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Answer: Develop a Cloud Monitoring dashboard utilizing the BigQuery metric slots/allocated_for_project.
The correct answer is A. Developing a Cloud Monitoring dashboard based on the BigQuery metric slots/allocated_for_project allows each team to monitor the number of slots allocated to their project and their current usage, providing clear visibility into their slot utilization. This method is efficient and directly addresses the need for slot usage monitoring. Option B is incorrect as it focuses on monitoring data volume rather than slot usage. Option C, while feasible, is inefficient due to the need for creating custom metrics for each project, potentially leading to a cumbersome number of metrics. Option D is also inefficient, as aggregating logs at the organization level can complicate the separate monitoring of slot usage for individual projects. Reference: [BigQuery Reservations Monitoring](https://cloud.google.com/bigquery/docs/reservations-monitoring#viewing-slot-usage)
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What is the most efficient method for enabling analytics teams to monitor their BigQuery slot usage within their respective projects?
A
Develop a Cloud Monitoring dashboard utilizing the BigQuery metric slots/allocated_for_project.
B
Build a Cloud Monitoring dashboard using the BigQuery metric query/scanned_bytes.
C
Export a log for each project, collect the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Cloud Monitoring dashboard based on the custom metric.
D
Export an aggregated log at the organization level, collect the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and build a Cloud Monitoring dashboard based on the custom metric.
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