The answer is to increase the maxmemory setting to restore the cache hit ratio. When the cache hit ratio drops from 95% to 70%, it signals that Redis is aggressively evicting frequently accessed keys under memory pressure, forcing the application to fetch data from the slower backend more often. By raising the maxmemory limit, you allow Redis to store more entries, directly reducing evictions and preserving the hot data that drives high hit ratios. On the Google Professional Cloud Database Engineer exam, this scenario tests your understanding of Redis eviction policies and capacity planning for Memorystore instances; a common trap is to immediately tweak the eviction policy (e.g., switching to allkeys-lru) when the root cause is simply insufficient memory allocation. Remember, no eviction policy can save a cache that is too small for its working set. Memory tip: think of maxmemory as the cache’s “room size”—if the room is too small, even the best bouncer (eviction policy) will have to kick out your most loyal customers (hot keys).
PCDE Monitor and optimize database performance Practice Question
This PCDE practice question tests your understanding of monitor and optimize database performance. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 monitoring a Memorystore for Redis instance serving as a cache for an e-commerce application. The cache hit ratio has dropped from 95% to 70%. Which action is most likely to restore the hit ratio?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the maxmemory setting to accommodate more cache entries.
A drop in cache hit ratio from 95% to 70% indicates that the cache is evicting frequently accessed keys to make room for new ones. Increasing the maxmemory setting allows Redis to store more entries, reducing evictions and restoring the hit ratio. This is the most direct way to address the capacity issue without changing application behavior.
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 network latency between the application and Redis by moving them to the same zone.
Why it's wrong here
Network latency does not affect cache hit ratio.
✗
Change the eviction policy to 'noeviction' to prevent key removal.
Why it's wrong here
'noeviction' causes writes to fail when memory is full, leading to errors.
✓
Increase the maxmemory setting to accommodate more cache entries.
Why this is correct
Larger memory reduces evictions, improving hit ratio.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Enable AOF persistence to ensure data survives restarts.
Why it's wrong here
Persistence does not affect hit ratio; it affects data durability.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse eviction policy changes (like 'noeviction') with capacity increases, or assume that persistence or network optimization can fix a hit ratio problem caused by insufficient memory.
Detailed technical explanation
How to think about this question
Redis uses an LRU (or LFU, depending on configuration) eviction algorithm when memory usage reaches the maxmemory limit. Increasing maxmemory gives Redis more room to retain frequently accessed keys, directly improving the hit ratio. The eviction policy (e.g., allkeys-lru) determines which keys are removed; changing maxmemory is the capacity knob, while eviction policy controls the selection algorithm.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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 maxmemory setting to accommodate more cache entries. — A drop in cache hit ratio from 95% to 70% indicates that the cache is evicting frequently accessed keys to make room for new ones. Increasing the maxmemory setting allows Redis to store more entries, reducing evictions and restoring the hit ratio. This is the most direct way to address the capacity issue without changing application behavior.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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