Quick Answer
The answer is a memory leak in the inference container. When troubleshooting SageMaker endpoint latency, a gradual increase over time—without errors or statefulness—points directly to the inference container consuming more memory with each request, eventually forcing the operating system to swap or throttle the process. This is a classic symptom of a memory leak, where allocated objects are never released, degrading performance even though the model itself is stateless. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to distinguish between infrastructure issues (like auto-scaling or model state) and container-level resource exhaustion. A common trap is assuming auto-scaling will fix latency, but scaling adds instances rather than fixing a leak in existing containers. Memory tip: think “leaky bucket”—each request adds a drop, and eventually the bucket overflows, slowing everything down.
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 to host a model for real-time inference. They notice that the endpoint's latency increases over time. The model is stateless and the inference code does not log any errors. What is the MOST likely cause?
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.
Memory leak in the inference container
Memory leaks cause slowdown over time.
Gradual increase in request payload size
Why wrong: Request size varies; not necessarily gradual.
Endpoint auto scaling is adding new instances
Why wrong: Auto scaling would reduce latency, not increase.
Model is accumulating state from previous requests
Why wrong: Model is stateless.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Memory leak in the inference container
Memory leaks cause gradual performance degradation. Option A is correct. Option B is wrong because the model is stateless. Option C is wrong because auto scaling would add instances, not degrade existing ones. Option D is wrong because the model is stateless.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Memory leak in the inference container
Why this is correct
Memory leaks cause slowdown over time.
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.
Gradual increase in request payload size
Why it's wrong here
Request size varies; not necessarily gradual.
Endpoint auto scaling is adding new instances
Why it's wrong here
Auto scaling would reduce latency, not increase.
Model is accumulating state from previous requests
Why it's wrong here
Model is stateless.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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.
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FAQ
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
The correct answer is: Memory leak in the inference container — Memory leaks cause gradual performance degradation. Option A is correct. Option B is wrong because the model is stateless. Option C is wrong because auto scaling would add instances, not degrade existing ones. Option D is wrong because the model is stateless.
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.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
This MLS-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 MLS-C01 exam.
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