Explanation:

Scenario Recap
You are building a streaming Speech-to-Text solution using the Speech SDK.
Input audio format: MP3 encoding
You must configure the audio stream format and the recognizer properly.
Code Analysis
Audio Format
For MP3 streams, the correct method is:
AudioStreamFormat.GetCompressedFormat(AudioStreamContainerFormat.MP3)
This creates an audio format object for compressed MP3 input.
Recognizer
Since the task is speech-to-text transcription, you need the SpeechRecognizer class.
Other options like KeywordRecognizer, SpeakerRecognizer, or SpeechSynthesizer are not appropriate (they handle keyword spotting, speaker identification, or text-to-speech).
Completed Code Snippet
var audioFormat = AudioStreamFormat.GetCompressedFormat(AudioStreamContainerFormat.MP3); var speechConfig = SpeechConfig.FromSubscription( " 18c51a87-3a69-47a8-aedc-a54745f708a1 " , " westus
" );
var audioConfig = AudioConfig.FromStreamInput(pushStream, audioFormat); using ( var recognizer = new SpeechRecognizer(speechConfig, audioConfig))
{
var result = await recognizer.RecognizeOnceAsync();
var text = result.Text;
}
Final Answer (Answer Area Selections)
AudioFormat: AudioStreamFormat.GetCompressedFormat
Recognizer: SpeechRecognizer
Microsoft References
Speech SDK - AudioStreamFormat
SpeechRecognizer class

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.

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

In Question Answering (Custom question answering in Azure AI Language), you improve coverage for different user phrasings by adding alternative questions to the same answer. For example, adding "How much does < product > cost?" as an alternate question to the existing Q/A about "What is the price of < product > ?" enables the service to match both user queries to the same answer. After adding alternates, you must retrain and republish the project for changes to take effect. This directly addresses the gap and meets the goal.
References
Add and manage alternative questions in custom question answering; retrain/republish for updates.
https://learn.microsoft.com/azure/ai-services/language-service/question-answering/how-to/contribute-to- project Custom question answering concepts and authoring workflow.
https://learn.microsoft.com/azure/ai-services/language-service/question-answering/overview

* When deploying Cognitive Services containers (such as Anomaly Detector), the container requires a billing parameter in the docker run command.
* This parameter points to the Azure resource key and endpoint for billing.
* Fluentd is for logging.
* Http Proxy is optional for outbound connectivity.
* Mounts are used for local storage but are not mandatory for billing.
The answer: B
Reference: Run Cognitive Services containers - Billing settings