MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A machine learning engineer has trained a scikit-learn model and saved it as model.joblib in Amazon S3. The engineer wants SageMaker to host the model for real-time inference without writing a custom container or inference script, because the model uses only standard predict behavior. Which deployment approach should the engineer use?
⚠ Common exam trap
The trap here is assuming that any deployment requires a custom container, when SageMaker built-in framework containers already provide default inference handlers for supported libraries.
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
✓
Deploy the model with the SageMaker Scikit-learn built-in framework container using the SageMaker Python SDK SKLearnModel class, pointing model_data to the S3 artifact.
Because the model is a standard scikit-learn artifact and no custom inference logic is required, the built-in Scikit-learn framework container is the correct fit. Passing the S3 model artifact to the SKLearnModel class lets SageMaker load the joblib file and use the container's default predict handler, producing a real-time endpoint without custom code or image maintenance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a custom Docker image that installs scikit-learn, push it to Amazon ECR, and write an inference handler for the /invocations route.
Why it's wrong here
This works but is unnecessary here. A custom container forces the engineer to build, push, and maintain an image plus write an inference handler, adding operational overhead. The scenario states no custom libraries or preprocessing are required, so the built-in framework container already satisfies the requirement with far less effort.
- ✗
Use SageMaker Batch Transform with the built-in Scikit-learn container and invoke the endpoint for each real-time request.
Why it's wrong here
Batch Transform is designed for offline, bulk scoring of datasets in S3, not for low-latency request/response serving. It does not expose a persistent HTTPS endpoint that an application can call per request, so it does not meet the real-time inference requirement even though the container itself is compatible.
- ✓
Deploy the model with the SageMaker Scikit-learn built-in framework container using the SageMaker Python SDK SKLearnModel class, pointing model_data to the S3 artifact.
Why this is correct
The Scikit-learn built-in framework container supports joblib and pickle artifacts and provides a default inference handler that loads the model and calls predict, so no custom script is needed. Supplying the S3 model artifact through SKLearnModel lets SageMaker extract the tarball and start a compliant real-time endpoint.
- ✗
Upload model.joblib to SageMaker Model Registry and rely on automatic endpoint creation when the model package is approved.
Why it's wrong here
The Model Registry stores model package metadata and versions for governance and approval workflows; approving a model package does not itself deploy an endpoint. The engineer must still create a model, endpoint configuration, and endpoint, so this does not deliver hosting on its own.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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JA
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.