hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: An e-commerce company uses a multi-model endpoint…
An e-commerce company uses a multi-model endpoint on Amazon SageMaker to serve several deep learning models. After a new model version is deployed, the endpoint starts returning 503 errors for some models. Monitoring shows that the endpoint's memory utilization is near 100%. What should the team do to resolve this issue while minimizing operational overhead?
⚠ Common exam trap
Watch out — candidates often confuse SageMaker's built-in auto-scaling (which typically uses invocation-based metrics like request count) with the need for custom memory-based scaling, or mistakenly think Model Monitor can fix performance issues when it is only for monitoring data and model quality.
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
✓
Increase the number of instances for the endpoint and configure an auto-scaling policy based on memory utilization.
Increasing the number of instances and configuring an auto-scaling policy based on memory utilization directly addresses the root cause (memory exhaustion) by distributing the load across more instances. SageMaker's auto-scaling can use custom CloudWatch metrics (like memory utilization) to dynamically adjust capacity, which minimizes operational overhead by automating scaling without manual intervention.
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 instances for the endpoint and configure an auto-scaling policy based on memory utilization.
Why this is correct
Adds capacity and auto-scales.
- ✗
Deploy each model on its own separate endpoint to isolate memory usage.
Why it's wrong here
Increases complexity and cost.
- ✗
Use Amazon SageMaker Model Monitor to detect memory leaks and send alerts.
Why it's wrong here
Model Monitor is for data and model quality, not memory management.
- ✗
Use SageMaker's built-in model scaling feature to allocate more memory to the affected model.
Why it's wrong here
No such feature; scaling is at endpoint level.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.