

Accepted Answer

Partitioned tables with partition expiration. Partitioned tables with partition expiration allow you to divide a table into segments based on a date/timestamp column, and automatically delete partitions that are older than a specified duration. This minimizes query costs by only scanning relevant partitions and eliminates storage costs for old, rarely queried data without manual intervention.

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BigQuery materialized views

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Clustered tables

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BigQuery BI Engine

Question 4

The exhibit shows a Cloud Logging query result. A data engineer sees this log for a streaming Dataflow job. What is the most likely cause?

Accepted Answer

The job has reached the maximum number of workers allowed by the project quota.. The log shows that the Dataflow job is not scaling up despite pending work. This typically occurs when the job has reached the maximum number of workers allowed by the project quota. Dataflow uses the Compute Engine default worker quota, and if the job attempts to exceed that limit, it will stop scaling and log messages indicating that it cannot add more workers.

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The job is experiencing network latency.

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The job is using too much memory per worker.

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The job has insufficient permissions to scale.

Question 5

A company uses Looker Studio to build dashboards from BigQuery data. They notice that queries take several seconds to return. They want to improve performance without changing the schema or adding materialized views. Which option should they use?

Accepted Answer

Enable BigQuery BI Engine on the relevant project.. BI Engine accelerates sub-second query response times in Looker Studio by caching data in memory within the BigQuery region.

Answer

Move the data to Cloud SQL.

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Switch to BigQuery Omni for cross-cloud queries.

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Use APPROX_COUNT_DISTINCT to speed up distinct counts.

Question 6

A data engineer needs to query data across BigQuery (in Google Cloud) and Snowflake (in AWS) without moving the data. Which service should they use?

Accepted Answer

BigQuery Omni. BigQuery Omni allows querying data across multiple clouds using BigQuery's interface, with compute running in the respective cloud. Data stays in place.

Answer

Dataflow

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Cloud SQL

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Vertex AI Feature Store

Question 7

You need to stream real-time user click events from your application into BigQuery for immediate analysis. The events must be available for query within seconds. Which approach is recommended?

Accepted Answer

Use Pub/Sub to Dataflow to BigQuery with the Storage Write API for high-throughput streaming.. The recommended approach is to use Pub/Sub to Dataflow to BigQuery with the Storage Write API. Dataflow provides a managed stream processing service that can handle high-throughput, low-latency ingestion, and the Storage Write API offers exactly-once semantics and is optimized for streaming inserts into BigQuery. This combination ensures events are available for query within seconds and scales well.

Answer

Use Cloud Data Fusion to ingest streaming data from Pub/Sub into BigQuery.

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Use Cloud Functions to receive events from Pub/Sub and insert them into BigQuery using the legacy streaming API.

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Use Pub/Sub with a BigQuery subscription to directly write events into BigQuery.

Question 8

You are designing a Cloud Composer workflow that loads data from Cloud Storage into BigQuery, runs a Dataflow job to transform the data, and then triggers a Dataproc Spark job. After each step, you need to conditionally branch based on success or failure. Which Airflow feature allows you to pass messages between tasks to enable dynamic branching?

Accepted Answer

XComs. XComs (cross-communications) are Airflow's built-in mechanism for passing small pieces of data between tasks. A task can push a value via xcom_push() or by returning it, and downstream tasks pull it via xcom_pull(), enabling dynamic decisions such as choosing a branch based on a prior task's output. This is exactly what conditional branching in a Composer DAG requires.

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Sensors

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TaskFlow API

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DAG dependencies

Question 9

A financial services company uses Vertex AI to serve a fraud detection model. The model was trained on historical data that is updated daily. The team wants to automate retraining when data drift is detected. Which approach best operationalizes this requirement with minimal manual intervention?

Accepted Answer

Enable Vertex AI Model Monitoring for feature drift and skew, then create a Cloud Function that triggers a Vertex AI Pipeline to retrain and deploy the model after validation.. It uses Vertex AI Model Monitoring to automatically detect feature drift or skew, then triggers a Cloud Function that invokes a Vertex AI Pipeline to retrain and redeploy the model after validation. This approach minimizes manual intervention by automating both the detection of data drift and the subsequent retraining and deployment lifecycle.

