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MLA-C01 Practice Question: A data scientist is training a deep learning…
A data scientist is training a deep learning model on Amazon SageMaker and notices that the training loss decreases but the validation loss starts increasing after a certain number of epochs. The model is likely overfitting. Which SageMaker feature can they use to detect and diagnose this issue during training?
⚠ Common exam trap
It's easy for candidates to confuse SageMaker Debugger's real-time training diagnostics with SageMaker Model Monitor's post-deployment monitoring, or assume that hyperparameter tuning (Automatic Model Tuning) inherently addresses overfitting, when in fact it only searches for optimal hyperparameters without detecting the overfitting condition during a specific training run.
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
✓
SageMaker Debugger
SageMaker Debugger is the correct choice because it provides real-time monitoring of training metrics, including loss values, and can automatically detect anomalies such as overfitting (where training loss decreases but validation loss increases). It allows you to set rules (e.g., `OverfitRule`) that trigger alerts or stop training when overfitting is detected, enabling proactive diagnosis during the training job.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Model Monitor
Why it's wrong here
SageMaker Model Monitor is for monitoring inference endpoints, not training.
- ✗
SageMaker Automatic Model Tuning
Why it's wrong here
Automatic Model Tuning optimizes hyperparameters but does not detect overfitting.
- ✗
SageMaker Experiments
Why it's wrong here
SageMaker Experiments is used for tracking and comparing runs, not detecting overfitting.
- ✓
SageMaker Debugger
Why this is correct
SageMaker Debugger provides built-in rules such as OverfitRule to monitor training and detect issues like overfitting in real time.
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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.