The answer is implementing scheduled scaling to add capacity ahead of known flash sales. This is correct because reactive scaling policies, such as those based on average CPU utilization, suffer from a cold-start delay where new instances take several minutes to become available, even when the scaling trigger fires correctly—as seen here with CPU utilization never exceeding 70% yet requests still timing out. Scheduled scaling proactively pre-warms the endpoint by adjusting the desired instance count before the traffic spike hits, eliminating that latency gap entirely. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of the difference between dynamic and predictive scaling strategies, with a common trap being to assume that increasing the scaling threshold or switching to a different metric like memory utilization would solve the cold-start problem. Remember the key distinction: reactive scaling responds to a problem already in progress, while scheduled scaling prevents the problem from occurring. A useful memory tip is “schedule before the spike, not after the sigh.”
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. 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 company operates an e-commerce platform that uses a machine learning model to recommend products to users. The model is deployed on an Amazon SageMaker endpoint with automatic scaling enabled based on average CPU utilization. The model was trained on historical data and is updated weekly. Recently, the platform experienced a flash sale event that caused a sudden spike in traffic. During the event, the endpoint's latency increased dramatically, and many requests timed out. After the event, the team reviews the CloudWatch metrics and notices that the CPU utilization never exceeded 70%, and the scaling policy was triggered but instances took several minutes to become available. The team wants to prevent similar issues in future flash sales. Which course of action would be MOST effective?
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
✓
Implement scheduled scaling to add capacity ahead of known flash sales.
Option D is correct because scheduled scaling allows you to proactively add capacity ahead of known traffic events like flash sales, eliminating the cold-start delay that occurs when reactive scaling policies (like those based on CPU utilization) must launch new instances. During the flash sale, the scaling policy was triggered but instances took minutes to become available, causing timeouts; scheduled scaling pre-warms the endpoint by adjusting the desired instance count before the traffic spike hits.
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.
✗
Use predictive scaling based on historical traffic patterns.
Why it's wrong here
Predictive scaling requires long-term data and may not capture flash sales accurately.
✗
Lower the CPU utilization threshold for the scaling policy to 40%.
Why it's wrong here
Lowering threshold might cause premature scaling but does not solve the delay in instance provisioning.
✗
Switch to larger instance types to handle higher CPU loads.
Why it's wrong here
Larger instances may still have provisioning delays and increase cost.
✓
Implement scheduled scaling to add capacity ahead of known flash sales.
The clue word "never" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume reactive scaling (lowering thresholds or using predictive scaling) can handle sudden spikes, but the exam tests your understanding that provisioning latency is the bottleneck, and only proactive scheduled scaling can eliminate that delay for known events.
Detailed technical explanation
How to think about this question
SageMaker endpoints use Auto Scaling groups behind the scenes, and when a scaling policy triggers a scale-out event, the new instances must go through provisioning steps: downloading the model artifacts, loading the container, and initializing the inference code — this can take 2–5 minutes or more. Scheduled scaling works by calling the Application Auto Scaling API (e.g., put-scheduled-action) to set a specific desired capacity at a given time, effectively pre-warming the endpoint so that when the traffic hits, the instances are already serving requests. This is especially critical for flash sales where traffic can increase by 10x or more within seconds, far outpacing the reaction time of reactive scaling.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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: Implement scheduled scaling to add capacity ahead of known flash sales. — Option D is correct because scheduled scaling allows you to proactively add capacity ahead of known traffic events like flash sales, eliminating the cold-start delay that occurs when reactive scaling policies (like those based on CPU utilization) must launch new instances. During the flash sale, the scaling policy was triggered but instances took minutes to become available, causing timeouts; scheduled scaling pre-warms the endpoint by adjusting the desired instance count before the traffic spike hits.
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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Question Discussion
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