- A
Define a scaling policy using Application Auto Scaling for the SageMaker endpoint variant.
Auto Scaling policies adjust capacity based on CloudWatch metrics.
- B
Set up an Amazon CloudWatch alarm to trigger scaling based on the InvocationsPerInstance metric.
This alarm triggers the scaling policy when utilization is high or low.
- C
Enable SageMaker Model Monitor to detect data drift.
Why wrong: Model Monitor is for monitoring data and concept drift, not scaling.
- D
Configure a multi-model endpoint to serve multiple models.
Why wrong: Multi-model endpoints host multiple models but do not automatically scale based on traffic.
- E
Use SageMaker batch transform to handle variable traffic.
Why wrong: Batch transform is for asynchronous processing, not real-time scaling.
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 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.)
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.
Option A is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing 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.
Detailed technical explanation
How to think about this question
Application Auto Scaling for SageMaker endpoints uses the AWS Application Auto Scaling service with a target tracking scaling policy that maintains a target value for the InvocationsPerInstance metric (e.g., 1000 invocations per instance). The scaling cooldown period (default 300 seconds) prevents rapid fluctuations. Under the hood, CloudWatch alarms evaluate the metric every minute, and when the threshold is breached, Auto Scaling adjusts the desired instance count for the endpoint variant, which can take 2–5 minutes to provision new instances.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Deployment and Orchestration of ML Workflows — study guide chapter
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Define a scaling policy using Application Auto Scaling for the SageMaker endpoint variant. — Option A is correct because 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.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
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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