The answer is to change the target tracking metric to memory utilization with a target of 70%. This is correct because the model is memory-bound, meaning memory pressure is the actual bottleneck, not CPU. The original CPU-based policy failed to trigger scaling during the traffic surge because CPU utilization stayed below the 70% threshold, even as memory hit 95%, causing the endpoint to become unresponsive. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding that Application Auto Scaling policies must align with the actual resource constraint—a common trap is assuming CPU is always the right metric for scaling. For memory-bound models, always prioritize memory utilization as the scaling trigger. Memory tip: "Match the metric to the bottleneck—if memory maxes out, scale by memory, not by clout."
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 media company uses SageMaker to host a real-time video recommendation model. The model is deployed on a single ml.c5.xlarge endpoint. During a major live event, traffic surges to 10 times the normal load, and the endpoint becomes unresponsive, causing high latency and errors. The team had set up an Application Auto Scaling target tracking policy based on CPU utilization with a target of 70%. However, scaling did not trigger quickly enough. After the event, the team reviews CloudWatch metrics and notices that CPU utilization never exceeded 70% during the surge, but memory utilization peaked at 95%. The model is memory-bound. The team wants to ensure the endpoint scales automatically before performance degrades during future events. What should the team do?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "never"
Why it matters: Absolute qualifier. True only if the statement has zero exceptions — be cautious of options that seem obvious but break down in edge cases.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Change the target tracking metric to memory utilization and set a target of 70%
Option A is correct because the model is memory-bound, and the current CPU-based target tracking policy failed to trigger scaling since CPU utilization never exceeded 70% during the surge. By switching to a memory utilization metric with a target of 70%, scaling will activate based on the actual resource constraint (memory), preventing performance degradation before the endpoint becomes unresponsive.
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.
✓
Change the target tracking metric to memory utilization and set a target of 70%
Why this is correct
Memory is the bottleneck; scaling on memory utilization will trigger before memory runs out.
Clue confirmation
The clue word "never" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the target CPU utilization to 90% so that scaling triggers at higher load
Why it's wrong here
CPU is not the bottleneck; increasing target won't help because CPU stays low.
✗
Change the endpoint instance type to ml.c5.4xlarge to provide more memory per instance
Why it's wrong here
Larger instance may handle surge but does not auto-scale; still risk if surge exceeds capacity.
✗
Create a scheduled scaling policy to add instances during the known event time
Why it's wrong here
Scheduled scaling requires prior knowledge; this event was unpredictable, so scheduled scaling won't help for unknown surges.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume CPU utilization is always the correct metric for scaling, but the question explicitly states the model is memory-bound, so the scaling policy must match the actual bottleneck to be effective.
Detailed technical explanation
How to think about this question
Application Auto Scaling target tracking policies use CloudWatch metrics to maintain a target value; for memory-bound workloads, the custom memory utilization metric must be published via the CloudWatch agent or a custom script, as SageMaker endpoints do not natively expose memory metrics. Under the hood, the scaling cooldown period (default 300 seconds) can delay scale-out actions, so setting a lower target (e.g., 70%) provides a buffer before memory hits critical levels. In real-world scenarios, combining a memory-based target tracking policy with a step scaling policy for rapid response can further reduce latency during sudden spikes.
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.
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: Change the target tracking metric to memory utilization and set a target of 70% — Option A is correct because the model is memory-bound, and the current CPU-based target tracking policy failed to trigger scaling since CPU utilization never exceeded 70% during the surge. By switching to a memory utilization metric with a target of 70%, scaling will activate based on the actual resource constraint (memory), preventing performance degradation before the endpoint becomes unresponsive.
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: "never". Absolute qualifier. True only if the statement has zero exceptions — be cautious of options that seem obvious but break down in edge cases.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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