Quick Answer
The answer is to increase the TTL (time-to-live) for cached data. A low cache hit ratio in Cloud Memorystore for Redis means most requests miss the cache and fall back to the primary database, often because cached entries expire or are evicted too quickly. By extending the TTL, you keep valid data in Redis longer, reducing evictions and directly improving the cache hit ratio as more requests find their data already stored. On the Google Professional Cloud Database Engineer exam, this scenario tests your understanding of caching fundamentals and Redis memory management; a common trap is to assume adding more nodes or increasing memory size is the fix, but those address capacity, not data retention. The core insight is that a low hit ratio signals premature expiration, not insufficient storage. Remember the mnemonic: “TTL up, hit ratio up”—time is the lever, not space.
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 using Cloud Memorystore for Redis as a caching layer. You notice that cache hit ratio is below 50%. What is the best action to improve it?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Flush the cache periodically to remove stale data.
Why wrong: Flushing reduces hit ratio.
Increase the TTL (time-to-live) for cached data.
Longer TTL keeps data in cache for more reads.
Enable persistence to avoid data loss.
Why wrong: Persistence does not affect cache hits.
Increase the instance memory size.
Why wrong: More memory may not improve hit ratio if TTL is short.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Increase the TTL (time-to-live) for cached data.
A low cache hit ratio indicates that a large proportion of requests are not finding their data in the cache, forcing the application to fetch from the primary database. Increasing the TTL (time-to-live) for cached data keeps valid entries in Redis longer, reducing the frequency of evictions and cache misses. This directly improves the hit ratio by ensuring that more requests can be served from the cache before the data expires.
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.
Flush the cache periodically to remove stale data.
Why it's wrong here
Flushing reduces hit ratio.
Increase the TTL (time-to-live) for cached data.
Why this is correct
Longer TTL keeps data in cache for more reads.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Enable persistence to avoid data loss.
Why it's wrong here
Persistence does not affect cache hits.
Increase the instance memory size.
Why it's wrong here
More memory may not improve hit ratio if TTL is short.
Common exam traps
Google Cloud often tests the misconception that a low cache hit ratio is always a memory capacity problem, leading candidates to choose 'increase memory size' when the real issue is data expiring too quickly due to short TTLs.
Detailed technical explanation
Redis uses an LRU (Least Recently Used) eviction policy when memory is full, but a low hit ratio often stems from short TTLs causing premature expiration rather than memory pressure. By extending the TTL, you allow frequently accessed keys to remain in memory longer, which is especially effective for read-heavy workloads with stable access patterns. In practice, you should also monitor the eviction count and maxmemory usage to ensure the TTL increase does not cause memory exhaustion.
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
Monitor and optimize database performance — This question tests Monitor and optimize database performance — Read the scenario before looking for a memorised answer..
The correct answer is: Increase the TTL (time-to-live) for cached data. — A low cache hit ratio indicates that a large proportion of requests are not finding their data in the cache, forcing the application to fetch from the primary database. Increasing the TTL (time-to-live) for cached data keeps valid entries in Redis longer, reducing the frequency of evictions and cache misses. This directly improves the hit ratio by ensuring that more requests can be served from the cache before the data expires.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This PCDE 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 PCDE exam.
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