- A
Upgrade Cloud SQL to a higher tier with more CPU.
Why wrong: This is costly and does not address the need to reduce database load.
- B
Increase the number of replicas in GKE to reduce load per pod.
Why wrong: This would increase load on Cloud SQL as more pods query it.
- C
Add read replicas to Cloud SQL.
Why wrong: Read replicas help with read scaling but not if the primary is overloaded with writes.
- D
Implement caching with Memorystore for frequently accessed data.
Caching reduces database read load, alleviating CPU pressure and latency.
Question 974 of 999
Designing highly scalable, available, and reliable cloud-native applications →hardMultiple ChoiceObjective-mapped
How to Reduce Cloud SQL CPU Load Using Memorystore Caching
This PCD practice question tests your understanding of designing highly scalable, available, and reliable cloud-native applications. 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 company runs a microservices application on Google Kubernetes Engine. They use Cloud SQL for persistent data. Recently, during a traffic spike, the application experienced increased latency and some requests failed with timeout errors. The team observed that the Cloud SQL CPU utilization spiked to 100%, and the GKE pods had high memory usage. They are using a standard Cloud SQL tier (db-n1-standard-2). Which course of action would best improve the application's performance and reliability?
Quick Answer
The answer is to implement caching with Memorystore for frequently accessed data. This is correct because the root cause is Cloud SQL CPU exhaustion from repeated read queries during a traffic spike, and in-memory caching offloads those queries, drastically reducing database CPU load and query latency. On the Google Professional Cloud Developer exam, this scenario tests your ability to diagnose performance bottlenecks in a GKE and Cloud SQL architecture, distinguishing between scaling the database vertically (which still hits the same CPU ceiling) and using a cache to absorb read-heavy traffic. A common trap is choosing read replicas, but replicas share the same CPU-intensive query patterns and don’t eliminate the primary bottleneck. Memory tip: “Cache the hot reads, not the hot CPU”—if the database CPU is pegged, think caching first.
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
✓
Implement caching with Memorystore for frequently accessed data.
The correct answer is D because the primary bottleneck is the Cloud SQL CPU spiking to 100% under heavy read traffic. Implementing Memorystore (Redis) caching offloads repeated read queries from the database, reducing CPU load and query latency. This directly addresses the root cause—database CPU exhaustion—without requiring a larger database instance or adding replicas that would still be limited by the same CPU.
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.
- ✗
Upgrade Cloud SQL to a higher tier with more CPU.
Why it's wrong here
This is costly and does not address the need to reduce database load.
- ✗
Increase the number of replicas in GKE to reduce load per pod.
Why it's wrong here
This would increase load on Cloud SQL as more pods query it.
- ✗
Add read replicas to Cloud SQL.
Why it's wrong here
Read replicas help with read scaling but not if the primary is overloaded with writes.
- ✓
Implement caching with Memorystore for frequently accessed data.
Why this is correct
Caching reduces database read load, alleviating CPU pressure and latency.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The PCD exam often tests the misconception that scaling compute (pods or database tier) is the only solution to performance issues, when in reality caching is a more cost-effective and architecturally sound approach for read-heavy workloads with spiky traffic patterns.
Detailed technical explanation
How to think about this question
Memorystore for Redis provides sub-millisecond in-memory data access, which is ideal for caching frequently accessed data like session state, product catalogs, or API responses. Under the hood, Redis uses a single-threaded event loop, so it excels at high-throughput, low-latency lookups. In a real-world scenario, a social media app experiencing a viral post could see thousands of identical queries per second; caching the post data in Redis reduces Cloud SQL CPU from 100% to under 30%, while also cutting p99 latency from 2 seconds to 5 milliseconds.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PCD question test?
Designing highly scalable, available, and reliable cloud-native applications — This question tests Designing highly scalable, available, and reliable cloud-native applications — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement caching with Memorystore for frequently accessed data. — The correct answer is D because the primary bottleneck is the Cloud SQL CPU spiking to 100% under heavy read traffic. Implementing Memorystore (Redis) caching offloads repeated read queries from the database, reducing CPU load and query latency. This directly addresses the root cause—database CPU exhaustion—without requiring a larger database instance or adding replicas that would still be limited by the same CPU.
What should I do if I get this PCD 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.
About these practice questions
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Last reviewed: Jun 25, 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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