AWS Certified CloudOps Engineer Associate SOA-C03 Practice Question
An RDS for PostgreSQL running on a db.t3.medium instance shows sustained high DB load. Performance Insights issues a proactive recommendation stating that the CPU wait dimension is saturated. Which modification best follows the recommendation to increase performance efficiency?
Enable storage autoscaling and double the gp2 volume size.
Scale the instance to a larger class such as db.m6g.large.
Create a read replica in another Availability Zone for analytic traffic.
Turn on automatic minor version upgrades to apply the latest patch.
When CPU is identified as the dominant wait dimension, Performance Insights proactive recommendations advise adding compute capacity. Scaling the DB instance class to a larger size (for example, moving from a burstable db.t3.medium to a compute-optimized or general-purpose db.m6g.large) directly increases available vCPUs and memory, reducing CPU saturation. Enlarging storage, creating replicas, or applying patches can help other bottlenecks but do not address immediate CPU exhaustion flagged by the recommendation.
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What is CPU wait dimension in RDS Performance Insights?
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How do instance classes like db.t3.medium or db.m6g.large differ?
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Why does scaling the instance improve performance for CPU saturation?
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What is the difference between burstable and general-purpose DB instance classes in AWS RDS?
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What does the 'CPU wait dimension is saturated' mean in Performance Insights?
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What is the role of Storage Autoscaling in AWS RDS, and why doesn't it solve CPU saturation?
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AWS Certified CloudOps Engineer Associate SOA-C03
Monitoring, Logging, Analysis, Remediation, and Performance Optimization
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