Question 357 of 1,672
MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is using SageMaker to train a model using the built-in XGBoost algorithm. The training job fails with the error 'AlgorithmError: Framework error: No module named 'xgboost''. What is the most likely cause?
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
✓
The training job is using a custom container that does not have XGBoost installed.
The built-in XGBoost algorithm requires the 'xgboost' Python package; SageMaker's built-in algorithms provide the necessary environment, but if the container is overridden or the wrong image is used, the module may be missing. Option A is wrong because the error is about missing module, not data format. Option C is wrong because the error is not about permissions. Option D is wrong because the error is not about output path.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The training data is not in CSV format.
Why it's wrong here
Would cause a data parsing error, not module error.
- ✓
The training job is using a custom container that does not have XGBoost installed.
Why this is correct
Missing module indicates container issue.
- ✗
The IAM role does not have permission to access SageMaker.
Why it's wrong here
Would cause access denied errors.
- ✗
The S3 output path is incorrect.
Why it's wrong here
Would cause failure at end, not start.
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Last reviewed: Jun 20, 2026
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