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MLS-C01 Data Engineering Practice Question
Drag and drop the steps to set up cross-validation in a SageMaker training job using the built-in XGBoost algorithm in the correct order.
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
✓
Split the data into training and validation folds, then configure the SageMaker estimator with cross-validation parameters (e.g., num_round, eval_metric, num_cv_folds), then execute the training job (fit), then select the best model based on average CV metrics.
Cross-validation requires data splitting, job configuration with CV parameters, execution, and model selection.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Split the data into training and validation folds, then configure the SageMaker estimator with cross-validation parameters (e.g., num_round, eval_metric, num_cv_folds), then execute the training job (fit), then select the best model based on average CV metrics.
Why this is correct
This is the correct order because you first need to prepare the data, then configure the job to use cross-validation, then run the training, and finally choose the best model from the results.
- ✗
Configure the SageMaker estimator with cross-validation parameters, then split the data into folds, then execute the training job, then select the best model.
Why it's wrong here
This is incorrect because the estimator configuration typically requires knowledge of the data structure (e.g., number of folds), so the data should be prepared before configuration.
- ✗
Split the data into folds, then execute the training job, then configure the estimator, then select the best model.
Why it's wrong here
This is incorrect because the training job cannot execute without a properly configured estimator; configuration must precede execution.
- ✗
Execute the training job, then split the data into folds, then configure the estimator, then select the best model.
Why it's wrong here
This is incorrect because you cannot execute a training job without first having data and a configured estimator; the order is completely reversed.
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