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MLA-C01 Practice Question: A machine learning engineer is deploying a model…
A machine learning engineer is deploying a model using SageMaker and needs to ensure that the endpoint can automatically scale based on traffic patterns. Which TWO actions should the engineer take? (Choose two.)
⚠ Common exam trap
A common mix-up: candidates confuse monitoring and scaling: candidates often pick Model Monitor (Option C) because it sounds like it monitors traffic, but it is for data drift, not scaling; similarly, batch transform (Option E) is mistaken for a scaling solution when it is a separate inference mode.
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
✓
Define a scaling policy using Application Auto Scaling for the SageMaker endpoint variant.
SageMaker endpoints use Application Auto Scaling to automatically adjust the number of instances based on traffic. You define a scaling policy (e.g., target tracking, step scaling) that references a CloudWatch metric. Option B is correct because the InvocationsPerInstance metric is a standard SageMaker endpoint metric that reflects the load per instance, and a CloudWatch alarm on this metric can trigger the scaling policy to add or remove instances as traffic changes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Define a scaling policy using Application Auto Scaling for the SageMaker endpoint variant.
Why this is correct
Auto Scaling policies adjust capacity based on CloudWatch metrics.
- ✓
Set up an Amazon CloudWatch alarm to trigger scaling based on the InvocationsPerInstance metric.
Why this is correct
This alarm triggers the scaling policy when utilization is high or low.
- ✗
Enable SageMaker Model Monitor to detect data drift.
Why it's wrong here
Model Monitor is for monitoring data and concept drift, not scaling.
- ✗
Configure a multi-model endpoint to serve multiple models.
Why it's wrong here
Multi-model endpoints host multiple models but do not automatically scale based on traffic.
- ✗
Use SageMaker batch transform to handle variable traffic.
Why it's wrong here
Batch transform is for asynchronous processing, not real-time 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.