- A
The inference script is not using batch processing.
Why wrong: Batch processing is not applicable for real-time endpoints.
- B
The SageMaker endpoint auto scaling is not configured to scale out quickly enough under increasing traffic.
If auto scaling policies are too conservative, the endpoint may not add instances fast enough during traffic spikes, leading to increased latency.
- C
The model size is too large for the instance type.
Why wrong: The model size hasn't changed, so this cannot explain the gradual increase.
- D
The endpoint has data capture enabled, causing additional overhead.
Why wrong: Data capture overhead is constant and does not increase over time.
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. 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 deployed a machine learning model on an Amazon SageMaker real-time endpoint. Over several weeks, they notice that inference latency has been gradually increasing, especially during peak business hours. The model and instance type have remained unchanged. What is the most likely cause of the increased 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 choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
The SageMaker endpoint auto scaling is not configured to scale out quickly enough under increasing traffic.
Option B is correct because the gradual increase in latency over time, especially during peak hours, suggests that the endpoint may not be scaling out adequately to handle increased traffic. Option A is incorrect because the model size has not changed. Option C is incorrect because data capture does not inherently cause latency increases over time. Option D is incorrect because batch processing is not used for real-time endpoints.
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.
- ✗
The inference script is not using batch processing.
Why it's wrong here
Batch processing is not applicable for real-time endpoints.
- ✓
The SageMaker endpoint auto scaling is not configured to scale out quickly enough under increasing traffic.
Why this is correct
If auto scaling policies are too conservative, the endpoint may not add instances fast enough during traffic spikes, leading to increased 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.
- ✗
The model size is too large for the instance type.
Why it's wrong here
The model size hasn't changed, so this cannot explain the gradual increase.
- ✗
The endpoint has data capture enabled, causing additional overhead.
Why it's wrong here
Data capture overhead is constant and does not increase over 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
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 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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
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: The SageMaker endpoint auto scaling is not configured to scale out quickly enough under increasing traffic. — Option B is correct because the gradual increase in latency over time, especially during peak hours, suggests that the endpoint may not be scaling out adequately to handle increased traffic. Option A is incorrect because the model size has not changed. Option C is incorrect because data capture does not inherently cause latency increases over time. Option D is incorrect because batch processing is not used for real-time endpoints.
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.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jun 23, 2026
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.
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