Mock sample for your project: AWS Shield API

AWS Support The AWS Support API Reference is intended for programmers who need detailed information about the AWS Support operations and data types. You can use the API to manage your support cases programmatically. The AWS Support API uses HTTP methods that return results in JSON format. You must have a Business or Enterprise Support plan to use the AWS Support API. If you call the AWS Support API from an account that does not have a Business or Enterprise Support plan, the SubscriptionRequiredException error message appears. For information about changing your support plan, see AWS Support. The AWS Support service also exposes a set of AWS Trusted Advisor features. You can retrieve a list of checks and their descriptions, get check results, specify checks to refresh, and get the refresh status of checks. The following list describes the AWS Support case management operations: Service names, issue categories, and available severity levels - The DescribeServices and DescribeSeverityLevels operations return AWS service names, service codes, service categories, and problem severity levels. You use these values when you call the CreateCase operation. Case creation, case details, and case resolution - The CreateCase, DescribeCases, DescribeAttachment, and ResolveCase operations create AWS Support cases, retrieve information about cases, and resolve cases. Case communication - The DescribeCommunications, AddCommunicationToCase, and AddAttachmentsToSet operations retrieve and add communications and attachments to AWS Support cases. The following list describes the operations available from the AWS Support service for Trusted Advisor: DescribeTrustedAdvisorChecks returns the list of checks that run against your AWS resources. Using the checkId for a specific check returned by DescribeTrustedAdvisorChecks, you can call DescribeTrustedAdvisorCheckResult to obtain the results for the check that you specified. DescribeTrustedAdvisorCheckSummaries returns summarized results for one or more Trusted Advisor checks. RefreshTrustedAdvisorCheck requests that Trusted Advisor rerun a specified check. DescribeTrustedAdvisorCheckRefreshStatuses reports the refresh status of one or more checks. For authentication of requests, AWS Support uses Signature Version 4 Signing Process. See About the AWS Support API in the AWS Support User Guide for information about how to use this service to create and manage your support cases, and how to call Trusted Advisor for results of checks on your resources.

Kinesis Video Streams Signaling Service is a intermediate service that establishes a communication channel for discovering peers, transmitting offers and answers in order to establish peer-to-peer connection in webRTC technology.

Doc Engage API - Amazon Pinpoint API

AWS Mobile Service provides mobile app and website developers with capabilities required to configure AWS resources and bootstrap their developer desktop projects with the necessary SDKs, constants, tools and samples to make use of those resources.

AWS OpsWorks Welcome to the AWS OpsWorks Stacks API Reference. This guide provides descriptions, syntax, and usage examples for AWS OpsWorks Stacks actions and data types, including common parameters and error codes. AWS OpsWorks Stacks is an application management service that provides an integrated experience for overseeing the complete application lifecycle. For information about this product, go to the AWS OpsWorks details page. SDKs and CLI The most common way to use the AWS OpsWorks Stacks API is by using the AWS Command Line Interface (CLI) or by using one of the AWS SDKs to implement applications in your preferred language. For more information, see: AWS CLI AWS SDK for Java AWS SDK for .NET AWS SDK for PHP 2 AWS SDK for Ruby AWS SDK for Node.js AWS SDK for Python(Boto) Endpoints AWS OpsWorks Stacks supports the following endpoints, all HTTPS. You must connect to one of the following endpoints. Stacks can only be accessed or managed within the endpoint in which they are created. opsworks.us-east-1.amazonaws.com opsworks.us-east-2.amazonaws.com opsworks.us-west-1.amazonaws.com opsworks.us-west-2.amazonaws.com opsworks.ca-central-1.amazonaws.com (API only; not available in the AWS console) opsworks.eu-west-1.amazonaws.com opsworks.eu-west-2.amazonaws.com opsworks.eu-west-3.amazonaws.com opsworks.eu-central-1.amazonaws.com opsworks.ap-northeast-1.amazonaws.com opsworks.ap-northeast-2.amazonaws.com opsworks.ap-south-1.amazonaws.com opsworks.ap-southeast-1.amazonaws.com opsworks.ap-southeast-2.amazonaws.com opsworks.sa-east-1.amazonaws.com Chef Versions When you call CreateStack, CloneStack, or UpdateStack we recommend you use the ConfigurationManager parameter to specify the Chef version. The recommended and default value for Linux stacks is currently 12. Windows stacks use Chef 12.2. For more information, see Chef Versions. You can specify Chef 12, 11.10, or 11.4 for your Linux stack. We recommend migrating your existing Linux stacks to Chef 12 as soon as possible.

