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Exam Certified Machine Learning Associate All Questions

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Exam Certified Machine Learning Associate topic 1 question 10 discussion

Actual exam question from Databricks's Certified Machine Learning Associate
Question #: 10
Topic #: 1
[All Certified Machine Learning Associate Questions]

A machine learning engineer is trying to scale a machine learning pipeline pipeline that contains multiple feature engineering stages and a modeling stage. As part of the cross-validation process, they are using the following code block:

A colleague suggests that the code block can be changed to speed up the tuning process by passing the model object to the estimator parameter and then placing the updated cv object as the final stage of the pipeline in place of the original model.
Which of the following is a negative consequence of the approach suggested by the colleague?

  • A. The model will take longer to train for each unique combination of hyperparameter values
  • B. The feature engineering stages will be computed using validation data
  • C. The cross-validation process will no longer be parallelizable
  • D. The cross-validation process will no longer be reproducible
  • E. The model will be refit one more per cross-validation fold
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Suggested Answer: B 🗳️

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Deuterium44
2 weeks, 3 days ago
Selected Answer: B
A. This is incorrect because the training time is generally determined by the pipeline complexity and the data size, not by the cross-validation object placement. C. This is incorrect. The parallelism=2 parameter still allows parallel processing, and the placement of the cross-validation object doesn’t impact parallelization. D. This is incorrect as reproducibility depends on the seed parameter, which is set to 42 here. Moving the cross-validator in the pipeline does not inherently affect reproducibility. E. This is incorrect. Moving the cross-validator object does not affect the number of times the model is refit in each fold.
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