Drag and Drop Question You plan to deploy an Azure Machine Learning model by using the Machine Learning designer. Which four actions should you perform in sequence? To answer, move the appropriate act

The correct answer is Ingest and prepare a dataset.; Split the data randomly into training data and validation data.; Train the model.; Evaluate the model against the validation dataset.. The correct sequence follows the standard machine learning workflow: first you must ingest and prepare your dataset to ensure it is clean and usable, then split it into training and validation sets to enable unbiased evaluation, then train the model using only the training data,

Submitted by carlos_mx· Mar 30, 2026Implement and manage Azure Machine Learning solutions - specifically, designing and deploying machine learning models using Azure ML Designer, including understanding the end-to-end training pipeline sequence.

Question

Drag and Drop Question You plan to deploy an Azure Machine Learning model by using the Machine Learning designer. Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. Answer:

Answer Area

Drag items

Train the model.Split the data randomly into training data and validation data.Evaluate the model against the original dataset.Evaluate the model against the validation dataset.Ingest and prepare a dataset.

Correct arrangement

Ingest and prepare a dataset.
Split the data randomly into training data and validation data.
Train the model.
Evaluate the model against the validation dataset.

Explanation

The correct sequence follows the standard machine learning workflow: first you must ingest and prepare your dataset to ensure it is clean and usable, then split it into training and validation sets to enable unbiased evaluation, then train the model using only the training data, and finally evaluate the model's performance against the held-out validation dataset. This separation of training and validation data is critical to prevent data leakage and ensure the evaluation reflects true generalization performance on unseen data.

Topics

#Azure Machine Learning Designer#Model Training Workflow#Train-Validation Split#MLOps

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