The answer is to configure the endpoint with a target tracking scaling policy and pre-warm additional instances during expected traffic surges. This directly mitigates the latency spikes caused by the cold start time inherent in SageMaker’s scaling process, as pre-warming ensures that new instances are already loaded with your model and ready to serve requests before traffic hits. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding that auto scaling alone does not eliminate provisioning delay—you must combine it with proactive instance preparation to handle burst traffic without latency. A common trap is choosing to optimize the model or use larger instances, but those address compute efficiency, not the scaling delay itself. Remember the key trade-off: cost savings from auto scaling require pre-warming to avoid cold-start penalties. Memory tip: “Pre-warm before the storm” to recall that proactive instance hydration prevents latency spikes during traffic surges.
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.
A company is using SageMaker endpoints for inference. To reduce costs, they want to use Automatic Scaling. However, they observe that scaling up takes several minutes, causing latency spikes during traffic bursts. What should they do to mitigate this?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure the endpoint with a target tracking scaling policy and pre-warm additional instances during expected traffic surges.
Option A is correct because pre-warming the endpoint reduces cold start time. Option B (larger instances) might help but not cost-effective. Option C (optimizing model) reduces computational load but not scaling delay. Option D (scale down slowly) addresses scale-down, not up.
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.
✗
Optimize the model to reduce inference time.
Why it's wrong here
Faster inference can help but does not address scaling delay.
✗
Use larger instance types to handle more requests per instance.
Why it's wrong here
Larger instances may reduce per-instance load but not eliminate scaling latency.
✓
Configure the endpoint with a target tracking scaling policy and pre-warm additional instances during expected traffic surges.
Why this is correct
Pre-warming ensures instances are ready, minimizing cold start latency.
Related concept
Read the scenario before looking for a memorised answer.
✗
Set the endpoint to scale down slowly to maintain capacity.
Why it's wrong here
Slow scale-down helps during traffic drops, not bursts.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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 the endpoint with a target tracking scaling policy and pre-warm additional instances during expected traffic surges. — Option A is correct because pre-warming the endpoint reduces cold start time. Option B (larger instances) might help but not cost-effective. Option C (optimizing model) reduces computational load but not scaling delay. Option D (scale down slowly) addresses scale-down, not up.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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