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Answer: Implement a Cloud Monitoring dashboard using the BigQuery metric `slots/allocated_for_project` to monitor slot allocation per project.
The correct approach is **B**. Utilizing the `slots/allocated_for_project` metric in a Cloud Monitoring dashboard directly provides each team with visibility into their project's slot usage, offering a straightforward method to track allocations. - **Option A** focuses on data scanned by queries, not slot usage. - **Option C** and **D** involve creating custom metrics from `totalSlotMs`, which, while possible, are more complex and less direct than using the dedicated `slots/allocated_for_project` metric. Additionally, **D** aggregates data at the organization level, which doesn't serve the goal of project-specific monitoring.
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To enable each analytics team in your organization to monitor their BigQuery slot usage within their respective projects, what is the most effective approach?
A
Develop a Cloud Monitoring dashboard utilizing the BigQuery metric query/scanned_bytes to track data scanned by queries.
B
Implement a Cloud Monitoring dashboard using the BigQuery metric slots/allocated_for_project to monitor slot allocation per project.
C
For each project, export logs, capture BigQuery job execution details, create a custom metric from totalSlotMs, and then build a Cloud Monitoring dashboard based on this metric.
D
Aggregate logs at the organization level, capture BigQuery job executions, derive a custom metric from totalSlotMs, and establish a Cloud Monitoring dashboard from this metric.
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