Reducing RDS MySQL High CPU for Write-Heavy Workloads

DEA-C01 By islever Editorial Last updated Oct 12, 2026

Automate data processing by using AWS services.

Short answer

AD Use Performance Insights to identify problematic queries and upgrade the instance size to handle the write load.

Question

A company uses Amazon RDS for MySQL as the database for a critical application. The database workload is mostly writes, with a small number of reads. A data engineer notices that the CPU utilization of the DB instance is very high. The high CPU utilization is slowing down the application. The data engineer must reduce the CPU utilization of the DB Instance. Which actions should the data engineer take to meet this requirement? (Choose two.)

Use the Performance Insights feature of Amazon RDS to identify queries that have high CPU utilization. Optimize the problematic queries. Correct Answer
Modify the database schema to include additional tables and indexes.
Reboot the RDS DB instance once each week.
Upgrade to a larger instance size. Correct Answer
Implement caching to reduce the database query load.

Key Takeaways

The question tests diagnosing and resolving database performance bottlenecks, with the common trap being the selection of caching (E) which primarily benefits read-heavy workloads.

This page explains how to reduce high CPU utilization on an Amazon RDS for MySQL instance with a write-heavy workload, establishing that A and D are the correct actions.

Common mistake: Many candidates incorrectly choose E (Implement caching) because it is a common database optimization, but caching does not effectively reduce CPU load for write-dominant transactions.

Expert Analysis

Why the Answer Is Correct

To address high CPU utilization in a write-heavy RDS MySQL environment, you must first identify the root cause and then scale resources. Option A is correct because AWS documentation explicitly recommends using Performance Insights to identify queries causing high load before optimizing them; this is the standard diagnostic step. Option D is correct because if the workload exceeds the current instance's compute capacity, upgrading to a larger instance size provides the necessary CPU power to handle the increased write volume.

Why the Other Options Are Wrong

Option B is incorrect because modifying schema without data indicates speculative changes that may not address the specific query causing the spike. Option C is invalid as rebooting does not solve persistent performance issues and causes downtime. Option E is incorrect because caching (like ElastiCache) reduces read latency and load, but since the workload is mostly writes, caching has minimal impact on the CPU utilization driven by write operations.

Community Comment Notes

Community consensus strongly supports AD, with users noting that caching does not solve write-heavy issues. One user referenced the official AWS knowledge center article on high CPU, confirming that diagnosis via Performance Insights is the recommended first step. Another commenter emphasized that options improving read performance should be eliminated given the write-dominant nature of the workload.

Official Reference

https://repost.aws/knowledge-center/rds-instance-high-cpu

This link opens the official vendor documentation in a new tab.

Exam Strategy

Always align your solution with the specific workload characteristics described (e.g., write-heavy vs. read-heavy). For performance issues, prioritize diagnosis (Performance Insights) and scaling (instance upgrade) over speculative schema changes or irrelevant optimizations like caching.

Frequently Asked Questions

Why is caching not effective for write-heavy RDS workloads?

Caching stores frequently accessed data to reduce database reads. Since write-heavy workloads generate most traffic through insert/update operations, caching cannot significantly offload the CPU burden from these transactional processes.

Is upgrading the instance always the best first step?

No. You should always diagnose first using Performance Insights (Option A) to ensure the issue isn't just inefficient queries. Upgrading (Option D) is the corrective action if the workload legitimately exceeds the current instance's capacity after optimization.

More DEA-C01 FAQ →

Community Votes

76%

24%

76% of anonymous learners picked answer AD. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 7 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 7

kj07 👍 10

Here the issue is with the writes and caching will not solve them. I will go with A and D.
lucas_rfsb 👍 9

I will go for A and D, since other options are more likely to improve read performance issues.
GiorgioGss 👍 5

Since the questions states that "the database workload is mostly writes" let's eliminate the options that improves the reads.
fceb2c1 👍 5

A and D. For A it is mentioned here https://repost.aws/knowledge-center/rds-instance-high-cpu
damaldon 👍 4

Ans. AE A) Use Amazon RDS Performance Insights to identify the query that's responsible for the database load. Check the SQL tab that corresponds to a particular timeframe. E) If there's a query that's repeatedly running, use prepared statements to lower the pressure on your CPU. Repeated running of prepared statements caches the query plan. Because the plan is already in cache for further runs, the time for planning is much less. https://repost.aws/knowledge-center/rds-aurora-postgresql-high-cpu

Show 2 more comments

sdas1 👍 2

A and D With a workload that is mostly writes and a small number of reads, caching will not be as effective in reducing CPU utilization compared to read-heavy workloads. https://repost.aws/knowledge-center/rds-aurora-postgresql-high-cpu
michele_scar 👍 2

With A you should understand why the CPU is in high loading. B is mentioned in the last phrase of A (optimizing). Remain valid only D

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