- A
Exploding gradients
Exploding gradients can cause memory spikes leading to OOM; Debugger can capture this.
- B
Overfit
Why wrong: Overfit rule detects overfitting, not OOM.
- C
Dead relu
Why wrong: Dead relu detects dead neurons, not memory issues.
- D
OOM rule
Why wrong: There is no built-in OOM rule in SageMaker Debugger.
Question 483 of 1,000
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. 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.
An ML engineer is debugging a training job that is consistently failing due to an out-of-memory error. The engineer is using SageMaker's built-in XGBoost algorithm. Which Debugger rule can help identify the issue?
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
✓
Exploding gradients
The 'Exploding gradients' rule detects when gradients become too large, which is a common cause of training instability but not necessarily OOM. The 'Overfit' rule detects overfitting. The 'Dead relu' rule is for ReLU activation. None of these directly address OOM. However, Debugger does not have a specific OOM rule; instead, the engineer should monitor memory utilization via CloudWatch or adjust instance type. Among the options, 'Exploding gradients' is the most relevant because large gradients can lead to memory spikes.
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.
- ✓
Exploding gradients
Why this is correct
Exploding gradients can cause memory spikes leading to OOM; Debugger can capture this.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Overfit
Why it's wrong here
Overfit rule detects overfitting, not OOM.
- ✗
Dead relu
Why it's wrong here
Dead relu detects dead neurons, not memory issues.
- ✗
OOM rule
Why it's wrong here
There is no built-in OOM rule in SageMaker Debugger.
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 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?
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Exploding gradients — The 'Exploding gradients' rule detects when gradients become too large, which is a common cause of training instability but not necessarily OOM. The 'Overfit' rule detects overfitting. The 'Dead relu' rule is for ReLU activation. None of these directly address OOM. However, Debugger does not have a specific OOM rule; instead, the engineer should monitor memory utilization via CloudWatch or adjust instance type. Among the options, 'Exploding gradients' is the most relevant because large gradients can lead to memory spikes.
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.
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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