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MLA-C01 Practice Question: During deployment of a Hugging Face model, the…

Exhibit

Refer to the exhibit.
```
CloudWatch Logs from a SageMaker endpoint:
[ERROR] Runtime.ImportModuleError: Unable to import module 'inference': No module named 'transformers'
```

During deployment of a Hugging Face model, the endpoint logs show this error. Which step was likely missed?

⚠ Common exam trap

A common mix-up: candidates confuse runtime dependency issues (missing Python libraries) with infrastructure or configuration problems (IAM permissions, instance types, or packaging), leading them to select a plausible-sounding but incorrect option like B or C.

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

The inference container does not include the transformers library; the team should use a pre-built Hugging Face container.

The error indicates that the inference container cannot find the `transformers` library, which is required to load and run the Hugging Face model. By using a pre-built Hugging Face container from AWS, the team ensures that all necessary dependencies (like `transformers`, `tokenizers`, and `torch`) are pre-installed and compatible with the SageMaker inference environment. Option A is correct because the most likely missed step was selecting a generic container instead of the purpose-built Hugging Face container.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • The inference container does not include the transformers library; the team should use a pre-built Hugging Face container.

    Why this is correct

    Hugging Face containers are pre-built with transformers and other dependencies.

  • The IAM role does not have permissions to download additional libraries.

    Why it's wrong here

    Permission issues are not reflected in import module errors.

  • The model artifact was not packaged correctly; the inference script is missing.

    Why it's wrong here

    Missing script would cause a different import error, not specifically transformers.

  • The endpoint configuration specifies the wrong instance type.

    Why it's wrong here

    Instance type affects performance, not module imports.

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JA

Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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