The answer is to use a larger instance type, such as moving from an ml.c5.large to an ml.c5.xlarge, because the current latency of 15 ms exceeds the 10 ms requirement due to insufficient CPU resources for the small scikit-learn model. For real-time inference, per-request latency is primarily driven by the compute capacity of the individual instance; scaling up to a larger instance provides more vCPUs and memory, directly reducing the time each prediction takes. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding that vertical scaling (larger instance) addresses single-request latency, while horizontal scaling (adding more instances) improves throughput but not per-call speed. A common trap is confusing batch size or Batch Transform with real-time endpoints—batch processing is for offline, not latency-sensitive predictions. Memory tip: “Bigger box, faster clock” for real-time; more instances only help when you have many concurrent requests.
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.
Exhibit
2023-01-01 12:00:00,000 - ERROR - Model prediction took 15 ms for request ID abc123
Refer to the exhibit. A data scientist is reviewing CloudWatch logs for a SageMaker real-time endpoint. The log shows that a prediction took 15 ms. The endpoint is configured with an ml.c5.large instance and the model is a small scikit-learn model. The latency requirement is under 10 ms. Which action would most likely reduce the latency?
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
✓
Use a larger instance type
Option D is correct because using a larger instance (ml.c5.xlarge) provides more CPU resources, reducing inference time. Option A (increase batch size) is not applicable for real-time single requests. Option B (enable SageMaker Batch Transform) is for offline. Option C (add more instances) does not reduce per-request latency. Option E (use a different framework) is not likely the issue.
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 a larger instance type
Why this is correct
More CPU power reduces latency.
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.
✗
Add more instances to the endpoint
Why it's wrong here
Adding instances increases throughput, not reduces latency.
✗
Change the model to a TensorFlow model
Why it's wrong here
Framework change may not reduce latency.
✗
Enable SageMaker Batch Transform
Why it's wrong here
Batch transform is not real-time.
✗
Increase the batch size for inference
Why it's wrong here
Batch size is for batch transform, not real-time.
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: Use a larger instance type — Option D is correct because using a larger instance (ml.c5.xlarge) provides more CPU resources, reducing inference time. Option A (increase batch size) is not applicable for real-time single requests. Option B (enable SageMaker Batch Transform) is for offline. Option C (add more instances) does not reduce per-request latency. Option E (use a different framework) is not likely the issue.
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.
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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