The answer is the `sagemaker.xgboost.estimator.XGBoost` class with a script mode entry point. This is the correct choice because the SageMaker XGBoost framework estimator provides an optimized, pre-built container that supports custom training scripts, allowing you to override the default training logic while still benefiting from SageMaker’s performance optimizations for XGBoost. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of the distinction between built-in algorithms (which require specific input formats and do not accept custom scripts) and framework estimators (which are designed for script mode). A common trap is confusing the built-in XGBoost algorithm with the XGBoost framework estimator—remember that the built-in version is for no-code training, while the framework estimator is for custom code. Memory tip: “Framework equals flexibility” — if you need a custom script, reach for the framework estimator, not the built-in algorithm.
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. 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 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 framework estimator allows users to bring their own training script while using the optimized XGBoost container. Option A is wrong because the built-in XGBoost algorithm does not support custom scripts; it expects a specific input format. Option B is wrong because scikit-learn estimator does not natively support XGBoost training. Option D is wrong because TensorFlow estimator is for TensorFlow models.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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
Static NAT maps one inside address to one outside address.
✗
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: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. 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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLA-C01 NAT questions on configuration and troubleshooting.
ML Model Development — This question tests ML Model Development — Static NAT maps one inside address to one outside address..
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 framework estimator allows users to bring their own training script while using the optimized XGBoost container. Option A is wrong because the built-in XGBoost algorithm does not support custom scripts; it expects a specific input format. Option B is wrong because scikit-learn estimator does not natively support XGBoost training. Option D is wrong because TensorFlow estimator is for TensorFlow models.
What should I do if I get this MLA-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLA-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Question Discussion
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