easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data science team has trained a model using…
A data science team has trained a model using SageMaker and wants to deploy it to a production endpoint with automatic scaling based on request volume. Which SageMaker feature should they use to configure scaling?
⚠ Common exam trap
Candidates often confuse SageMaker Debugger (a training debugger) or SageMaker Pipelines (a workflow tool) with scaling features, when only Endpoint Autoscaling directly manages production instance count based on request volume.
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 Autoscaling
SageMaker Endpoint Autoscaling is the correct feature because it automatically adjusts the number of instances behind a SageMaker hosted endpoint based on a target metric (e.g., requests per minute, CPU utilization) using Application Auto Scaling. This allows the endpoint to handle varying request volumes without manual intervention, ensuring cost efficiency and performance.
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 Autoscaling
Why this is correct
Endpoint Autoscaling automatically adjusts the number of instances based on demand.
- ✗
SageMaker Debugger
Why it's wrong here
Debugger monitors training jobs for anomalies.
- ✗
SageMaker Model Registry
Why it's wrong here
Model Registry is for managing model versions, not scaling.
- ✗
SageMaker Pipelines
Why it's wrong here
Pipelines are for orchestrating ML workflows.
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Senior Network & Security Engineer · founder of Courseiva
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