AI-900 exam dumps

AI-900 practice question 79 of 286

Microsoft Azure AI Fundamentals. Free level, Microsoft. Free question with the correct answer and a full explanation.

AI-900 Question 79

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You are tasked with building a machine learning model on Azure to predict customer churn for a subscription-based service. You want to use Azure Machine Learning and follow the basic principles of machine learning. Which steps should you include in your workflow?

  1. A

    Define the business problem and identify the data needed.

  2. B

    Use Azure Resource Manager templates to deploy the model directly.

  3. C

    Split the dataset into training and testing data.

  4. D

    Train the model using labeled data to optimize its performance.

  5. E

    Evaluate the model's performance using appropriate metrics.

Show answer and explanation

Correct answers: A, C, D, E

Explanation

The fundamental principles of machine learning on Azure include defining the problem, preparing the data, training the model, and evaluating its performance. These steps are critical in ensuring a successful machine learning workflow. Azure Resource Manager templates, while useful for resource management, are unrelated to the machine learning process.

  • A. Correct.

    Defining the business problem and identifying the data is the initial step in any machine learning workflow. It ensures that the problem and goals are clearly understood.

  • B. Incorrect.

    Azure Resource Manager templates are used for infrastructure deployment, not for building or training machine learning models. This is not a part of the machine learning workflow.

  • C. Correct.

    Splitting the dataset into training and testing partitions is critical to ensure the model is trained properly and its performance can be evaluated fairly.

  • D. Correct.

    Training the model using labeled data is a fundamental step in supervised machine learning, which is likely used for a churn prediction task.

  • E. Correct.

    Evaluating the model's performance using appropriate metrics ensures that the model meets the required accuracy or effectiveness standards.

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