- A
Reduce the maxmemory setting to force more aggressive eviction and free up memory.
Why wrong: Reducing maxmemory would increase eviction frequency, likely increasing CPU usage.
- B
Change the eviction policy to volatile-lru to prefer evicting keys with TTL.
Why wrong: Changing eviction policy may not reduce CPU usage; it might increase cache misses.
- C
Upgrade the Memorystore instance to a larger size (e.g., M5 with 60 GB) to provide more CPU and memory resources.
A larger instance has more CPU cores and memory, reducing pressure.
- D
Implement Redis monitoring with Cloud Monitoring and set alerts to notify when CPU exceeds 80%.
Why wrong: Monitoring is important but does not solve the current performance issue.
Question 1,000 of 1,000
Reducing Redis CPU Utilization by Upgrading Instance
This PCDE practice question tests your understanding of monitor and optimize database performance. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 social media application uses Memorystore for Redis to cache user profiles and session data. Recently, the application experienced intermittent errors and high latency. You observe that the Redis CPU utilization is consistently above 90% and the cache hit ratio is 85%. The instance type is a Standard tier M2 (30 GB) with a maxmemory setting of 25 GB. The eviction policy is allkeys-lru. The number of keys is 10 million with an average value size of 2 KB. You suspect memory pressure is causing CPU spikes. What should you do to reduce CPU utilization and improve performance?
Quick Answer
The correct answer is to upgrade the Memorystore instance to a larger size, such as an M5 with 60 GB, because this directly addresses the root cause of high Redis CPU utilization by providing more CPU cores and memory bandwidth to handle the workload. When CPU is consistently above 90% with a 85% cache hit ratio and memory pressure from a 25 GB maxmemory limit on a 30 GB instance, the eviction policy (allkeys-lru) is constantly running, consuming CPU cycles to remove keys and causing performance degradation. On the Google Professional Cloud Database Engineer exam, this scenario tests your understanding that scaling vertically is the immediate operational fix for resource contention, while traps like changing the eviction policy to volatile-lru would worsen cache misses without reducing CPU load. A key memory tip: when CPU is high and hit ratio is decent, think "scale up, not tune down"—upgrading instance tier provides both memory and compute relief simultaneously.
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
✓
Upgrade the Memorystore instance to a larger size (e.g., M5 with 60 GB) to provide more CPU and memory resources.
Option C is correct because upgrading to a larger instance (e.g., M5 with 60 GB) directly addresses both the high CPU utilization and memory pressure. The M2 instance is CPU-bound with sustained >90% usage, and the 85% cache hit ratio indicates that evictions are already occurring under the allkeys-lru policy, causing CPU spikes from eviction overhead and key lookups. A larger instance provides more CPU cores and memory, reducing eviction frequency and allowing the cache to serve more requests without thrashing.
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.
- ✗
Reduce the maxmemory setting to force more aggressive eviction and free up memory.
Why it's wrong here
Reducing maxmemory would increase eviction frequency, likely increasing CPU usage.
- ✗
Change the eviction policy to volatile-lru to prefer evicting keys with TTL.
Why it's wrong here
Changing eviction policy may not reduce CPU usage; it might increase cache misses.
- ✓
Upgrade the Memorystore instance to a larger size (e.g., M5 with 60 GB) to provide more CPU and memory resources.
Why this is correct
A larger instance has more CPU cores and memory, reducing pressure.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Implement Redis monitoring with Cloud Monitoring and set alerts to notify when CPU exceeds 80%.
Why it's wrong here
Monitoring is important but does not solve the current performance issue.
Common exam traps
Common exam trap: answer the scenario, not the keyword
In the Google PCDE exam, a common trap is the misconception that tuning eviction policies or reducing memory limits can solve CPU pressure, when in fact the root cause is resource exhaustion that requires vertical scaling.
Detailed technical explanation
How to think about this question
Under the hood, Redis uses a single-threaded event loop for command processing, so CPU spikes often stem from O(n) operations like eviction scans under memory pressure. With allkeys-lru, Redis samples a subset of keys (default 5) per eviction, and with 10 million keys, the sampling overhead multiplies under high eviction rates. In real-world scenarios, upgrading instance size not only adds CPU cores (Redis can leverage multiple cores for background tasks like I/O and replication) but also increases the maxmemory ceiling, reducing eviction frequency and improving cache hit ratio beyond the current 85%.
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
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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: Upgrade the Memorystore instance to a larger size (e.g., M5 with 60 GB) to provide more CPU and memory resources. — Option C is correct because upgrading to a larger instance (e.g., M5 with 60 GB) directly addresses both the high CPU utilization and memory pressure. The M2 instance is CPU-bound with sustained >90% usage, and the 85% cache hit ratio indicates that evictions are already occurring under the allkeys-lru policy, causing CPU spikes from eviction overhead and key lookups. A larger instance provides more CPU cores and memory, reducing eviction frequency and allowing the cache to serve more requests without thrashing.
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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