PDE practice questions 331–340

1. Question 331 · 340 in bank

   You monitor and optimize the BigQuery instance for your team. You notice that a particular daily report that uses a large JOIN operation is consistently slow. You want to examine the query's execution plan to identify potential performance bottlenecks within the JOIN as quickly as possible. What should you do?

   • AUse the bg query --dry_run command to review the estimated number of bytes read and review query syntax.
   • BRun a query on the INFORMATION_SCHEMA.JOBS_BY_PROJECT view filtering by the job_ID and analyze total_bytes_processed.
   • CReview the BigQuery audit logs in Cloud Logging.
   • DLeverage BigQuery's Query History view and analyze the execution graph.

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   Answer: D

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2. Question 332 · 340 in bank

   Your company stores vital operational sales data in a BigQuery dataset in us-central1. Your company requires a disaster recovery plan to restore this data to us-east1 with a recovery point objective (RPO) of 24 hours and a recovery time objective (RTO) of 4 hours if us-central1 experiences an outage. You need to implement the disaster recovery plan while keeping costs and complexity to a minimum. What should you do?

   • ASet up continuous queries and Pub/Sub to stream data changes from BigQuery tables in us-central1 to us-east1.
   • BManually export data to a CSV file in a multi-regional Cloud Storage bucket daily and use bq load to restore to us-east1.
   • CConfigure BigQuery cross-region dataset replication from ns-central1 to us-east1.
   • DTake daily BigQuery table snapshots in us-central1.

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   Answer: C

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3. Question 333 · 340 in bank

   You are developing a fraud detection model using BigQuery ML. You have a raw transaction dataset and need to create new features such as the average_transaction_amount_last_24_hours and time_since_last_transaction. These features require aggregation and time-window calculations on the existing data. The goal is to ensure that these features are consistently applied during both model training and prediction without manual intervention. You need to prepare these features efficiently for your model. What should you do?

   • AImplement a Cloud Run function that triggers on new transactions, calculates the features, and inserts them into a feature store before model serving.
   • BExport the BigQuery data to Cloud Storage, perform feature engineering using a custom Python script in a Dataflow job, and then re-import the engineered features into BigQuery.
   • CUse the TRANSFORM clause within the CREATE MODEL statement, leveraging SQL functions for aggregations and time-based calculations.
   • DCreate a separate BigQuery table containing pre-computed features using complex SQL queries and join this table with the raw data during model training and serving.

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   Answer: C

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4. Question 334 · 340 in bank

   Your organization has thousands of images stored in Cloud Storage. The organization's data analysts need to categorize these images upon analyzing the image content and store the results in a structured format for integration with other BigQuery datasets. You need a solution that allows the analysts to perform this task with the least amount of coding. What should you do?

   • AUse the Gemini 2.5 Pro model in Vertex AI to analyze the images and store the results in BigQuery.
   • BUse the ML.UNDERSTAND_TEXT function along with object tables to analyze the images, and store the JSON output in a Cloud Storage bucket.
   • CUse Vertex AI Vision API to analyze the images and store the results in BigQuery.
   • DUse the ML.GENERATE_TEXT function along with object tables to analyze the images and store the JSON output in a BigQuery table.

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   Answer: C

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5. Question 335 · 340 in bank

   You are building a system to process financial transactions. The system must handle a high throughput of concurrent user operations and each operation requires low-latency reads and writes to individual records. You need to identify a storage solution that guarantees ACID compliance for the processed transactions. You want a Google Cloud managed service. What should you do?

   • ASelect Bigtable.
   • BSelect BigQuery.
   • CSelect Cloud Storage.
   • DSelect Spanner.

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   Answer: D

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6. Question 336 · 340 in bank

   You are preparing data to serve a sales demand prediction model. The training data undergoes several pre-processing steps, including scaling numerical features and one-hot encoding categorical features. The model is deployed on Vertex AI Endpoints. You need to prevent training-serving skew and ensure accurate predictions in production. You want a solution that is easy to implement. What should you do?

   • AImplement a custom handler within the Vertex AI Endpoint to automatically perform data transformations before the model makes a prediction.
   • BReplicate the exact same pre-processing logic in the inference pipeline that was used during model training.
   • CStore the raw, unprocessed data in a separate Cloud Storage bucket exclusively for serving.
   • DEnsure the serving data is a smaller, random sample of the training data.

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   Answer: B

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7. Question 337 · 340 in bank

   Your retail company is concerned about their BigQuery analytics spend. The company runs several queries that require the use of the same aggregation for the store ID and real-time sales volume. You need to implement the optimal solution that minimizes analytics spend and returns faster results. What should you do?

   • ACreate a new table from a CSV file with the repeated aggregation for the other queries to reference for faster processing.
   • BCreate a materialized view to minimize repetitive computations.
   • CUse join acceleration with primary and foreign keys to increase query joining to live data.
   • DLeverage partitioning to minimize the number of bytes read.

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   Answer: B

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8. Question 338 · 340 in bank

   You are defining the data governance strategy for a new BigQuery table with medical and financial data. You want a scalable solution that ensures the clinical researchers can access patient medical data without financial information, while allowing the accounting team to access only financial data with minimal patient identifiers. What should you do?

   • AImplement column-level security policies in BigQuery tables with IAM permissions.
   • BCreate separate tables for personally identifiable information (PII), financial data, and anonymized medical data. Use IAM permissions to control access to each table.
   • CImplement row-level security policies in BigQuery tables with IAM permissions.
   • DCreate separate datasets with authorized views exposing only approved data.

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   Answer: D

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9. Question 339 · 340 in bank

   You are designing a data lake on Google Cloud to store vast amounts of customer interaction data from various sources, such as websites, mobile apps, and social media. You need to ensure that this data, which arrives in different formats, is consistently cataloged and easy for data analysts to discover and use. You also want to perform basic data quality checks and transformations before the data is consumed by downstream applications. You need an automated and managed data governance solution. What should you do?

   • AUse Cloud Storage as the central repository. Use Vertex AI to classify and process the data and perform data quality checks.
   • BStream all the data directly into BigQuery, where it is automatically cataloged and governed.
   • CUse Cloud Storage and BigQuery as repositories. Use Dataplex Universal Catalog for metadata discovery, data quality checks, and transformations.
   • DUse Cloud Storage as the central repository. Use a Cloud Run function to catalog, transform the data, and perform data quality checks.

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   Answer: C

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10. Question 340 · 340 in bank

    You are using Dataflow to build a streaming data pipeline to analyze user website click activity from Pub/Sub. You need to calculate the number of clicks for each user site visit. A site visit is defined as a period of activity followed by 30 minutes of inactivity for a specific user. What should you do?

    • AUse tumbling windows with a 30-minute window.
    • BUse hopping windows with a 30-minute window, and a 1-minute period.
    • CUse hopping windows with a 30-minute window, and a 30-minute period.
    • DUse session windows with a 30-minute gap duration.

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    Answer: D

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PDE practice, question 331. 340 questions available.