- A
The endpoint is under-provisioned and requests are throttled
Why wrong: Throttling results in 4XX errors (TooManyRequests), not 5XX.
- B
The input data format has changed
Why wrong: Input format change typically causes 4XX errors, not 5XX.
- C
The model container is out of memory or crashing
Out-of-memory errors or crashes cause the container to return 5XX responses.
- D
The model is returning predictions with high latency
Why wrong: High latency does not cause 5XX errors; it may increase latency metrics.
Question 516 of 1,000
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. 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 team monitors a production endpoint and notices a sudden increase in 5XXError count. Which of the following 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.
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 model container is out of memory or crashing
A sudden increase in 5XX errors, particularly HTTP 503 or 502, typically indicates that the model container is failing to process requests due to resource exhaustion (e.g., OOM kills) or a crash in the inference process. In a production ML endpoint, such errors often stem from the container running out of memory, leading to the container being terminated by the orchestrator (e.g., Kubernetes OOMKill) or the application crashing internally, which directly causes 5XX responses.
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 endpoint is under-provisioned and requests are throttled
Why it's wrong here
Throttling results in 4XX errors (TooManyRequests), not 5XX.
- ✗
The input data format has changed
Why it's wrong here
Input format change typically causes 4XX errors, not 5XX.
- ✓
The model container is out of memory or crashing
Why this is correct
Out-of-memory errors or crashes cause the container to return 5XX responses.
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 is returning predictions with high latency
Why it's wrong here
High latency does not cause 5XX errors; it may increase latency metrics.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between client-side errors (4XX) and server-side errors (5XX), and the trap here is that candidates confuse throttling (429) or input format issues (400) with server-side failures, overlooking that 5XX errors specifically indicate the server or container is failing to handle the request.
Detailed technical explanation
How to think about this question
In SageMaker or similar ML endpoints, the model container runs as a Docker process; when memory limits are exceeded, the Linux kernel's Out-Of-Memory Killer (OOMKill) terminates the container, causing the endpoint to return 503 or 502 errors until the container is restarted by the orchestrator. This is often detected via CloudWatch metrics like `MemoryUtilization` spiking to 100% followed by a drop, or via `ModelContainerCrash` events. A real-world scenario is a model that leaks memory over time due to improper tensor or session management, leading to periodic 5XX spikes that are misdiagnosed as traffic surges.
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.
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ML Solution Monitoring, Maintenance, and Security — study guide chapter
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
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: The model container is out of memory or crashing — A sudden increase in 5XX errors, particularly HTTP 503 or 502, typically indicates that the model container is failing to process requests due to resource exhaustion (e.g., OOM kills) or a crash in the inference process. In a production ML endpoint, such errors often stem from the container running out of memory, leading to the container being terminated by the orchestrator (e.g., Kubernetes OOMKill) or the application crashing internally, which directly causes 5XX responses.
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: "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
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Last reviewed: Jul 4, 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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