Answer

Use Cloud Monitoring alerts on prediction latency to trigger a retraining pipeline.

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Manually monitor model performance metrics in Vertex AI Experiments and retrain when accuracy drops.

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Use scheduled Vertex AI Pipelines to retrain the model every night, then deploy automatically.

Question 10

A team has multiple versions of a model and wants to manage them centrally, including tracking metadata and promoting versions to production. Which tool should they use?

Accepted Answer

Vertex AI Model Registry. Vertex AI Model Registry is the correct tool because it is purpose-built for centrally managing multiple model versions, tracking metadata (such as training parameters, evaluation metrics, and lineage), and promoting versions through stages like staging to production. Unlike generic storage or version control systems, it provides native integration with Vertex AI Pipelines and endpoints for controlled rollout and rollback.

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Cloud Storage

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BigQuery

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GitHub

Question 11

Which TWO actions can help reduce prediction latency for a Vertex AI endpoint?

Accepted Answer

Optimize the model architecture to reduce size. Optimizing the model architecture to reduce size directly decreases the computational load during inference, which lowers prediction latency. Smaller models require fewer floating-point operations (FLOPs) per prediction, enabling faster response times on Vertex AI endpoints.

Answer

Increase the number of features

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Use a larger machine type with more vCPUs

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Set min replicas to 0 to save cost

Question 12

A company is running a Cloud Dataflow streaming pipeline that aggregates events in 1-minute windows. They notice that the watermark is lagging significantly behind real-time. What is the most likely cause?

Accepted Answer

A hot key is causing data skew.. A hot key causes data skew, which means a disproportionate amount of data is assigned to a single key. In Cloud Dataflow, this leads to a single worker processing the bulk of the events, creating a processing bottleneck. The watermark, which tracks the progress of event-time processing, cannot advance until all data for a given window is processed, so the skewed key delays watermark progression significantly behind real-time.

Answer

The window duration is too short.

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The pipeline was recently updated.

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The allowed lateness is set too high.

Question 13

A team wants to retrain a model weekly using new data stored in BigQuery. They want to minimize manual effort. Which approach should they use?

Accepted Answer

Create a Vertex AI Pipeline scheduled via Cloud Scheduler. Vertex AI Pipelines allow you to define a repeatable, automated ML workflow that can be triggered on a schedule via Cloud Scheduler. This minimizes manual effort by handling data extraction from BigQuery, model retraining, and deployment without human intervention, while also providing versioning and monitoring capabilities.

Answer

Use Cloud Scheduler to trigger a Cloud Function that retrains

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Retrain manually in a notebook each week

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Use Cloud Composer to orchestrate retraining

Question 14

A data pipeline reading from Cloud Storage and writing to BigQuery using Dataflow is experiencing high cost. The data is CSV and needs schema inference. What change reduces cost?

Accepted Answer

Use BigQuery load jobs with schema auto-detection. BigQuery load jobs with schema auto-detection can directly ingest CSV files from Cloud Storage without the need for a Dataflow pipeline, eliminating the compute cost associated with Dataflow. Schema auto-detection infers column names and types from the CSV header and data, matching the requirement for schema inference while being a serverless, no-cost-for-compute operation (you only pay for storage and querying). This reduces cost by removing the Dataflow processing step entirely.

Answer

Use Dataproc instead of Dataflow

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Use Cloud Functions to transform data

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Use BigQuery Data Transfer Service

Question 15

A company uses Cloud Dataproc to run Spark jobs on ephemeral clusters. The input data is in Cloud Storage and output is also to Cloud Storage. The cluster is created and deleted daily. The cost is high due to spinning up nodes. Which change can reduce cost without sacrificing performance?

Accepted Answer

Use a combination of standard and preemptible VMs for worker nodes. Using a combination of standard and preemptible VMs for worker nodes reduces cost significantly while maintaining performance. Preemptible VMs are up to 80% cheaper than standard VMs, and since Spark is fault-tolerant and can handle node preemptions via speculative execution, the job can complete without performance degradation. Standard VMs for master nodes ensure cluster stability, while preemptible workers handle the bulk of data processing.

Answer

Use standard VMs with a larger number of smaller machines

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Use Cloud Dataflow instead

Answer

Use preemptible VMs for all nodes