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MLA-C01 Practice Question: Deploy a trained model to a SageMaker endpoint…
A company wants to deploy a trained model to a SageMaker endpoint with automatic scaling based on traffic. Which TWO configurations are required? (Choose two.)
⚠ Common exam trap
A common mix-up: candidates confuse 'required configurations for scaling' with 'optional features that improve monitoring or cost efficiency,' leading them to select data capture or multi-model endpoints instead of recognizing that a CloudWatch alarm is the trigger mechanism for the scaling policy.
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
✓
Set up an Application Auto Scaling policy
Application Auto Scaling is the AWS service that automatically adjusts the number of instances for a SageMaker endpoint based on demand. You define a scaling policy (e.g., target tracking, step scaling) that tells Auto Scaling when to add or remove instances, which is essential for handling variable traffic 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.
- ✗
Use a multi-model endpoint
Why it's wrong here
Multi-model endpoint is not required for auto scaling.
- ✗
Enable data capture
Why it's wrong here
Data capture is for logging, not scaling.
- ✓
Set up an Application Auto Scaling policy
Why this is correct
Auto Scaling policy defines how to scale the endpoint.
- ✗
Configure a lifecycle configuration
Why it's wrong here
Lifecycle configuration is for notebook instances, not endpoints.
- ✓
Create a CloudWatch alarm
Why this is correct
CloudWatch alarm triggers the scaling policy based on a metric.
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JA
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.