Mock sample for your project: ServiceBusManagementClient API

ARM Databricks

The Azure Management Groups API enables consolidation of multiple
subscriptions/resources into an organizational hierarchy and centrally
manage access control, policies, alerting and reporting for those resources.

Azure Log Analytics API reference for Solution.

The Microsoft Azure management API provides create, read, update, and delete functionality for Azure PostgreSQL resources including servers, databases, firewall rules, VNET rules, security alert policies, log files and configurations with new business model.

PowerBI Dedicated Web API provides a RESTful set of web services that enables users to create, retrieve, update, and delete Power BI dedicated capacities

This API exposes Azure Log Analytics query capabilities

Logical network operation endpoints and objects.

Public IP Address admin endpoints and objects.

The Microsoft Azure management API provides create, read, update, and delete functionality for Azure MySQL resources including servers, databases, firewall rules, VNET rules, log files and configurations with new business model.

The Admin Subscriptions Management Client.

Virtual Machine Image Template

API spec for Microsoft.Security (Azure Security Center) resource provider

Describes the interface for the per-tenant enclave service.

Use these REST APIs for performing operations on the ApiVersionSet entity associated with your Azure API Management deployment. Using this entity you create and manage API Version Sets that are used to group APIs for consistent versioning.

Role based access control provides you a way to apply granular level policy administration down to individual resources or resource groups. These operations enable you to manage role assignments. A role assignment grants access to Azure Active Directory users.

Use these REST APIs for performing operations on who is going to receive notifications associated with your Azure API Management deployment.

The Container Service Client.

REST API for Azure VirtualWAN As a Service.

IoT Analytics allows you to collect large amounts of device data, process messages, and store them. You can then query the data and run sophisticated analytics on it. IoT Analytics enables advanced data exploration through integration with Jupyter Notebooks and data visualization through integration with Amazon QuickSight. Traditional analytics and business intelligence tools are designed to process structured data. IoT data often comes from devices that record noisy processes (such as temperature, motion, or sound). As a result the data from these devices can have significant gaps, corrupted messages, and false readings that must be cleaned up before analysis can occur. Also, IoT data is often only meaningful in the context of other data from external sources. IoT Analytics automates the steps required to analyze data from IoT devices. IoT Analytics filters, transforms, and enriches IoT data before storing it in a time-series data store for analysis. You can set up the service to collect only the data you need from your devices, apply mathematical transforms to process the data, and enrich the data with device-specific metadata such as device type and location before storing it. Then, you can analyze your data by running queries using the built-in SQL query engine, or perform more complex analytics and machine learning inference. IoT Analytics includes pre-built models for common IoT use cases so you can answer questions like which devices are about to fail or which customers are at risk of abandoning their wearable devices.

Use these REST APIs for performing operations on entities like API, Product, and Subscription associated with your Azure API Management deployment.