A media platform runs a CPU-heavy thumbnail generation workload on an EC2 Auto Scaling group using t3.large instances. During peak traffic, p95 processing time increases significantly even though average CPU remains around 40–50%. CloudWatch also shows CPU credit depletion behavior. Which change will most directly improve performance predictability for this workload?

Increase the t3.large maximum CPU credits and keep the Auto Scaling group using the same burstable instance type.

Increasing credits can reduce throttling for short or intermittent bursts, but t3 instances still depend on accumulated CPU credits. During sustained peak traffic, credits will deplete and the instance can throttle, so p95 latency predictability remains limited.

Add a placement group to the existing t3.large instances so they are packed close together for lower latency between nodes.

Placement groups can help certain network-oriented communication patterns by improving latency and/or bandwidth, but the observed symptom is CPU credit depletion on burstable instances. Changing placement does not address CPU throttling caused by credit exhaustion.

Switch the workload to run on Lambda with the same logic so invocations automatically scale without instance selection changes.

Lambda may scale automatically, but provisioned and on-demand execution characteristics do not inherently remove the underlying issue you observed (burstable CPU throttling on t3). For CPU-bound, latency-sensitive batch-like processing, moving to Lambda could introduce other constraints (cold starts, execution limits, concurrency dynamics) and does not directly target credit-based throttling as the root cause.

What does this SAA-C03 question test?

Static NAT maps one inside address to one outside address.

What is the correct answer to this question?

The correct answer is: Change the Auto Scaling group instance type to a compute-optimized family (for example, c7i) to provide steady CPU performance. — The combination of increasing p95 latency and CloudWatch CPU credit depletion indicates burstable t3 instances are throttling during sustained load. The most direct way to improve performance predictability is to move off the credit-based burst model to an instance family that provides steady CPU capacity (for example, a compute-optimized family like c7i). This addresses the bottleneck without requiring application-level logic changes. (A) Credits can delay throttling, but during sustained peaks the burst-credit model still depletes and latency can degrade again. (C) Placement affects network placement, not the instance CPU-credit throttling that is driving the p95 behavior. (D) Lambda scaling does not directly address the burst-credit limitation seen with t3 instances and may introduce different performance behaviors (for example, cold starts or concurrency-related effects).

What should I do if I get this SAA-C03 question wrong?

Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.