AI-900 Question 130
Single answerA data scientist wants to create a machine learning model to predict customer churn but has limited experience in hyperparameter tuning and feature engineering. How can Azure's Automated Machine Learning (AutoML) assist in this scenario?
- A
It automatically selects the best model and optimizes hyperparameters based on the dataset.
- B
It generates visualization dashboards for manual model performance comparison.
- C
It automates the deployment of the trained model directly to a web service.
- D
It provides a drag-and-drop interface to manually build machine learning pipelines.
Show answer and explanation
Correct answer: A
Explanation
Azure Automated Machine Learning (AutoML) simplifies the machine learning process by automating tasks such as algorithm selection, hyperparameter tuning, and feature engineering. This is especially beneficial for users with limited experience in these areas, allowing them to focus on leveraging the best model for their data without requiring extensive manual intervention.
- A. Correct.
Correct: Azure AutoML automatically selects the best model and optimizes hyperparameters by iterating through different algorithms and configurations.
- B. Incorrect.
Incorrect: While visualization tools are available in Azure ML, AutoML itself focuses on automating model selection and tuning rather than manual comparison dashboards.
- C. Incorrect.
Incorrect: Although Azure Machine Learning enables deployment, AutoML's primary function is model creation and optimization, not deployment automation.
- D. Incorrect.
Incorrect: AutoML does not provide a drag-and-drop interface; it automates the machine learning process programmatically or through minimal configuration.