DP-100 · Question #204
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might…
The correct answer is A. Yes. The answer is Yes because the solution correctly constructs an Azure ML pipeline with two sequential steps: a data processing step that loads data from a datastore and a model training step that consumes the processed output. In Azure ML, pipeline steps can pass data between…
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Options
- AYes
- BNo
How the community answered
(37 responses)- A81% (30)
- B19% (7)
Explanation
The answer is Yes because the solution correctly constructs an Azure ML pipeline with two sequential steps: a data processing step that loads data from a datastore and a model training step that consumes the processed output. In Azure ML, pipeline steps can pass data between one another using OutputFileDatasetConfig or pipeline data objects. This architecture cleanly separates data preparation from model training, which is exactly the goal described. The code properly chains the steps so the training script receives the processed data as input.
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