This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
SageMaker Endpoint Autoscaling
Endpoint Autoscaling automatically adjusts the number of instances based on demand.
SageMaker Debugger
Why wrong: Debugger monitors training jobs for anomalies.
SageMaker Model Registry
Why wrong: Model Registry is for managing model versions, not scaling.
SageMaker Pipelines
Why wrong: Pipelines are for orchestrating ML workflows.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
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.
Common exam traps
The trap here is that candidates may 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.
Detailed technical explanation
Under the hood, SageMaker Endpoint Autoscaling leverages AWS Application Auto Scaling with a target tracking scaling policy, which dynamically adjusts the desired instance count based on a predefined CloudWatch metric (e.g., SageMakerVariantInvocationsPerInstance). A real-world scenario where this matters is handling unpredictable traffic spikes during a product launch, where the endpoint scales up to maintain low latency and scales down to zero during idle periods to minimize costs.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
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FAQ
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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.
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