- A
sagemaker.sklearn.SKLearnEstimator.
Why wrong: SKLearn estimator is for scikit-learn, not XGBoost.
- B
sagemaker.tensorflow.TensorFlowEstimator.
Why wrong: TensorFlow estimator is for TensorFlow, not XGBoost.
- C
sagemaker.xgboost.estimator.XGBoost with a script mode entry point.
Framework estimator allows custom scripts and leverages the XGBoost container.
- D
sagemaker.xgboost.XGBoostEstimator with the built-in algorithm mode.
Why wrong: Built-in mode does not accept custom scripts; it uses default training logic.
Question 581 of 1,000
easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data scientist wants to train an XGBoost model…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
A data scientist wants to train an XGBoost model using the SageMaker Python SDK with a custom training script. Which estimator class should be used?
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
✓
sagemaker.xgboost.estimator.XGBoost with a script mode entry point.
Option C is correct because the `sagemaker.xgboost.estimator.XGBoost` estimator with a script mode entry point allows you to provide a custom training script while leveraging the SageMaker-managed XGBoost container. This is the only estimator class that combines the XGBoost framework with the flexibility of a user-defined entry point for custom preprocessing or training logic.
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.
- ✗
sagemaker.sklearn.SKLearnEstimator.
Why it's wrong here
SKLearn estimator is for scikit-learn, not XGBoost.
- ✗
sagemaker.tensorflow.TensorFlowEstimator.
Why it's wrong here
TensorFlow estimator is for TensorFlow, not XGBoost.
- ✓
sagemaker.xgboost.estimator.XGBoost with a script mode entry point.
Why this is correct
Framework estimator allows custom scripts and leverages the XGBoost container.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
sagemaker.xgboost.XGBoostEstimator with the built-in algorithm mode.
Why it's wrong here
Built-in mode does not accept custom scripts; it uses default training logic.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between the built-in algorithm mode (which uses a pre-defined training script) and script mode (which allows a custom entry point), leading candidates to mistakenly choose the non-existent `XGBoostEstimator` or the wrong framework estimator.
Detailed technical explanation
How to think about this question
Under the hood, the `XGBoost` estimator in script mode uses the SageMaker XGBoost container (e.g., `683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-xgboost:1.5-1`) and executes your custom `entry_point` script via the `sagemaker-training-toolkit`. This allows you to override the default training loop, add custom metrics, or preprocess data, while still benefiting from the optimized XGBoost binary. In a real-world scenario, you might use script mode to implement early stopping based on a custom validation metric not natively supported by the built-in algorithm.
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: sagemaker.xgboost.estimator.XGBoost with a script mode entry point. — Option C is correct because the `sagemaker.xgboost.estimator.XGBoost` estimator with a script mode entry point allows you to provide a custom training script while leveraging the SageMaker-managed XGBoost container. This is the only estimator class that combines the XGBoost framework with the flexibility of a user-defined entry point for custom preprocessing or training logic.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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