Quick Answer
The answer is vanishing gradients, along with poor weight initialization and overfitting, as the three types of issues Amazon SageMaker Debugger can monitor during training. SageMaker Debugger works by capturing real-time tensor data, such as gradients and weights, and applying built-in rules to detect anomalies like vanishing or exploding gradients, which occur when gradient values shrink or grow uncontrollably, stalling or destabilizing learning. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of Debugger’s core monitoring capabilities—specifically its ability to surface training problems without manual logging. A common trap is confusing Debugger with SageMaker Model Monitor, which focuses on inference data drift, not training dynamics. Remember the mnemonic “VPO” for Vanishing gradients, Poor weight initialization, and Overfitting—the three issues Debugger’s built-in rules are designed to catch.
This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 data scientist is using Amazon SageMaker Debugger to monitor training. Which THREE types of issues can Debugger monitor?
Hardware failures
Why wrong: Debugger does not monitor hardware.
Poor weight initialization
Debugger can detect issues from poor initialization.
Data drift
Why wrong: Data drift is monitored by Model Monitor, not Debugger.
Overfitting
Debugger can detect overfitting via loss curves.
Vanishing gradients
Debugger can detect vanishing gradients.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Poor weight initialization
Amazon SageMaker Debugger can monitor training for poor weight initialization by analyzing tensors and gradients during the training process. It uses built-in rules to detect if weights are initialized with values that are too large or too small, which can lead to slow convergence or failure to learn. This is a core capability of Debugger's real-time monitoring of model parameters.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Hardware failures
Why it's wrong here
Debugger does not monitor hardware.
Poor weight initialization
Why this is correct
Debugger can detect issues from poor initialization.
Related concept
Read the scenario before looking for a memorised answer.
Data drift
Why it's wrong here
Data drift is monitored by Model Monitor, not Debugger.
Overfitting
Why this is correct
Debugger can detect overfitting via loss curves.
Related concept
Read the scenario before looking for a memorised answer.
Vanishing gradients
Why this is correct
Debugger can detect vanishing gradients.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates confuse SageMaker Debugger (which monitors training metrics like gradients and weights) with SageMaker Model Monitor (which monitors inference data for drift and bias), leading them to incorrectly select data drift as a Debugger capability.
Detailed technical explanation
SageMaker Debugger captures tensors (e.g., weights, gradients, losses) at specified steps and applies built-in rules like VanishingGradient, Overfitting, and PoorWeightInitialization. For vanishing gradients, it checks if gradient norms fall below a threshold (e.g., 1e-7) across layers, which is critical for deep networks where gradients can exponentially decay. In practice, Debugger can also trigger custom actions like stopping training or logging to Amazon S3 when these conditions are met.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Poor weight initialization — Amazon SageMaker Debugger can monitor training for poor weight initialization by analyzing tensors and gradients during the training process. It uses built-in rules to detect if weights are initialized with values that are too large or too small, which can lead to slow convergence or failure to learn. This is a core capability of Debugger's real-time monitoring of model parameters.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This MLS-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 MLS-C01 exam.
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