Quick Answer
The answer is to optimize the database query and add appropriate indexes because Cloud Trace has pinpointed the database as the root cause of the latency. When a service is shown waiting on a database query, the bottleneck is not the network or compute resources but the data retrieval itself, so optimizing the query logic and ensuring proper indexes directly reduces the time spent on that operation. On the Google Professional Cloud Developer exam, this scenario tests your ability to interpret Cloud Trace spans and apply database performance tuning rather than jumping to scale infrastructure. A common trap is to assume the solution is to increase GKE pod resources or add caching, but Cloud Trace explicitly shows the service waiting on the database, meaning the fix must target the query execution. Remember the memory tip: "Trace the wait, then index the gate" — if Cloud Trace shows a database wait, your first action is to examine and index the query.
This PCD practice question tests your understanding of managing application performance monitoring. 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.
An application running on GKE is experiencing high latency. The team uses Cloud Trace to identify the bottleneck. They notice that a particular service spends most of its time waiting on a database query. How can they optimize performance?
Decrease the number of pods to reduce load
Why wrong: Reducing pods may increase latency due to less capacity.
Use Cloud CDN to cache database results
Why wrong: Cloud CDN caches static content, not database responses.
Optimize the database query and add appropriate indexes
Query optimization reduces execution time.
Increase the number of replicas for the service
Why wrong: Scaling replicas doesn't speed up slow queries.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Optimize the database query and add appropriate indexes
Option C is correct because the bottleneck is identified as a database query causing high latency. Optimizing the query and adding appropriate indexes directly reduces the time spent waiting on the database, which is the root cause. Cloud Trace shows the service is waiting on the database, so improving database performance is the most effective solution.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Decrease the number of pods to reduce load
Why it's wrong here
Reducing pods may increase latency due to less capacity.
Use Cloud CDN to cache database results
Why it's wrong here
Cloud CDN caches static content, not database responses.
Optimize the database query and add appropriate indexes
Why this is correct
Query optimization reduces execution time.
Related concept
Read the scenario before looking for a memorised answer.
Increase the number of replicas for the service
Why it's wrong here
Scaling replicas doesn't speed up slow queries.
Common exam traps
Google Cloud often tests the misconception that scaling horizontally (adding replicas) solves all performance issues, but here the bottleneck is external to the service (database), so scaling the service does not reduce the per-query wait time.
Detailed technical explanation
Database query optimization often involves using EXPLAIN plans to identify full table scans, then adding composite indexes to match query filter and sort patterns. In GKE, the database connection pool size and query timeout settings also affect perceived latency; even with optimized queries, a misconfigured connection pool can cause queuing. Real-world scenarios include N+1 query problems in ORMs where adding eager loading or batch fetching reduces round trips.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Managing application performance monitoring — This question tests Managing application performance monitoring — Read the scenario before looking for a memorised answer..
The correct answer is: Optimize the database query and add appropriate indexes — Option C is correct because the bottleneck is identified as a database query causing high latency. Optimizing the query and adding appropriate indexes directly reduces the time spent waiting on the database, which is the root cause. Cloud Trace shows the service is waiting on the database, so improving database performance is the most effective solution.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This PCD 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 PCD exam.
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