MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "least"
Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
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
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Clue confirmation
The clue word "least" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
The built-in XGBoost container uses the SageMaker XGBoost serving stack, which automatically loads the model from the /opt/ml/model directory and exposes a REST API on port 8080 for inference requests. Under the hood, it leverages the xgboost-serving library, which handles input serialization (e.g., CSV, libsvm, JSON) and output formatting, eliminating the need for a custom inference script. In a real-world scenario, if the team needs to preprocess features or postprocess predictions, they can optionally provide a custom inference script (e.g., inference.py) without building a full Docker image, but the question asks for the option requiring the least custom code, which is the default container behavior.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Deploy to a real-time endpoint using the built-in XGBoost container — Option B is correct because 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.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "least". You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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