MLA-C01 ML Model Development Practice Question

A team is fine-tuning a Hugging Face BERT model for text classification using SageMaker. They want to use the Hugging Face estimator for convenience. Which parameter must be set to use a custom training script?

⚠ Common exam trap

MLA-C01 often tests whether candidates confuse configuration parameters (framework_version, instance_type, hyperparameters) with the parameter that actually supplies the training code, so the trap is picking a familiar estimator argument that does not point to the script.

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

✓

entry_point

The entry_point parameter specifies the path to the custom training script that the Hugging Face estimator will execute inside the container. Without it, the estimator has no user-supplied Python file to run, so it cannot perform custom fine-tuning logic. framework_version, instance_type, and hyperparameters configure the environment but do not point to the training code itself.

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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framework_version
Why it's wrong here
framework_version pins the Hugging Face container image version, not the training script. It is tempting because version mismatches commonly break fine-tuning jobs, but specifying it does not point SageMaker at custom code; entry_point is the parameter that supplies the script.
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instance_type
Why it's wrong here
instance_type selects the compute hardware for the training job and has no bearing on locating a custom script. It is tempting because every training job requires an instance type, but the estimator still runs its default script unless entry_point names the custom training script file.
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hyperparameters
Why it's wrong here
Hyperparameters pass training arguments to the script; they do not tell the estimator which script to run. It is tempting because custom scripts typically need hyperparameters configured, but the entry_point parameter is what actually supplies the training script, so omitting it leaves the estimator using its built-in default.
✓
entry_point
Why this is correct
The entry_point parameter specifies the path to the custom training script within the source directory, which the Hugging Face estimator then executes. This satisfies the stem's requirement of using a custom training script rather than the default built-in one.

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MLA-C01 ML Model Development Practice Question

Why each option matters

Option-by-option breakdown

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