- A
INFORMATION_SCHEMA tables (e.g., JOBS_BY_PROJECT)
INFORMATION_SCHEMA provides historical query performance and slot usage.
- B
Cloud Monitoring (Metrics like 'bigquery.googleapis.com/scheduler/slot_allocation')
Cloud Monitoring provides real-time and alerting on slot utilization.
- C
Cloud Logging (audit logs)
Why wrong: Audit logs track administrative actions, not performance metrics.
- D
bq command-line tool with the '--format=prettyjson' flag
Why wrong: The CLI is for administration, not monitoring.
- E
Cloud Profiler
Why wrong: Cloud Profiler is for application profiling, not BigQuery.
Question 511 of 1,000
BigQuery Performance Monitoring: Key Tools
This PCDE practice question tests your understanding of monitor and optimize database performance. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data engineering team wants to monitor BigQuery query performance and slot utilization. Which TWO tools or features should they use? (Choose two.)
Quick Answer
The answer is Cloud Monitoring and BigQuery’s INFORMATION_SCHEMA. Cloud Monitoring provides the essential metric `bigquery.googleapis.com/scheduler/slot_allocation`, which directly tracks slot utilization and allows you to set alerts for performance bottlenecks, while INFORMATION_SCHEMA views like `JOBS_BY_PROJECT` deliver detailed query statistics, execution timelines, and per-query slot consumption for deep analysis. On the Google Professional Cloud Database Engineer exam, this pairing tests your understanding that BigQuery performance monitoring requires both real-time infrastructure metrics (Cloud Monitoring) and historical query-level diagnostics (INFORMATION_SCHEMA), a common trap being to confuse Cloud Logging (audit trails) or Cloud Profiler (code profiling) with slot monitoring. Remember the mnemonic “Slots in the Monitor, Queries in the Schema” to keep the two distinct tools straight.
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
✓
INFORMATION_SCHEMA tables (e.g., JOBS_BY_PROJECT)
Option A is correct because INFORMATION_SCHEMA tables like `JOBS_BY_PROJECT` provide detailed metadata about query execution, including slot usage, job timing, and user-level performance metrics. This allows the team to analyze historical query performance and identify inefficiencies directly within BigQuery without external tools.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
INFORMATION_SCHEMA tables (e.g., JOBS_BY_PROJECT)
Why this is correct
INFORMATION_SCHEMA provides historical query performance and slot usage.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Cloud Monitoring (Metrics like 'bigquery.googleapis.com/scheduler/slot_allocation')
Why this is correct
Cloud Monitoring provides real-time and alerting on slot utilization.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cloud Logging (audit logs)
Why it's wrong here
Audit logs track administrative actions, not performance metrics.
- ✗
bq command-line tool with the '--format=prettyjson' flag
Why it's wrong here
The CLI is for administration, not monitoring.
- ✗
Cloud Profiler
Why it's wrong here
Cloud Profiler is for application profiling, not BigQuery.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often mistakenly choose audit logs for performance data because they conflate 'logging' with 'monitoring'. However, Cloud Logging audit logs focus on administrative activities, not detailed query performance metrics.
Detailed technical explanation
How to think about this question
BigQuery slot utilization is a key performance indicator that reflects the total compute capacity consumed by queries. The `INFORMATION_SCHEMA.JOBS_BY_PROJECT` view exposes `total_slot_ms` and `query_info` columns, enabling precise calculation of slot usage per job. Cloud Monitoring's `bigquery.googleapis.com/scheduler/slot_allocation` metric provides near real-time visibility into allocated versus available slots, which is critical for detecting contention in fixed-slot reservations or on-demand pricing models.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PCDE question test?
Monitor and optimize database performance — This question tests Monitor and optimize database performance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: INFORMATION_SCHEMA tables (e.g., JOBS_BY_PROJECT) — Option A is correct because INFORMATION_SCHEMA tables like `JOBS_BY_PROJECT` provide detailed metadata about query execution, including slot usage, job timing, and user-level performance metrics. This allows the team to analyze historical query performance and identify inefficiencies directly within BigQuery without external tools.
What should I do if I get this PCDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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