Mock sample for your project: Power BI Embedded Management Client API

Use these APIs to manage Visual Studio Team Services resources through the Azure Resource Manager. All task operations conform to the HTTP/1.1 protocol specification and each operation returns an x-ms-request-id header that can be used to obtain information about the request. You must make sure that requests made to these resources are secure. For more information, see https://docs.microsoft.com/en-us/rest/api/index.

The Admin Storage Management Client.

The Container Service Client.

Fabric location operation endpoints and objects.

Azure Event Hubs client

Azure Application Insights client for web test based alerting.

The Admin Subscriptions Management Client.

APIs for managing software update configurations.

The Admin Storage Management Client.

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.

Azure Log Analytics API reference for Query Packs management.

Azure Event Hubs client

The HDInsight Management Client.

The Compute Management Client.

The Azure management API provides a RESTful set of web services that interact with Azure Key Vault.

REST API for Azure Logic Apps.

The Azure management API provides a RESTful set of web services that interact with Azure Key Vault.

An API for QnAMaker Service

The Anomaly Detector API detects anomalies automatically in time series data. It supports two kinds of mode, one is for stateless using, another is for stateful using. In stateless mode, there are three functionalities. Entire Detect is for detecting the whole series with model trained by the time series, Last Detect is detecting last point with model trained by points before. ChangePoint Detect is for detecting trend changes in time series. In stateful mode, user can store time series, the stored time series will be used for detection anomalies. Under this mode, user can still use the above three functionalities by only giving a time range without preparing time series in client side. Besides the above three functionalities, stateful model also provide group based detection and labeling service. By leveraging labeling service user can provide labels for each detection result, these labels will be used for retuning or regenerating detection models. Inconsistency detection is a kind of group based detection, this detection will find inconsistency ones in a set of time series. By using anomaly detector service, business customers can discover incidents and establish a logic flow for root cause analysis.

The DevTest Labs Client.