MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A company wants to deploy a scikit-learn model to a SageMaker AI real-time endpoint. The model must be loaded from a custom Python module that contains preprocessing logic not present in the built-in scikit-learn container. The team wants to minimize operational overhead and does not need to change system-level libraries. Which approach should the engineer take?
⚠ Common exam trap
The trap here is defaulting to a fully custom container whenever any custom code is needed, even when an entry point script on a managed framework container is sufficient.
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
✓
Package the preprocessing logic in an inference.py file and pass it as the entry_point to a SageMaker AI framework estimator using the scikit-learn framework.
A SageMaker AI framework estimator with an entry_point script lets the team inject custom Python preprocessing while reusing the managed scikit-learn container. This satisfies the functional requirement with far less operational effort than building and maintaining a custom Docker image.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Package the preprocessing logic in an inference.py file and pass it as the entry_point to a SageMaker AI framework estimator using the scikit-learn framework.
Why this is correct
SageMaker AI framework estimators support an entry_point script that defines model_fn and input_fn or transform_fn. This lets the engineer add custom Python preprocessing while reusing the managed scikit-learn container, which minimizes operational overhead because no Docker image must be built or maintained.
- ✗
Use the SageMaker AI built-in scikit-learn container without an entry point and rely on the default inference handler.
Why it's wrong here
The default handler serves the model but provides no place for the custom preprocessing logic the team requires. Without an entry point, the preprocessing module would never be loaded, so requests would not be transformed as needed and predictions could be incorrect.
- ✗
Build a fully custom Docker image with a Bring Your Own Container (BYOC) approach and push it to Amazon ECR.
Why it's wrong here
BYOC gives maximum control but requires building and maintaining a Docker image, including the inference stack, which adds operational overhead. Since the team only needs to add Python preprocessing logic and does not need custom system libraries, a lighter mechanism is available and preferable.
- ✗
Deploy the model with SageMaker AI batch transform and invoke it from the application on demand.
Why it's wrong here
Batch transform is an offline job that reads from and writes to Amazon S3, not a persistent endpoint that an application can call for real-time predictions. It also does not solve the custom preprocessing requirement, since the same handler logic must still be packaged somewhere.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.