For a table projection in a knowledge store (skillset definition), the table node must define:
* tableName: the table to project into (physical expression).
* generatedKeyName: the name of the system-generated key column.
* source: an object-shaping expression (projection path) describing what document content flows into the table.dataSource/dataSourceConnection are service-level objects used by the indexer, not properties of a table projection node.
References
* Knowledge store and projections schema: table projections require tableName, generatedKeyName, and source in the skillset JSON.

Explanation:
The subscription will be charged 86 tokens for the execution of the session. No The text completion was truncated because the Max response tokens value was exceeded. No The prompt_tokens value will be included in the calculation of the Max response tokens value. No From the response payload:
* usage.prompt_tokens = 37
* usage.completion_tokens = 86
* usage.total_tokens = 123
* finish_reason = "stop"
* Billing is for total tokens (input + output), not just completion tokens.Azure OpenAI bills on both prompt (input) and completion (output) tokens. Here, the total is 123 tokens, so it is not just 86. Hence No.
* Not truncated by max response tokens.If a response hits the maximum allowed output tokens, finish_reason would be "length". Here it is "stop" and the model produced only 86 completion tokens while Max response tokens was set to 100, so the limit was not reached. Hence No.
* max response tokens applies only to the generated output, not the prompt.max_tokens (Max response tokens) limits the number of tokens the model may generate in the completion; it does not include prompt_tokens. Hence No.
Microsoft References
* Azure OpenAI Service - Chat Completions API (parameters, max_tokens, response usage, finish_reason).https://learn.microsoft.com/azure/ai-services/openai/reference#chat-completions
* Azure OpenAI pricing and token usage model (charges for input and output tokens).https://learn.
microsoft.com/azure/ai-services/openai/overview#pricing
* Token counting and limits (context vs. max output tokens).https://learn.microsoft.com/azure/ai-services
/openai/concepts/tokenizers#token-limits

Explanation:

To let an Azure AI Agent access publicly available, recent web content, you add the Bing grounding tool. In the SDK, you first define a ToolConnectionList with the Bing connection ID (here, "bingConnectionId"), then construct a BingGroundingToolDefinition(connectionList) so the agent can call Bing Search with recency filters (e.g., last 90 days) when answering questions.
When creating the agent, tool definitions are passed via the tools: parameter of CreateAgentAsync(...) as a List<ToolDefinition>. The toolResources parameter is used when you must also supply resource descriptors (for example, an Azure AI Search index configuration), which is not needed here because the Bing grounding tool already references the connection through the ToolConnectionList.
Therefore:
* Use BingGroundingToolDefinition to ground the agent on current public web data.
* Provide that tool in tools: new List<ToolDefinition> { grounding } when calling CreateAgentAsync(...).
Microsoft Azure AI References (titles only)
* Azure AI Agent Service - Bing web grounding tool (tool definitions and connections)
* Azure AI Agent Service SDK - Agent creation (CreateAgentAsync parameters: tools vs toolResources)
* Azure AI Agent Service - Tool connections and ToolConnectionList usage

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

Explanation:
a tabular form of rows and columns
* Relational data is, by definition, data that adheres to the relational model.
* In the relational model, data is organized into tables, which consist of rows (records or tuples) and columns (fields or attributes). This is known as the tabular form.
* The other options are incorrect:
* Unstructured data is the opposite of relational data.
* A hierarchical folder structure is used for file organization, not the logical structure of relational data.
* Comma-separated value (CSV) files can contain relational data, but the core definition of relational data is the tabular structure, not the file format itself.