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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
Refer to the exhibit. A team observes that their SageMaker endpoint scales out quickly when load increases, but scales in very slowly when load decreases, causing over-provisioning. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
ScaleInCooldown is too high
The correct answer is C because a high ScaleInCooldown value causes the SageMaker endpoint to wait too long before initiating a scale-in event after load decreases. This delay prevents the endpoint from releasing resources promptly, leading to over-provisioning. In contrast, the scaling out behavior is unaffected by this cooldown, which explains why the endpoint scales out quickly but scales in slowly.
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.
✗
TargetValue is too high
Why it's wrong here
A high target value may cause under-provisioning, not slow scale-in.
✗
ScaleOutCooldown is too low
Why it's wrong here
A low ScaleOutCooldown makes scale-out faster, not slower scale-in.
✓
ScaleInCooldown is too high
Why this is correct
A high ScaleInCooldown delays scale-in responses.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Wrong predefined metric selected
Why it's wrong here
SageMakerVariantInvocationsPerInstance is a common metric for scaling.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse cooldown periods with scaling thresholds, assuming that slow scale-in is caused by a high TargetValue or wrong metric, rather than recognizing that cooldown timers directly control the delay between scaling actions.
Detailed technical explanation
How to think about this question
SageMaker uses Application Auto Scaling with cooldown periods that prevent new scaling activities from starting until the cooldown expires. The ScaleInCooldown (default 300 seconds) specifically controls how long after a scale-in activity the system waits before evaluating another scale-in. When this value is set too high, it creates a 'sticky' effect where the endpoint holds onto provisioned instances even after the load has dropped, leading to unnecessary costs. This is distinct from the ScaleOutCooldown, which governs the pace of adding 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.
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: ScaleInCooldown is too high — The correct answer is C because a high ScaleInCooldown value causes the SageMaker endpoint to wait too long before initiating a scale-in event after load decreases. This delay prevents the endpoint from releasing resources promptly, leading to over-provisioning. In contrast, the scaling out behavior is unaffected by this cooldown, which explains why the endpoint scales out quickly but scales in slowly.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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