Medium1 markMultiple Choice
Subtask 5.3: Cost OptimizationBigQueryCost OptimizationPartitioningQuotas

GCP PCA · Question 47 · Cost Optimization

Your data analytics team uses BigQuery extensively. The CFO has noticed that BigQuery costs have tripled in the last two months. Upon investigation, you find that analysts are running SELECT * queries on massive, multi-terabyte tables. Which TWO actions should you take to optimize costs without stopping the analysts from doing their work? (Select TWO)

Answer options:

A.

Revoke BigQuery access from the analysts.

B.

Enforce a maximum bytes billed limit at the project or user level.

C.

Switch BigQuery from on-demand pricing to flat-rate pricing immediately.

D.

Implement table partitioning and clustering, and require a partition filter on queries.

E.

Export the data to Cloud Storage and have analysts use Dataproc instead.

How to approach this question

Look for the BigQuery feature that physically reduces data scanned (Partitioning) and the administrative control that caps spending (Custom Quotas).

Full Answer

B,D
BigQuery on-demand pricing charges based on the amount of data scanned. `SELECT *` on a 10TB table scans 10TB. By partitioning the table (e.g., by day) and enabling the 'Require partition filter' option, analysts are forced to include a date range in their queries, reducing the scan to only a few gigabytes. Additionally, setting a 'maximum bytes billed' custom quota acts as a financial circuit breaker, stopping queries if an analyst exceeds their daily budget.

Common mistakes

Assuming flat-rate pricing (Option C) is always cheaper. If the team only runs a few massive queries a week, flat-rate will result in paying for idle capacity.

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