easyMultiple Choice
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
Building a custom Docker container means writing and maintaining the inference handler, dependencies and image yourself, which is exactly the custom code the team wants to avoid. Custom containers are the right choice only when the built-in algorithm image cannot serve the framework or preprocessing you need.
- ✓
Deploy to a real-time endpoint using the built-in XGBoost container
Why this is correct
SageMaker's built-in XGBoost container already implements the inference handler and serving stack, so deploying it to a real-time endpoint needs only a model artefact and an endpoint configuration. No custom inference script or Dockerfile is required, minimising code.
- ✗
Attach Elastic Inference to a generic container
Why it's wrong here
Elastic Inference attaches accelerator capacity to a generic container, which still leaves you supplying the XGBoost serving stack and inference code yourself. It is intended to cut GPU cost for already-working custom containers, not to avoid custom code when a built-in XGBoost image exists.
- ✗
Use SageMaker Python SDK to download the model and run local inference
Why it's wrong here
Downloading the model and running local inference bypasses the built-in XGBoost container entirely, so it does not satisfy the stated requirement and needs bespoke loading and prediction code. Local inference suits offline testing or environments without SageMaker hosting, not managed endpoint deployment.
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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.