MLA-C01 ML Model Development Practice Question
A team is fine-tuning a Hugging Face transformer model on SageMaker. They need to use a custom training script with the Hugging Face Estimator. Which SageMaker feature does this represent?
⚠ Common exam trap
MLA-C01 often tests the distinction between built-in algorithms and Script Mode, causing candidates to confuse custom script execution with automated ML features like Autopilot or debugging tools like Debugger.
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
✓
Script mode
Using a custom training script with the Hugging Face Estimator in SageMaker represents Script Mode. Script Mode allows you to bring your own training script and run it within a pre-built framework container, such as Hugging Face, PyTorch, or TensorFlow. This provides flexibility to customize training logic while leveraging SageMaker's managed infrastructure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Built-in algorithm
Why it's wrong here
Built-in algorithms are pre-written containers maintained by SageMaker, so they cannot run a custom training script. It is tempting because built-in algorithms also train models, and one would be correct when a supported algorithm such as XGBoost fits the data without custom code.
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SageMaker Autopilot
Why it's wrong here
Autopilot automates model selection, feature engineering and tuning on tabular data; it does not execute a user-supplied training script. It is tempting because Autopilot also trains models, and it would be correct when the goal is automatic model building without writing training code.
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SageMaker Debugger
Why it's wrong here
Debugger monitors training jobs for tensors, metrics and anomalies; it does not run custom training scripts through the Hugging Face Estimator. It is tempting because Debugger attaches to training jobs, and it would be correct when the requirement is detecting vanishing gradients or debugging model convergence during training.
- ✓
Script mode
Why this is correct
Script mode lets the Hugging Face Estimator run a user-supplied Python training script inside the managed container, satisfying the custom training script requirement. The estimator passes hyperparameters and data channels to that entry point, so no prebuilt algorithm image is needed.
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