Microsoft AI-102 Dumps

According to the course AI-102T00: Designing and Implementing a Microsoft Azure AI Solution1, the correct sequence of actions to run model1 on Host1 is:

Export model1 to Host1

Run the container

Configure disk logging

The explanation and references are as follows:

Export model1 to Host1: This step is required to deploy the custom speech-to-text model to the Docker host. You can use the Azure portal or the Azure CLI to export the model as a container image2.

Run the container: This step is required to start the container and run the model on the Docker host. You can use the Docker CLI to run the container image3.

Configure disk logging: This step is optional but recommended to monitor the performance and health of the container. You can use the Docker CLI to configure disk logging for the container4.

You created five bots with Microsoft Bot Framework Composer, but want to make them appear as one unified bot for users, with dynamic routing depending on user input.

This is exactly the Bot Framework Orchestrator scenario. Orchestrator is the recognizer that allows you to dispatch incoming utterances to the correct skill (bot) or dialog.

Step 1 – Install Orchestrator package

To use Orchestrator inside Bot Framework Composer, you first install the Orchestrator package.

This provides the functionality for language understanding and routing.

Correct answer: F.

Step 2 – Change Recognizer/Dispatch type

By default, a bot project might use LUIS recognizer or Regex recognizer.

To use Orchestrator, you need to change the recognizer type to Orchestrator in Composer.

Correct answer: B.

Step 3 – Create an Orchestrator model

Orchestrator requires a trained model that maps utterances to intents or routes them to the correct skill/bot.

You must create an Orchestrator model for dispatch.

Correct answer: A.

Other Options (Incorrect):

C. Create a Composer extension → Not required for orchestrating multiple bots.

D. Enable WebSockets → Useful for streaming scenarios, not for bot orchestration.

E. Create a custom recognizer JSON file → Applies to building custom recognizers, not needed with Orchestrator.

To validate that an input language is supported for translation, call the Translator SDK’s GetLanguagesAsync with scope: "translation" to retrieve the supported languages. The call returns a GetLanguagesResult, whose Translation property is a dictionary keyed by language codes (e.g., "en", "it"). You can then check membership with ContainsKey(language). If the key exists, the language is supported; otherwise, it isn’t.

This matches the .NET Translator client design:

TextTranslationClient.GetLanguagesAsync(...) fetches supported languages.

GetLanguagesResult.Translation is a dictionary of translation-capable languages, so ContainsKey is the correct check.

Microsoft References

TextTranslationClient.GetLanguagesAsync (overview and signature).

Languages REST reference — use scope=translation to get languages supported for translation. Microsoft Learn

GetLanguagesResult class — Translation dictionary property.