Azure Data Scientists Associate Practice Exam

What is a key characteristic of the Experiment.submit() method in Azure Machine Learning?

1. It's used for normalizing data.

2. It can only be used locally on a PC.

3. It submits a training script to a remote compute target.

4. It retrieves model registration information.

The correct answer is: It submits a training script to a remote compute target.

The Experiment.submit() method in Azure Machine Learning is designed to submit a training script to a specified compute target, which is often a remote compute resource such as Azure Virtual Machines or Azure Machine Learning Compute clusters. This allows for scalable training of machine learning models, leveraging the computational power and resources that Azure provides. When using this method, you can easily manage and orchestrate model training workflows, while also ensuring that the training jobs run in a controlled and isolated environment provided by Azure. As a result, it simplifies the deployment and scaling of experiments, which is a critical requirement for data scientists working with large datasets or complex machine learning models. In contrast, normalizing data is an important preprocessing step in machine learning but is not related to the responsibilities of the Experiment.submit() method. The method is specifically for submitting and managing training jobs rather than performing data transformations directly. Additionally, the method is not limited to local usage; it is explicitly designed for cloud-based scenarios, enabling users to take full advantage of Azure's cloud capabilities for distributed computing. Lastly, while model registration is an essential part of the machine learning lifecycle, retrieving model registration information is handled by other methods in the Azure Machine Learning SDK, making this choice unrelated to the functionality of Experiment.submit().