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Explanation:

The correct answer is B. Exporting BigQuery data access logs to BigQuery allows you to analyze queries in real-time, which helps you identify the most costly queries and which users are responsible for them. Option A is not feasible because the Billing page does not provide real-time query-level details. Option C is unnecessarily complex and involves additional steps that are not required for this task. Option D is useful for a broad overview but does not provide the timely granularity needed for real-time monitoring and analysis of individual queries and users.

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Your company uses BigQuery on Google Cloud for data warehousing on a pay-per-use basis. Managing costs and optimizing resource usage are important to you. Specifically, you need to monitor queries in real-time to identify which queries are the most costly and determine which users are generating the highest costs. What should you do?

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Last updated: April 6, 2026 at 14:02

In the BigQuery dataset that contains all the tables to be queried, add a label for each user that can launch a query. 2. Open the Billing page of the project. 3. Select Reports. 4. Select BigQuery as the product and filter by the user you want to check.

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Create a Cloud Logging sink to export BigQuery data access logs to BigQuery. 2. Perform a BigQuery query on the generated table to extract the information you need.

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Create a Cloud Logging sink to export BigQuery data access logs to Cloud Storage. 2. Develop a Dataflow pipeline to compute the cost of queries split by users.

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Activate billing export into BigQuery. 2. Perform a BigQuery query on the billing table to extract the information you need.

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