AI-900: Azure Machine Learning Features and Capabilities

Learn about the features and capabilities of Azure Machine Learning, such as publishing predictive services, preparing data, and monitoring usage of used services, and how they can help you pass the AI-900 exam.

Question

What features and capabilities are available in Azure Machine Learning?

Select all that apply.

A. Publish predictive services
B. Prepare data
C. Monitor usage of used services

Answer

A. Publish predictive services
B. Prepare data
C. Monitor usage of used services

Explanation

Azure Machine Learning is a cloud-based service with a wide range of features and capabilities that help data scientists to prepare data, train models, publish predictive services, and monitor their usage.

The correct answer is A, B, and C. All of these features and capabilities are available in Azure Machine Learning.

Azure Machine Learning is a cloud-based service that allows data scientists and developers to build, deploy, and manage high-quality machine learning models faster and with confidence. It supports the end-to-end machine learning lifecycle, including data preparation, model building and training, validation, and deployment. It also offers management and monitoring capabilities, allowing users to track, log, and analyze data, models, and resources.

Some of the specific features and capabilities of Azure Machine Learning are:

• Publish predictive services: Azure Machine Learning enables users to publish their trained models as web services that can be consumed by other applications or users. Users can choose from various deployment options, such as Azure Kubernetes Service, Azure Container Instances, or Azure Machine Learning compute instances. Users can also configure authentication, scaling, and load balancing for their web services.
• Prepare data: Azure Machine Learning provides tools and services for data exploration and preparation. Users can access data and create and share datasets from various sources, such as Azure Blob Storage, Azure Data Lake Storage, Azure SQL Database, or Azure Synapse Analytics. Users can also use data labeling to annotate training data and manage labeling projects. Users can also use data preparation to transform, clean, and enrich their data using a graphical interface or code.
• Monitor usage of used services: Azure Machine Learning allows users to monitor the performance, health, and usage of their web services and compute resources. Users can view metrics, logs, and alerts for their web services and troubleshoot issues using Application Insights. Users can also view the status, cost, and utilization of their compute resources and manage them using Azure Machine Learning studio or SDK.

Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.