PDE bq command Practice Question
A data engineer notices that BigQuery queries are slower than expected. They want to identify the most expensive stages in the query execution. Which tool or command should they use?
⚠ Common exam trap
Candidates often think EXPLAIN can identify expensive stages, but EXPLAIN only shows the query plan structure without execution cost data; for runtime expense analysis, the bq command with job details is required.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use bq query --format=prettyjson and look at statistics
To identify expensive stages in BigQuery query execution, you should run the query with the bq command-line tool using the --format=prettyjson flag. This returns a detailed job object that includes a query plan with stages, each having cost estimates like 'avg execution time' and 'peak memory'. Analyzing these stages reveals the most resource-intensive parts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use bq show to view job statistics
Why it's wrong here
bq show displays job metadata but not detailed per-stage cost breakdown.
- ✓
Use bq query --format=prettyjson and look at statistics
Why this is correct
The bq query command with --format=prettyjson returns job details including query plan stages and their costs, enabling identification of most expensive stages.
- ✗
Use Cloud Monitoring to view query execution graphs
Why it's wrong here
Cloud Monitoring provides aggregated metrics and graphs, but not the detailed per-stage query plan for a specific job.
- ✗
Use EXPLAIN statement in BigQuery
Why it's wrong here
BigQuery does not support the EXPLAIN statement. Query plan information is obtained through job statistics.
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