MLA-C01 ML Model Development Practice Question
A team is training a PyTorch model using SageMaker. They have a custom training script that requires specific Python packages not included in the SageMaker default PyTorch container. Which approach should they use?
⚠ Common exam trap
The trap here is assuming any dependency gap requires a custom container; MLA-C01 tests whether you know requirements.txt in source_dir is the supported lightweight mechanism for Python-only packages.
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
✓
Use the built-in PyTorch estimator and specify a requirements.txt in the source directory
The SageMaker PyTorch estimator supports a requirements.txt file placed in the source_dir; the training toolkit automatically installs those packages into the container at the start of the job. This is the intended, low-effort way to add Python dependencies without rebuilding the image. It satisfies the requirement while keeping the managed PyTorch framework benefits (optimized libraries, GPU support, distributed training).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use the built-in PyTorch estimator and specify a requirements.txt in the source directory
Why this is correct
Specifying a requirements.txt in the source directory causes SageMaker to install those packages into the container before training begins. This satisfies the need for custom Python dependencies absent from the default PyTorch container, without building a custom image.
- ✗
Build a custom Docker container from scratch and push it to Amazon ECR
Why it's wrong here
Building a custom Docker container from scratch is unnecessary overhead because SageMaker provides a mechanism to extend its pre-built containers by installing additional packages via a `requirements.txt` file or a Dockerfile that inherits from the SageMaker PyTorch base image. This option is tempting because custom containers are the correct approach when the training script requires a fundamentally different runtime environment—such as a different operating system, a non-Python dependency, or a package that conflicts with the SageMaker container’s base libraries—where extending the existing image is insufficient.
- ✗
Use the SageMaker XGBoost estimator and modify the script to use PyTorch
Why it's wrong here
The XGBoost estimator runs the XGBoost framework, so a PyTorch training script cannot execute within it regardless of edits. It is tempting because switching estimators appears to resolve container mismatches, and would be correct if the workload genuinely used XGBoost rather than PyTorch.
- ✗
Use SageMaker Autopilot to automatically handle dependencies
Why it's wrong here
Autopilot automates algorithm selection, tuning and deployment for tabular data; it offers no mechanism to install arbitrary Python packages into a custom PyTorch training container. It is tempting because Autopilot removes manual ML engineering, and would be correct for standard tabular regression or classification without custom dependencies.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.