Question 528 of 835
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MLA-C01 Practice Question: A media company uses SageMaker to host a…
A media company uses SageMaker to host a real-time video recommendation model. The model is deployed on a single ml.c5.xlarge endpoint. During a major live event, traffic surges to 10 times the normal load, and the endpoint becomes unresponsive, causing high latency and errors. The team had set up an Application Auto Scaling target tracking policy based on CPU utilization with a target of 70%. However, scaling did not trigger quickly enough. After the event, the team reviews CloudWatch metrics and notices that CPU utilization never exceeded 70% during the surge, but memory utilization peaked at 95%. The model is memory-bound. The team wants to ensure the endpoint scales automatically before performance degrades during future events. What should the team do?
⚠ Common exam trap
Many candidates assume CPU utilization is always the correct metric for scaling, but the question explicitly states the model is memory-bound, so the scaling policy must match the actual bottleneck to be effective.
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
✓
Change the target tracking metric to memory utilization and set a target of 70%
The model is memory-bound, and the current CPU-based target tracking policy failed to trigger scaling since CPU utilization never exceeded 70% during the surge. By switching to a memory utilization metric with a target of 70%, scaling will activate based on the actual resource constraint (memory), preventing performance degradation before the endpoint becomes unresponsive.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Change the target tracking metric to memory utilization and set a target of 70%
Why this is correct
Memory is the bottleneck; scaling on memory utilization will trigger before memory runs out.
- ✗
Increase the target CPU utilization to 90% so that scaling triggers at higher load
Why it's wrong here
CPU is not the bottleneck; increasing target won't help because CPU stays low.
- ✗
Change the endpoint instance type to ml.c5.4xlarge to provide more memory per instance
Why it's wrong here
Larger instance may handle surge but does not auto-scale; still risk if surge exceeds capacity.
- ✗
Create a scheduled scaling policy to add instances during the known event time
Why it's wrong here
Scheduled scaling requires prior knowledge; this event was unpredictable, so scheduled scaling won't help for unknown surges.
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Last reviewed: Jun 24, 2026
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.
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