The correct approach is to pre-warm the SageMaker endpoint by setting a minimum number of instances that can handle the expected peak load before the flash sale. This is necessary because the existing auto-scaling policy is reactive, relying on a five-minute evaluation period for average latency, which is far too slow to absorb a sudden traffic spike—by the time the policy triggers, the endpoint is already overwhelmed and returning HTTP 503 errors. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of SageMaker endpoint scaling behavior and the critical distinction between reactive auto-scaling and proactive capacity planning. A common trap is to assume that increasing instance size or changing the algorithm will solve latency issues, but the core problem here is the time lag in scaling response, not the model’s inference speed. Remember the key insight: pre-warming is like opening extra checkout lanes before a sale starts—you need capacity ready, not just a plan to add it later. A useful memory tip is “pre-warm, not re-arm” to emphasize proactive versus reactive scaling.
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 company operates a real-time fraud detection system using an Amazon SageMaker endpoint. The model is a gradient boosting model trained on historical transaction data. The endpoint is deployed on an ml.c5.2xlarge instance with auto-scaling enabled based on average latency. Recently, during a flash sale event, the endpoint started returning HTTP 503 errors. The CloudWatch metrics show that the CPU utilization is at 70%, and the average latency has increased from 50 ms to 200 ms. The auto-scaling policy is configured to add one instance when average latency exceeds 100 ms for 5 consecutive minutes, and remove one instance when latency drops below 50 ms for 5 minutes. The current number of instances is 2. The flash sale lasted 30 minutes. What should the company do to prevent this issue in future flash sales?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Pre-warm the endpoint by setting a minimum number of instances that can handle the expected peak load before the flash sale
Option C is correct because the auto-scaling policy is reactive and too slow (5-minute evaluation period) to handle rapid traffic spikes. Pre-warming the endpoint by increasing the number of instances before the flash sale ensures capacity is available. Option A (increase instance size) may help but is more expensive and still reactive. Option B (use a different algorithm) is not the core issue. Option D (enable throttling) would still result in errors.
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.
✗
Enable request throttling to drop excess requests
Why it's wrong here
Throttling still results in errors for dropped requests.
✗
Change the instance type to ml.c5.4xlarge to handle higher load
Why it's wrong here
This increases capacity but still relies on reactive auto-scaling during the event.
✓
Pre-warm the endpoint by setting a minimum number of instances that can handle the expected peak load before the flash sale
Why this is correct
This ensures capacity is available from the start.
Related concept
Read the scenario before looking for a memorised answer.
✗
Change the model to a simpler model with lower latency
Why it's wrong here
Model complexity is not the cause; the issue is scaling speed.
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 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 MLS-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.
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Pre-warm the endpoint by setting a minimum number of instances that can handle the expected peak load before the flash sale — Option C is correct because the auto-scaling policy is reactive and too slow (5-minute evaluation period) to handle rapid traffic spikes. Pre-warming the endpoint by increasing the number of instances before the flash sale ensures capacity is available. Option A (increase instance size) may help but is more expensive and still reactive. Option B (use a different algorithm) is not the core issue. Option D (enable throttling) would still result in errors.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-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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