MLA-C01 ML Model Development Practice Question
A team is using SageMaker to train a distributed model with data parallelism. They notice that the training loss is not decreasing as expected and suspect a bug in the data loading pipeline. Which SageMaker Debugger feature can help them inspect the data distributions during training?
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
✓
Custom rules to monitor input tensors
SageMaker Debugger can capture tensors (including inputs and outputs) during training. By saving input tensors, the team can inspect data distributions. Rules fire on issues like overfitting or dead relu, but tensor captures allow direct data inspection. SaveConfig defines which tensors to save. Using a different instance type does not help debug data issues.
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
Model Monitor is for inference data drift, not training data inspection.
- ✗
Built-in rules such as overfit detection
Why it's wrong here
Overfit detection monitors training vs validation loss, not data distributions.
- ✓
Custom rules to monitor input tensors
Why this is correct
By creating a custom rule or using tensor captures, Debugger can save input tensors for analysis of data distributions.
- ✗
SageMaker Processing jobs
Why it's wrong here
Processing jobs are for pre- or post-processing, not real-time training debugging.
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