MLS-C01 Modeling Practice Question
A data scientist is using Amazon SageMaker Debugger to monitor training. Which THREE types of issues can Debugger monitor?
⚠ Common exam trap
Watch out — candidates often 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.
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
✓
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.
Answer analysis
Option-by-option breakdown
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.
- ✗
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.
- ✓
Vanishing gradients
Why this is correct
Debugger can detect vanishing gradients.
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