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Answer: Create a Cloud Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
The correct answer is B. You should create a Cloud Monitoring dashboard based on the BigQuery metric slots/allocated_for_project. This metric represents the number of BigQuery slots allocated for a project, which allows you to monitor slot usage within each project. Option A is incorrect because the query/scanned_bytes metric represents the number of bytes scanned by BigQuery queries, not the slot usage. Options C and D involve creating log exports and custom metrics, which are more complex and not as straightforward as using the slots/allocated_for_project metric.
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Each analytics team in your organization operates BigQuery jobs within their respective projects. As a data engineering lead, you want to empower each team to effectively monitor their own slot usage, ensuring efficient resource management and cost optimization. What steps should you take to enable each team to monitor slot usage within their projects?
A
Create a Cloud Monitoring dashboard based on the BigQuery metric query/scanned_bytes
B
Create a Cloud Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
C
Create a log export for each project, capture 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
Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Cloud Monitoring dashboard based on the custom metric
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