Microsoft AI-102 Based on Real Exam Environment

The scenario involves a Language Understanding (LUIS) or Conversational Language Understanding (CLU) model that has:

An intent called FindContact (used for searching a contact list).

Training phrases like:

“Find contacts in London”

“Who do I know in Seattle?”

“Search for contacts in Ukraine”

Key Requirement

The model must recognize locations (London, Seattle, Ukraine) inside the FindContact intent.

Why the Solution is Incorrect

The proposed solution is to create a new intent for location.

This is not correct because:

Intents represent what the user wants to do (the action, e.g., FindContact ).

Entities represent details within the user’s utterance (the data, e.g., location ).

Here, the intent is already defined: FindContact .

Location should be modeled as an entity (Location entity), not as a separate intent.

Creating a new intent for each location (London, Seattle, Ukraine) would fragment the model and make it harder to generalize. Instead, the model should capture location as a parameter of the single intent FindContact .

Correct Approach

Keep the existing FindContact intent.

Add an entity for location (prebuilt GeographyV2 in LUIS or “Location” entity in CLU).

Train the model so that London , Seattle , Ukraine are recognized as entity values inside the FindContact intent.

Correct Answer: B. No

Microsoft References

Intents and entities in Language Understanding

Prebuilt entity: GeographyV2

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.

Instead, you could migrate to a Cognitive Search service that uses a higher tier.

Note: A simple fix to most throttling issues is to throw more resources at the search service (typically replicas for query-based throttling, or partitions for indexing-based throttling). However, increasing replicas or partitions adds cost, which is why it is important to know the reason why throttling is occurring at all.

Comprehensive Detailed Explanation

To move a Question Answering project to a different Azure region, you migrate the project data and then re-deploy it in the target region:

Export the existing project (source region).

In Language Studio (Question Answering), you first export the project. This produces a package (JSON/ZIP) containing your knowledge bases, sources, synonyms, and settings. Exporting is the supported way to move a project between resources/regions.

Import the project into the new Language service instance (target region).

In the target region’s Language service instance, use Language Studio to import the previously exported file. This recreates the project (KBs and configuration) under the new resource.

Train and publish from the new instance.

After import, you must train (build) and then publish the project so that an endpoint is created under the new regional resource. Clients can then point to the new endpoint and key.

Actions like “enable custom text classification” or “regenerate the keys” are not required for moving a Question Answering project, and training/publishing from the original instance won’t deploy to the target region.

Microsoft Azure AI Solution References

Export and import Question Answering projects in Language Studio (move between resources/regions):

Microsoft Learn – Export and import projects (Question Answering) .

Train and publish a Question Answering project:

Microsoft Learn – Create, train, and publish projects (Question Answering) .

Language service overview and regional deployment concepts:

Microsoft Learn – What is Azure AI Language?