- A
Increase the endpoint's invocation timeout to 120 seconds
Why wrong: Timeout does not address capacity; requests may still fail due to overload.
- B
Deploy the model on a SageMaker batch transform job
Why wrong: Batch transform is for offline predictions, not real-time endpoints.
- C
Configure auto-scaling for the endpoint with a target tracking policy
Auto-scaling adds instances during high load and removes them when traffic subsides, reducing errors cost-effectively.
- D
Create a new endpoint with multiple instances and use weighted routing
Why wrong: Overprovisioning is not cost-effective; auto-scaling is better.
Question 30 of 1,000
MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance, and security. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 scientist notices that a SageMaker endpoint is returning HTTP 5XX errors under high load. The endpoint uses a single ml.m5.large instance. The team wants to reduce these errors without changing the instance type. What is the most cost-effective step?
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
✓
Configure auto-scaling for the endpoint with a target tracking policy
Option C is correct because configuring auto-scaling with a target tracking policy allows the endpoint to dynamically add more instances under high load, distributing the traffic and reducing HTTP 5XX errors. Since the team cannot change the instance type, scaling out is the most cost-effective way to handle increased demand, as it only adds capacity when needed and avoids over-provisioning.
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.
- ✗
Increase the endpoint's invocation timeout to 120 seconds
Why it's wrong here
Timeout does not address capacity; requests may still fail due to overload.
- ✗
Deploy the model on a SageMaker batch transform job
Why it's wrong here
Batch transform is for offline predictions, not real-time endpoints.
- ✓
Configure auto-scaling for the endpoint with a target tracking policy
Why this is correct
Auto-scaling adds instances during high load and removes them when traffic subsides, reducing errors cost-effectively.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Create a new endpoint with multiple instances and use weighted routing
Why it's wrong here
Overprovisioning is not cost-effective; auto-scaling is better.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think increasing the timeout (Option A) or using batch transform (Option B) can solve real-time load issues, but these options do not address the fundamental need for horizontal scaling under high concurrency.
Detailed technical explanation
How to think about this question
SageMaker auto-scaling uses Application Auto Scaling with a target tracking policy based on a predefined metric, such as 'SageMakerVariantInvocationsPerInstance', which maintains a target value (e.g., 1000 invocations per instance). Under the hood, the policy triggers a scale-out event when the metric exceeds the target, adding instances gradually to handle spikes, and scales in during low traffic to reduce costs. A real-world scenario is a retail recommendation endpoint during a flash sale, where auto-scaling prevents 5XX errors without requiring manual intervention or over-provisioning.
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
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
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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ML Solution Monitoring, Maintenance, and Security — study guide chapter
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
ML Solution Monitoring, Maintenance, and Security — This question tests ML Solution Monitoring, Maintenance, and Security — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Configure auto-scaling for the endpoint with a target tracking policy — Option C is correct because configuring auto-scaling with a target tracking policy allows the endpoint to dynamically add more instances under high load, distributing the traffic and reducing HTTP 5XX errors. Since the team cannot change the instance type, scaling out is the most cost-effective way to handle increased demand, as it only adds capacity when needed and avoids over-provisioning.
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: Jul 4, 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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