The answer is to increase the maximum memory size of the instance. When the evicted_keys metric spikes during SET operations, it signals that the Redis instance has hit its maxmemory limit and is aggressively evicting keys to free space for new writes, which directly causes the observed latency spikes. This scenario tests your understanding of Memorystore for Redis memory management under the Google Professional Cloud Database Engineer exam, where a common trap is to mistakenly tune the eviction policy instead of addressing the capacity shortage—changing the policy only shifts which keys are removed, not the underlying eviction overhead. A key memory tip is to remember that eviction itself is the latency culprit, not the policy; always scale memory first when evictions coincide with write spikes.
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.
You are managing a Memorystore for Redis cluster with standard tier (persistence disabled). The application experiences occasional latency spikes while performing SET operations. You observe that the 'evicted_keys' metric spikes during the spikes. What is the most effective solution?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the maximum memory size of the instance
The evicted_keys metric spikes during SET operations indicate that the Redis instance has reached its maxmemory limit and is evicting keys to accommodate new writes. Increasing the maximum memory size directly addresses the root cause by providing more headroom for data, reducing the need for eviction and the associated latency spikes.
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.
✗
Enable AOF persistence with fsync every second
Why it's wrong here
Persistence increases write latency and doesn't prevent eviction.
More memory reduces evictions, stabilizing write latency.
Related concept
Read the scenario before looking for a memorised answer.
✗
Configure a read replica to offload read traffic
Why it's wrong here
Read replicas don't help with SET operations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that changing the eviction policy (Option B) solves memory pressure, when in fact the policy only controls which keys are evicted, not whether eviction occurs at all.
Detailed technical explanation
How to think about this question
Redis uses an LRU approximation algorithm for eviction when maxmemory is reached; eviction itself is a blocking operation that can cause latency spikes. The evicted_keys metric increments per evicted key, and in a Memorystore for Redis standard tier (no persistence), the entire dataset resides in memory, making memory sizing critical. Increasing maxmemory is the most direct mitigation, as it reduces the frequency of eviction cycles and the associated CPU 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 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Increase the maximum memory size of the instance — The evicted_keys metric spikes during SET operations indicate that the Redis instance has reached its maxmemory limit and is evicting keys to accommodate new writes. Increasing the maximum memory size directly addresses the root cause by providing more headroom for data, reducing the need for eviction and the associated latency spikes.
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.
About these practice questions
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Variation 1. A Cloud Memorystore for Redis instance used as a session store has a high eviction rate. Which configuration change can reduce evictions while maintaining performance?
medium
A.Enable persistence (RDB)
B.Increase number of replicas
C.Decrease timeout
✓ D.Set maxmemory-policy to allkeys-lru
Why D: Option D is correct because setting `maxmemory-policy` to `allkeys-lru` allows Redis to evict the least recently used keys across all keys when memory is full, which directly reduces eviction rates by ensuring that only the least active session data is removed. This maintains performance by keeping frequently accessed session keys in memory, which is critical for a session store where active sessions are repeatedly read and written.
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Question Discussion
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