easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A company has a trained machine learning model…
A company has a trained machine learning model that needs to be deployed as a real-time inference endpoint on Amazon SageMaker. The endpoint must automatically scale based on incoming traffic. Which SageMaker feature should be used?
⚠ Common exam trap
Candidates often confuse SageMaker Elastic Inference (which accelerates inference) with auto scaling, or they assume Batch Transform can be used for real-time endpoints, but only Endpoint Auto Scaling directly manages dynamic instance count based on traffic.
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
✓
SageMaker Endpoint Auto Scaling
Amazon SageMaker Endpoint Auto Scaling is the correct feature because it automatically adjusts the number of instances serving a real-time inference endpoint based on the incoming traffic load. It uses Application Auto Scaling policies, which monitor CloudWatch metrics (e.g., InvocationsPerInstance) to scale in or out, ensuring low latency and cost efficiency 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.
- ✓
SageMaker Endpoint Auto Scaling
Why this is correct
Auto Scaling automatically adjusts the instance count based on configured policies to handle traffic changes.
- ✗
SageMaker Elastic Inference
Why it's wrong here
Elastic Inference attaches GPU acceleration to instances but does not scale the endpoint.
- ✗
SageMaker Batch Transform
Why it's wrong here
Batch Transform processes entire datasets asynchronously in a single job, not as a persistent endpoint that can scale in real time to fluctuating traffic. It is tempting because it handles large-scale inference efficiently when latency is not critical, and would be correct for offline predictions on a static dataset where a real-time endpoint is unnecessary.
- ✗
SageMaker Model Monitor
Why it's wrong here
Model Monitor is for monitoring inference data and model quality, not scaling.
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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.