The transactional data APIs for Amazon QLDB Instead of interacting directly with this API, we recommend using the QLDB driver or the QLDB shell to execute data transactions on a ledger. If you are working with an AWS SDK, use the QLDB driver. The driver provides a high-level abstraction layer above this QLDB Session data plane and manages SendCommand API calls for you. For information and a list of supported programming languages, see Getting started with the driver in the Amazon QLDB Developer Guide. If you are working with the AWS Command Line Interface (AWS CLI), use the QLDB shell. The shell is a command line interface that uses the QLDB driver to interact with a ledger. For information, see Accessing Amazon QLDB using the QLDB shell.

Amazon MQ is a managed message broker service for Apache ActiveMQ and RabbitMQ that makes it easy to set up and operate message brokers in the cloud. A message broker allows software applications and components to communicate using various programming languages, operating systems, and formal messaging protocols.

Amazon Personalize is a machine learning service that makes it easy to add individualized recommendations to customers.

Amazon Elastic Compute Cloud Amazon Elastic Compute Cloud (Amazon EC2) provides secure and resizable computing capacity in the AWS Cloud. Using Amazon EC2 eliminates the need to invest in hardware up front, so you can develop and deploy applications faster. Amazon Virtual Private Cloud (Amazon VPC) enables you to provision a logically isolated section of the AWS Cloud where you can launch AWS resources in a virtual network that you've defined. Amazon Elastic Block Store (Amazon EBS) provides block level storage volumes for use with EC2 instances. EBS volumes are highly available and reliable storage volumes that can be attached to any running instance and used like a hard drive. To learn more, see the following resources: Amazon EC2: AmazonEC2 product page, Amazon EC2 documentation Amazon EBS: Amazon EBS product page, Amazon EBS documentation Amazon VPC: Amazon VPC product page, Amazon VPC documentation AWS VPN: AWS VPN product page, AWS VPN documentation

Amazon Personalize can consume real-time user event data, such as stream or click data, and use it for model training either alone or combined with historical data. For more information see Recording Events.

With Application Auto Scaling, you can configure automatic scaling for the following resources: Amazon AppStream 2.0 fleets Amazon Aurora Replicas Amazon Comprehend document classification and entity recognizer endpoints Amazon DynamoDB tables and global secondary indexes throughput capacity Amazon ECS services Amazon ElastiCache for Redis clusters (replication groups) Amazon EMR clusters Amazon Keyspaces (for Apache Cassandra) tables Lambda function provisioned concurrency Amazon Managed Streaming for Apache Kafka broker storage Amazon SageMaker endpoint variants Spot Fleet (Amazon EC2) requests Custom resources provided by your own applications or services API Summary The Application Auto Scaling service API includes three key sets of actions: Register and manage scalable targets - Register Amazon Web Services or custom resources as scalable targets (a resource that Application Auto Scaling can scale), set minimum and maximum capacity limits, and retrieve information on existing scalable targets. Configure and manage automatic scaling - Define scaling policies to dynamically scale your resources in response to CloudWatch alarms, schedule one-time or recurring scaling actions, and retrieve your recent scaling activity history. Suspend and resume scaling - Temporarily suspend and later resume automatic scaling by calling the RegisterScalableTarget API action for any Application Auto Scaling scalable target. You can suspend and resume (individually or in combination) scale-out activities that are triggered by a scaling policy, scale-in activities that are triggered by a scaling policy, and scheduled scaling. To learn more about Application Auto Scaling, including information about granting IAM users required permissions for Application Auto Scaling actions, see the Application Auto Scaling User Guide.

Amazon Kinesis Data Analytics is a fully managed service that you can use to process and analyze streaming data using Java, SQL, or Scala. The service enables you to quickly author and run Java, SQL, or Scala code against streaming sources to perform time series analytics, feed real-time dashboards, and create real-time metrics.

Client that can be used to manage Azure Cognitive Search services and API keys.

Azure Cosmos DB Database Service Resource Provider REST API

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 Microsoft Azure Network management API provides a RESTful set of web services that interact with Microsoft Azure Networks service to manage your network resources. The API has entities that capture the relationship between an end user and the Microsoft Azure Networks service.

Mixed Reality Resource Provider Remote Rendering Resource API

API to Add/Remove/List Guest Diagnostics Configuration to Azure Resources

The Container Service Client.

Azure NotificationHub client

The Private DNS Management Client.