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Question 762 of 1,616

Troubleshooting and Optimization →mediumMultiple ChoiceObjective-mapped

Quick Answer

The answer is to scale up to a larger node type like cache.t3.medium. This is correct because high evictions and a low cache hit rate in ElastiCache Redis directly indicate that the node’s memory is exhausted, forcing Redis to remove keys under its eviction policy to make room for new writes. When data is evicted, subsequent requests miss the cache, driving down the hit rate and increasing latency as the application falls back to the primary database. On the AWS Certified Developer Associate DVA-C02 exam, this scenario tests your understanding that scaling vertically—not horizontally with more nodes—is the immediate fix when a single-node cluster runs out of memory. A common trap is to add read replicas or enable clustering, but those address read scaling or sharding, not memory pressure. Remember the memory tip: “Evictions equal exhaustion—scale up the size, not the count.”

DVA-C02 Troubleshooting and Optimization Practice Question

This DVA-C02 practice question tests your understanding of troubleshooting and optimization. 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.

A developer is troubleshooting an application that uses Amazon ElastiCache for Redis to improve performance. The application periodically experiences high latency during peak hours. The developer checks the ElastiCache metrics and sees that the 'Evictions' metric is consistently high and the 'CacheHitRate' metric is low. The cluster has a single node with a cache.t3.small instance type. Which action will most likely improve the cache hit rate and reduce latency?

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.

Question 1mediummultiple choice

Full question →

A
Scale up to a larger node type (e.g., cache.t3.medium) to increase available memory.
Increasing memory reduces the need for evictions, allowing more data to remain in cache, which improves the cache hit rate and reduces latency.
B
Enable cluster mode and distribute data across multiple shards to reduce memory pressure.
Why wrong: Cluster mode can help distribute memory, but it is more complex. For a single-node cluster, scaling up is the more direct solution. However, this option is plausible, but the most likely first step is to scale up the instance.
C
Change the eviction policy to 'allkeys-lfu' to better manage which keys are evicted.
Why wrong: Changing the eviction policy can improve the quality of evictions (e.g., keep frequently used keys), but if the memory is full, evictions will still occur. This may help the hit rate but not as effectively as adding memory.
D
Add a read replica for the Redis cluster to offload read traffic.
Why wrong: Read replicas help with read throughput but do not increase memory capacity. Evictions occur on the primary node, so a replica does not solve the memory shortage.

Full breakdown with real-world context →

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

✓

Scale up to a larger node type (e.g., cache.t3.medium) to increase available memory.

The high 'Evictions' and low 'CacheHitRate' metrics indicate that the Redis node is running out of memory, forcing it to evict keys to make room for new data. Scaling up to a larger node type (cache.t3.medium) increases the available memory, allowing more data to be cached and reducing evictions, which directly improves the cache hit rate and reduces latency.

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.

✓
Scale up to a larger node type (e.g., cache.t3.medium) to increase available memory.
Why this is correct
Increasing memory reduces the need for evictions, allowing more data to remain in cache, which improves the cache hit rate and reduces latency.
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 cluster mode and distribute data across multiple shards to reduce memory pressure.
Why it's wrong here
Cluster mode can help distribute memory, but it is more complex. For a single-node cluster, scaling up is the more direct solution. However, this option is plausible, but the most likely first step is to scale up the instance.
✗
Change the eviction policy to 'allkeys-lfu' to better manage which keys are evicted.
Why it's wrong here
Changing the eviction policy can improve the quality of evictions (e.g., keep frequently used keys), but if the memory is full, evictions will still occur. This may help the hit rate but not as effectively as adding memory.
✗
Add a read replica for the Redis cluster to offload read traffic.
Why it's wrong here
Read replicas help with read throughput but do not increase memory capacity. Evictions occur on the primary node, so a replica does not solve the memory shortage.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates may focus on optimizing eviction policies or adding replicas, but the core issue is insufficient memory capacity, which only scaling up can resolve.

Detailed technical explanation

How to think about this question

Under the hood, Redis uses an LRU (Least Recently Used) eviction policy by default when memory is full; the 'Evictions' metric counts how many keys are removed per minute. A low 'CacheHitRate' (e.g., below 80%) often correlates with high evictions because frequently accessed keys are being removed. Scaling up to a larger instance type like cache.t3.medium (from 0.5 GiB to 1 GiB memory) provides more headroom, reducing the frequency of evictions and retaining hot keys in memory.

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

An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.

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.

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FAQ

Questions learners often ask

What does this DVA-C02 question test?

Troubleshooting and Optimization — This question tests Troubleshooting and Optimization — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Scale up to a larger node type (e.g., cache.t3.medium) to increase available memory. — The high 'Evictions' and low 'CacheHitRate' metrics indicate that the Redis node is running out of memory, forcing it to evict keys to make room for new data. Scaling up to a larger node type (cache.t3.medium) increases the available memory, allowing more data to be cached and reducing evictions, which directly improves the cache hit rate and reduces latency.

What should I do if I get this DVA-C02 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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Same concept, more angles

1 more ways this is tested on DVA-C02

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. A developer is troubleshooting performance issues in an application that uses Amazon ElastiCache for Redis. The application experiences periodic latency spikes during peak hours. The developer checks CloudWatch metrics and sees that the 'Evictions' metric is consistently high and the 'CacheHitRate' metric is low. The cluster uses a single cache.t3.small node. Which action will most likely improve the cache hit rate and reduce latency?

hard

A.Increase the number of replicas
B.Enable cluster mode and add more shards
C.Increase the TTL of cached items
✓ D.Use a larger instance type

Why D: The correct answer is D because the symptoms—high evictions and low cache hit rate—indicate that the single cache.t3.small node is running out of memory. Using a larger instance type increases the available memory, allowing more data to be cached, reducing evictions, and improving the cache hit rate. This directly addresses the root cause of memory pressure without changing the cluster architecture or data expiration behavior.

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