For a social media messaging app that must identify the language of text messages in real time, the most direct SDK is the Azure AI Translator - Text Translation SDK. Its client libraries (e.g., Azure.AI.Translation.
Text in .NET) include a language detection operation that returns the detected language code and a confidence score, which is exactly what you need for real-time text scenarios.
Other options are not a fit:
* Microsoft.CognitiveServices.Speech is the Speech SDK for speech-to-text, TTS, and speech translation (audio), not text-only language detection.
* Azure.AI.Translation.Document targets asynchronous document translation workflows, not short text message detection. Microsoft Learn
* Azure.AI.Translation.Speech isn't the package used; speech translation is covered by the Speech SDK package above. Microsoft Learn Microsoft References
* Text Translation SDK overview (supports language detection): Azure AI Translator. Microsoft Learn
* DetectedLanguage in Azure.AI.Translation.Text (returned by detect operation). Microsoft Learn
* Quickstart: Translator text client libraries. Microsoft Learn
* Install/use the Speech SDK (contrast with text scenario). Microsoft Learn
* Document Translation SDK overview (not applicable to short text detection). Microsoft Learn

Explanation:

Comprehensive Detailed Explanation
When an indexer runs in Azure AI Search, it processes each document through a fixed series of stages:
* Document crackingThe indexer opens the item (file/row) and extracts text, images, and metadata, creating the root of the enriched document (/document). This is always the first stage.
* Field mappingsNext, source fields are mapped directly to target index fields before any transformations
. Field mappings occur after document cracking but before skillset execution. Microsoft Learn
* Skillset executionBuilt-in or custom skills (for example, language and sentiment analysis in your scenario) run and write outputs into the in-memory enriched document. Microsoft Learn
* Output field mappingsBecause skill outputs live only in memory, you must define output field mappings to route selected nodes of the enriched document into index fields. This step comes after skillset execution.
* Push to indexFinally, the indexer hands off raw and enriched content to the search index (the physical data structures) so it becomes searchable-i.e., the content is "pushed to the index." Microsoft Learn This order-document cracking # field mappings # skillset execution # output field mappings # push to index-ensures raw content is extracted, direct source-to-index mappings are applied, AI enrichment is performed, enriched outputs are projected to fields, and the completed documents are written into the index.
Microsoft Azure AI References
* Indexers in Azure AI Search - Stages of indexing (Document cracking; Field mappings; Skillset execution; Output field mappings, with field mappings occurring before transformations). Microsoft Learn
* Skillset concepts (Output of skillset is routed to the index via output field mappings after skillset execution). Microsoft Learn
* AI enrichment concepts (Field mappings move raw source content to the index; output field mappings move enriched content; indexing ingests raw and enriched content into the physical index). Microsoft Learn
* Annotations & enriched document tree (Enriched document is created during document cracking at
/document). Microsoft Learn

You want to prevent users from sharing sexually explicit images and minimize development effort.
Azure AI Content Safety Studio provides a no-code, browser-based experience to test and tune image moderation using the Content Safety service. It supports out-of-the-box classification for Sexual, Hate, Violence, and Self-harm categories, lets you adjust severity thresholds, review results, and export settings or sample code with minimal engineering work.
Other options:
* A. Visual Studio - an IDE, not a moderation service.
* B. Vision Studio in Azure AI Vision - focuses on vision tasks (OCR, image analysis). For policy
/compliance moderation of explicit content, Content Safety is the correct service.
* D. Azure AI Studio - general hub for building generative AI apps; not the specialized moderation experience.
Microsoft References
* Azure AI Content Safety overview and image moderation concepts.
* Content Safety Studio quickstart and scenarios.
* Content categories (Sexual, Hate, Violence, Self-harm).

Explanation:
account
The statement is:
"When provisioning an Azure Cosmos DB ______, you need to specify which type of API you will use." Options:
* account # Correct. When you create a Cosmos DB account, you must choose the API type (e.g., SQL API, MongoDB API, Cassandra API, Table API, Gremlin API). This decision defines the data model and query language. #
* container # A container is created inside a database (tables, collections, graphs) but does not determine the API type.
* database # Exists inside an account and inherits the API type from the account.
* item # Represents individual records/documents; not where the API is chosen.
Thus, the API choice is made at the account level.
The answer: account
* Azure Cosmos DB account overview
* Choose the right API for Cosmos DB

Explanation:

You are adding a custom plugin to a Semantic Kernel-based agent and you want function calling to be enforced and self-completing:
* To register a plugin that exposes one or more callable functions to the agent, use CreatePluginFromFunctions. This builds a plugin from Kernel functions (prompt- or code-based) and makes them available for tool/function calling by the model.
* To ensure the model must call functions (and request any required parameters it doesn't have), set the prompt execution settings to FunctionChoiceBehaviour.Required. In Semantic Kernel, this corresponds to the OpenAI tool/function-calling control that forces the assistant to invoke appropriate functions. When required parameters are marked in the function's schema, the assistant will ask follow- up questions to collect them before (or while) invoking the function.
References (Microsoft Azure AI Solution)
Semantic Kernel: Plugins and Functions, ChatCompletionAgent, OpenAIPromptExecutionSettings, and FunctionChoiceBehavior for enforced tool/function calling.