MLA-C01 ML Model Development Practice Question
A machine learning engineer is training a model using SageMaker and wants to set up monitoring to detect if gradients become too large, which could destabilize training. Which SageMaker Debugger built-in rule should they enable?
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
✓
ExplodingGradients
Debugger's built-in rule 'ExplodingGradients' monitors gradient norms and alerts if they exceed a threshold, helping to stabilize training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
DeadRelu
Why it's wrong here
DeadRelu detects when ReLU neurons are always inactive.
- ✗
LossNotDecreasing
Why it's wrong here
LossNotDecreasing monitors if loss plateaus.
- ✗
Overfit
Why it's wrong here
Overfit rule monitors training vs validation loss to detect overfitting.
- ✓
ExplodingGradients
Why this is correct
ExplodingGradients rule detects when gradients become too large.
Go deeper
Related to this question
About these practice questions
One of 835 original MLA-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.