- A
Use the INFORMATION_SCHEMA.INDEXES view to identify and drop unused or redundant secondary indexes.
Unused indexes cause extra write CPU and storage; removing them reduces CPU utilization directly.
- B
Adjust the application to use staleness of 5 seconds for reads to reduce CPU for read-write transactions.
Why wrong: This reduces read CPU but may not significantly impact overall CPU if writes dominate; also, it affects data freshness.
- C
Increase the number of nodes in the Spanner instance to spread the CPU load.
Why wrong: Adding nodes increases cost and may not address the root cause; it's a scale-up solution but not the first diagnostic step.
- D
Create a separate read-only replica pool to offload read traffic.
Why wrong: Spanner does not have read replicas in the same way; reading from followers is possible but not a separate pool.
Quick Answer
The answer is to use the INFORMATION_SCHEMA.INDEXES view to identify and drop unused or redundant secondary indexes. This is correct because high CPU utilization in Cloud Spanner is often driven by the write amplification caused by maintaining secondary indexes during each transaction; every index update adds CPU overhead, so removing indexes that are not used by your efficient queries directly reduces the per-transaction CPU cost. On the Google Professional Cloud Database Engineer exam, this scenario tests your understanding that OLTP performance tuning in Spanner frequently involves balancing read performance against write overhead, and a common trap is to focus on query optimization when the real culprit is index maintenance. Remember the memory tip: “If queries are lean, check the indexes in between”—meaning when your queries are already efficient, the hidden CPU drain is likely from index writes, not reads.
PCDE Monitor and optimize database performance Practice Question
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 Cloud Spanner instance is experiencing high CPU utilization (above 80%) on multiple nodes. The database is used for an e-commerce application with a high volume of read-write transactions. The application uses the googlesql dialect and runs typical OLTP queries. You have already reviewed the query performance and found that most queries are efficient. Which initial step should you take to reduce CPU utilization?
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 the INFORMATION_SCHEMA.INDEXES view to identify and drop unused or redundant secondary indexes.
Option A is correct because high CPU utilization in Cloud Spanner often stems from excessive secondary index maintenance during write operations. Dropping unused or redundant indexes reduces the write amplification and CPU overhead per transaction, directly lowering CPU usage without compromising query performance, as the queries are already efficient.
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.
- ✓
Use the INFORMATION_SCHEMA.INDEXES view to identify and drop unused or redundant secondary indexes.
Why this is correct
Unused indexes cause extra write CPU and storage; removing them reduces CPU utilization directly.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Adjust the application to use staleness of 5 seconds for reads to reduce CPU for read-write transactions.
Why it's wrong here
This reduces read CPU but may not significantly impact overall CPU if writes dominate; also, it affects data freshness.
- ✗
Increase the number of nodes in the Spanner instance to spread the CPU load.
Why it's wrong here
Adding nodes increases cost and may not address the root cause; it's a scale-up solution but not the first diagnostic step.
- ✗
Create a separate read-only replica pool to offload read traffic.
Why it's wrong here
Spanner does not have read replicas in the same way; reading from followers is possible but not a separate pool.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that scaling out (adding nodes) is the first step for performance issues, when in reality, eliminating unnecessary index maintenance is a more cost-effective and direct solution for CPU-bound write-heavy workloads.
Detailed technical explanation
How to think about this question
Cloud Spanner uses a distributed storage engine where each secondary index is a separate table that must be updated synchronously on every write. In OLTP workloads, even efficient queries can cause high CPU if many indexes exist, as each write triggers index maintenance across nodes. The INFORMATION_SCHEMA.INDEXES view reveals index usage statistics, allowing you to identify indexes with zero or low reads that still incur write overhead.
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
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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: Use the INFORMATION_SCHEMA.INDEXES view to identify and drop unused or redundant secondary indexes. — Option A is correct because high CPU utilization in Cloud Spanner often stems from excessive secondary index maintenance during write operations. Dropping unused or redundant indexes reduces the write amplification and CPU overhead per transaction, directly lowering CPU usage without compromising query performance, as the queries are already efficient.
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: Jun 30, 2026
This PCDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PCDE exam.
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