You have an Azure Machine Learning model that uses clinical data to predict whether a patient has a disease. You clean and transform the clinical data. You need to ensure that the accuracy of the…

The correct answer is B. Split the clinical data into Two datasets. To verify the accuracy of a machine learning model, you must evaluate its performance on data it hasn’t seen during training. This is done by splitting the data into: Training set – used to train the model. Validation or test set – used to evaluate how well the model…

Submitted by brentm· Mar 30, 2026Describe fundamental principles of machine learning on Azure

Question

You have an Azure Machine Learning model that uses clinical data to predict whether a patient has a disease. You clean and transform the clinical data. You need to ensure that the accuracy of the model can be verified. What should you do next?

Options

ATrain the model by using the clinical data.
BSplit the clinical data into Two datasets.
CTrain the model by using automated machine learning (automated ML).
DValidate the model by using the clinical data.

How the community answered

(23 responses)

B
91% (21)
C
4% (1)
D
4% (1)

Explanation

To verify the accuracy of a machine learning model, you must evaluate its performance on data it hasn’t seen during training. This is done by splitting the data into: Training set – used to train the model. Validation or test set – used to evaluate how well the model generalizes to unseen data. This separation helps avoid overfitting and provides a more accurate estimate of how the model will perform in the real world.

Topics

#Dataset splitting#ML model validation#Data preparation

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