Explanation:

Comprehensive Detailed Explanation
Active learning in Language Understanding (LUIS / Conversational Language Understanding) works by collecting real user queries sent to your prediction endpoint.
By setting log=true in the prediction endpoint query, these user utterances are stored in the LUIS service.
The logged utterances can then be reviewed in the Review endpoint utterances page of the LUIS portal, where you can relabel and retrain the model. This enables the active learning loop.
Why not the others?
A). show-all-intents=true: This simply forces the endpoint to return all intents, not related to active learning.
B). Enable speech priming: Used for improving speech-to-text accuracy, unrelated to LUIS active learning.
D). Enable sentiment analysis: Adds sentiment scores to prediction output, not related to active learning.
Reference:
LUIS active learning with endpoint utterances
utterances#log-user-queries-to-enable-active-learning

Semantic Kernel supports multiple prompt template languages. Handlebars and Liquid both allow you to bind complex objects (with nested/sub-properties), iterate over collections, and use conditionals-ideal when your prompt needs structured data.
JSONL is a dataset/record format, not a prompt templating language.
YAML is a configuration/serialization format; while SK can store configs in YAML, YAML itself isn't a prompt templating engine for rendering nested objects in prompts.
"Semantic Kernel" as an option is not a template language; SK provides the runtime and also a simple legacy template syntax, but for complex/nested objects Microsoft recommends Handlebars (cross-language) or Liquid (.NET).
References (Microsoft Docs):
Handlebars prompt templates in Semantic Kernel (supports SK prompts, expressions, iteration, object binding). Microsoft Learn Liquid prompt templates in Semantic Kernel (example shows passing objects and iterating over nested data).
Microsoft Learn
SK prompt template syntax overview (context on SK templating options). Microsoft Learn

According to the Microsoft documentation, Computer Vision is a cloud-based service that provides developers with access to advanced algorithms for processing images and returning information. By uploading an image or specifying an image URL, Computer Vision algorithms can analyze visual content in different ways based on inputs and user choices.
According to the Microsoft documentation, image type detection is one of the features of Computer Vision that can categorize an image as either a photograph or a drawing. You can use the image type detection feature by calling the Analyze Image API with the visualFeatures parameter set to ImageType. The API will return a JSON response with an imageType field that indicates whether the image is a photo or a clipart.
According to the Microsoft documentation, image descriptions is another feature of Computer Vision that can generate a caption for an image. You can use the image descriptions feature by calling the Analyze Image API with the visualFeatures parameter set to Description. The API will return a JSON response with a description field that contains a list of captions for the image, each with a confidence score.
Therefore, by using these two features of Computer Vision, you can achieve your app requirements with minimal development effort. You don't need to use any other services, such as object detection, content tags, or Custom Vision, which are designed for different purposes.

Explanation:

Azure Cosmos DB supports multiple APIs, each aligned to a specific data model:
Gremlin API
Designed for graph data.
Stores vertices (nodes) and edges (relationships).
Query language: Gremlin.
Correct mapping: Graph data.
MongoDB API
Designed for document data using JSON.
Supports MongoDB wire protocol.
Correct mapping: JSON documents.
Table API
Designed for key-value data.
Compatible with Azure Table Storage.
Correct mapping: Key/value data.
Cassandra API
Designed for wide-column (column-family) data.
Uses CQL (Cassandra Query Language).
Not listed in the answer area, but if included, it maps to columnar data, not the options shown here.
Correct Mapping:
Graph data # Gremlin API
JSON documents # MongoDB API
Key/value data # Table API
Microsoft References
APIs in Azure Cosmos DB
Choose the right API for Azure Cosmos DB