MLA-C01 Data Preparation for Machine Learning Practice Question
A data scientist is using Amazon SageMaker Processing to run a feature engineering job. The job requires installing additional Python libraries not included in the default SageMaker containers. Which approach should the data scientist use to include these libraries?
⚠ Common exam trap
Many candidates assume SageMaker containers have internet access by default or that a `requirements.txt` in S3 is automatically processed, but in reality, SageMaker Processing jobs often run in isolated subnets without outbound internet, making pip install impossible without a pre-built custom image.
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
✓
Create a custom Docker image with the libraries installed and specify it in the ProcessingInput
SageMaker Processing jobs run in isolated containers that cannot install packages at runtime via pip without internet access or custom images. Creating a custom Docker image with the required libraries pre-installed ensures the environment is consistent, reproducible, and avoids dependency resolution failures during job execution. This approach aligns with SageMaker's best practice for custom dependencies.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add the libraries to the `requirements.txt` file in the same S3 bucket as the script
Why it's wrong here
requirements.txt alone does not trigger installation; you must explicitly run pip.
- ✓
Create a custom Docker image with the libraries installed and specify it in the ProcessingInput
Why this is correct
A custom image ensures dependencies are available without runtime installation.
- ✗
Use Amazon EFS to store the libraries and mount them to the processing container
Why it's wrong here
EFS is an unnecessary complexity for simple library dependencies.
- ✗
Use the `pip install` command within the processing script at runtime
Why it's wrong here
Runtime pip install increases job duration and may fail inconsistently.
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