easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: An ML team wants to deploy a model that was…
An ML team wants to deploy a model that was trained using XGBoost in SageMaker. They want to use the built-in XGBoost algorithm container for inference. Which inference option requires the least custom code?
⚠ Common exam trap
AWS often tests the misconception that Elastic Inference can accelerate any ML model, but it is specifically designed for deep learning models and does not apply to tree-based algorithms like XGBoost.
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 to a real-time endpoint using the built-in XGBoost container
The built-in XGBoost container in SageMaker is pre-configured with the XGBoost serving stack, including the necessary inference code and dependencies. Deploying a model trained with XGBoost to a real-time endpoint using this container requires no custom inference script or Docker image, only the model artifact and endpoint configuration. This minimizes custom code to just the SageMaker SDK calls for creating the model and endpoint.
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 container with XGBoost and deploy to an endpoint
Why it's wrong here
Requires writing and maintaining custom code.
- ✓
Deploy to a real-time endpoint using the built-in XGBoost container
Why this is correct
The built-in container handles inference automatically.
- ✗
Attach Elastic Inference to a generic container
Why it's wrong here
Elastic Inference is an acceleration option, not a deployment method.
- ✗
Use SageMaker Python SDK to download the model and run local inference
Why it's wrong here
Not a deployment option; local inference is for development.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.