- A
Weight distribution rule
Why wrong: Weight distribution rule checks for weight updates but not directly overfitting.
- B
Exploding gradients rule
Why wrong: Exploding gradients cause loss to become NaN or spike, not plateau.
- C
Overfit rule
Overfit rule monitors validation vs training metrics to detect overfitting.
- D
Dead relu rule
Why wrong: Dead relu detects neurons that always output zero, not overfitting.
Question 163 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.
During a SageMaker training job, the loss stops decreasing and the validation accuracy plateaus early. SageMaker Debugger rules are enabled. Which rule is MOST likely to identify this issue?
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
✓
Overfit rule
The overfit rule detects when validation accuracy plateaus or decreases while training accuracy continues to improve, which is a sign of overfitting. Exploding gradients detects gradient spikes, dead relu detects dead neurons, and weight distribution checks weight distributions but not directly overfitting.
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.
- ✗
Weight distribution rule
Why it's wrong here
Weight distribution rule checks for weight updates but not directly overfitting.
- ✗
Exploding gradients rule
Why it's wrong here
Exploding gradients cause loss to become NaN or spike, not plateau.
- ✓
Overfit rule
Why this is correct
Overfit rule monitors validation vs training metrics to detect overfitting.
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.
- ✗
Dead relu rule
Why it's wrong here
Dead relu detects neurons that always output zero, not overfitting.
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.
Trap categories for this question
Command / output trap
Dead relu detects neurons that always output zero, not overfitting.
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: Overfit rule — The overfit rule detects when validation accuracy plateaus or decreases while training accuracy continues to improve, which is a sign of overfitting. Exploding gradients detects gradient spikes, dead relu detects dead neurons, and weight distribution checks weight distributions but not directly overfitting.
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: 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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