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Troubleshooting and OptimizationhardMultiple ChoiceObjective-mapped

DVA-C02 Troubleshooting and Optimization Practice Question

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?

⚠ Common exam trap

Many exam-takers confuse scaling out (adding replicas or shards) with scaling up (increasing instance size), but for a single-node cluster suffering from memory exhaustion, the most direct and effective solution is to increase memory capacity, not to add replicas or change the cluster mode.

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

Use a larger instance type

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.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Increase the number of replicas

    Why it's wrong here

    Increasing the number of replicas in an ElastiCache for Redis cluster primarily enhances read scalability and provides high availability through data redundancy. However, replicas maintain an exact copy of the primary node's data. If the primary node is experiencing memory pressure and evicting keys, its replicas will mirror this state or replicate the evictions, as they do not contribute additional aggregate memory capacity for storing new unique data beyond the primary's limits.

  • Enable cluster mode and add more shards

    Why it's wrong here

    Enabling cluster mode and adding more shards does increase the total available memory by distributing data across multiple independent primary nodes. However, this architectural change requires significant application modifications to support Redis Cluster's sharding logic, such as using a cluster-aware client to correctly route requests based on key hash slots. It is a complex operational and development effort, not a simple configuration adjustment for immediate memory relief.

  • Increase the TTL of cached items

    Why it's wrong here

    Increasing the Time-To-Live (TTL) for cached items will cause data to persist in memory for longer durations. If the cache is already experiencing evictions due to insufficient memory, extending the TTL will exacerbate the problem by consuming available memory for extended periods with existing items. This leaves even less space for new data, potentially leading to more frequent and aggressive evictions as the cache quickly reaches its maxmemory limit.

  • Use a larger instance type

    Why this is correct

    Upgrading to a larger instance type directly addresses memory constraints by providing a substantial increase in the node's available RAM. This vertical scaling approach immediately expands the cache's capacity, allowing more items to be stored without triggering eviction policies. It is a straightforward, non-disruptive operational change for ElastiCache, often involving a brief failover, and effectively resolves memory-related performance issues without requiring any application code modifications.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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