AI-900 Question 121
Single answerA data scientist at your organization wants to use Azure Machine Learning to build and deploy a machine learning model. They need an environment that supports automated model training, hyperparameter tuning, and deployment to a scalable endpoint without having to manage the underlying infrastructure. Which Azure Machine Learning capability should they use?
- A
Azure Machine Learning Designer
- B
Automated Machine Learning (AutoML)
- C
Azure Batch AI
- D
Azure Data Factory
Show answer and explanation
Correct answer: B
Explanation
Automated Machine Learning (AutoML) in Azure Machine Learning allows users to automate the process of training and optimizing machine learning models. It abstracts infrastructure management, enabling data scientists to focus on experimenting and deploying models efficiently. The described scenario aligns perfectly with the capabilities of AutoML.
- A. Incorrect.
Azure Machine Learning Designer is used for visually building machine learning workflows but does not specifically provide automated training, hyperparameter tuning, or direct deployment capabilities.
- B. Correct.
Automated Machine Learning (AutoML) enables automated model training, hyperparameter tuning, and deployment without requiring the user to manage infrastructure, making it the correct option.
- C. Incorrect.
Azure Batch AI was a legacy service for running machine learning and AI workloads but has been replaced by Azure Machine Learning and does not offer the features described in the scenario.
- D. Incorrect.
Azure Data Factory is primarily used for data integration and pipeline orchestration, not for model training, hyperparameter tuning, or deployment.