Free Microsoft AI-102 Practice Exam with Questions & Answers

Comprehensive Detailed Explanation

You are working with a custom question answering project in Azure AI Language (formerly QnA Maker). The requirement is to enable the chatbot to engage in multi-turn conversations.

Multi-turn conversation: This occurs when a bot needs to ask clarifying questions or present follow-up options to guide the user toward the right answer.

Now let’s evaluate the options:

A. Add alternate questions

Alternate questions help improve query matching by allowing different phrasings of the same question.

This does not enable multi-turn dialogue.

B. Enable chit-chat

Chit-chat adds conversational personality (e.g., greetings, small talk).

Useful for user engagement but not for structured multi-turn dialogues.

C. Add follow-up prompts ✅

Follow-up prompts allow you to define next-step questions after an answer.

They are the key mechanism in custom question answering to enable multi-turn conversation flows.

Example: If the answer is about " subscriptions, " the follow-up prompts might include " How to upgrade? " or " How to cancel? "

D. Enable active learning

Active learning helps improve accuracy by suggesting alternative phrasing for questions based on user feedback.

This is for model refinement, not multi-turn conversation.

Thus, the correct way to configure the project for multi-turn conversations is to add follow-up prompts.

Correct Answer: C. Add follow-up prompts

Microsoft References

Custom question answering in Azure AI Language – Multi-turn conversations

Custom question answering overview

Box 1: QnA Maker Editor

Scenario: Provide all employees with the ability to edit Q & As.

The QnA Maker Editor (read/write) has the following permissions:

Create KB API

Update KB API

Replace KB API

Replace Alterations

" Train API " [in

new service model v5]

Box 2: Contributor

Scenario: Only senior managers must be able to publish updates.

Contributor permission: All except ability to add new members to roles

Scenario: All videos must have transcripts that are associated to the video and included in product descriptions.

Product descriptions, transcripts, and all text must be available in English, Spanish, and Portuguese.

Step 1: Upload the video to blob storage

Given a video or audio file, the file is first dropped into a Blob Storage. T

Step 2: Index the video by using the Video Indexer API.

When a video is indexed, Video Indexer produces the JSON content that contains details of the specified video insights. The insights include: transcripts, OCRs, faces, topics, blocks, etc.

Step 3: Extract the transcript from the Video Indexer API.

Step 4: Translate the transcript by using the Translator API.

 The text value must use JSON-formatted text. No

 The command will analyze inputted text and identify whether the text contains published song lyrics. Yes

 The Ocp-Apim-Subscription-Key value must contain the ID of the Azure subscription that hosts Resource1. No

Comprehensive Detailed Explanation

The cURL calls the Protected Material detection endpoint:

POST {endpoint}/contentsafety/text:detectProtectedMaterial?api-version=2024-09-01.

This API expects a JSON body with a text property whose value is a plain string (the content to check). The body is JSON, but the text value itself is not required to be “JSON-formatted text”; it’s just ordinary text inside a JSON payload. Hence, statement 1 is No . Microsoft Learn

The Protected Material (text) feature “flags any known text content … such as song lyrics , articles, recipes, and selected web content.” Therefore, this command will analyze the input and can identify whether it contains published song lyrics. Statement 2 is Yes .

Ocp-Apim-Subscription-Key is the API (resource) key used to authenticate requests to Azure AI services or APIs fronted by API Management. It is not the Azure subscription ID . Authentication guidance explicitly states the header carries a resource key or token; API Management documentation identifies Ocp-Apim-Subscription-Key as the subscription key header. Therefore, statement 3 is No .

Microsoft Azure AI References

Detect Text Protected Material – REST API (endpoint, request/response schema): Microsoft Learn

Protected material detection (concepts; includes song lyrics examples): Microsoft Learn

What’s new in Azure AI Content Safety (mentions protected material detection for lyrics): Microsoft Learn

Azure AI services authentication (resource key in headers): Microsoft Learn

API Management subscriptions (meaning of Ocp-Apim-Subscription-Key): Microsoft Learn

Scenario: When the response confidence score is low, ensure that the chatbot can provide other response options to the customers.

When no good match is found by the ranker, the confidence score of 0.0 or " None " is returned and the default response is " No good match found in the KB " . You can override this default response in the bot or application code calling the endpoint. Alternately, you can also set the override response in Azure and this changes the default for all knowledge bases deployed in a particular QnA Maker service.

Choosing Ranker type: By default, QnA Maker searches through questions and answers. If you want to search through questions only, to generate an answer, use the RankerType=QuestionOnly in the POST body of the GenerateAnswer request.

Correct Sequence:

Add the prebuilt domain ToDo

Add a new application

Train the application

Publish the application

Comprehensive Detailed Explanation

You are building a Conversational Language Understanding (CLU) model for a chatbot that supports task tracking. To minimize development time, the best approach is to use a prebuilt domain (like ToDo) instead of building intents/entities manually.

Steps in correct order:

Add the prebuilt domain ToDo

Prebuilt domains in CLU provide ready-to-use intents and entities for common scenarios (e.g., calendar, email, ToDo).

This significantly reduces development time since you don’t need to create all intents/entities manually.

Add a new application

You must first create a CLU application that uses the prebuilt domain.

The application acts as the container for intents, entities, and utterances.

Train the application

Training is required after adding intents, entities, and utterances (or using a prebuilt domain).

This ensures the model can classify and extract information correctly.

Publish the application

Once trained, you must publish the application so it can be accessed via an endpoint and integrated into the chatbot.

Incorrect Actions in this Case

Add example utterances → Not required if you’re using the prebuilt ToDo domain (utterances are already included).

Correct Sequence:

Add the prebuilt domain ToDo

Add a new application

Train the application

Publish the application

Microsoft References

Conversational Language Understanding (CLU) overview

Prebuilt domains in CLU (LUIS ToDo example)

Train and publish CLU models

Box 1: Azure Blob storage

At the start of the pipeline, you have unstructured text or non-text content (such as images, scanned documents, or JPEG files). Data must exist in an Azure data storage service that can be accessed by an indexer.

Box 2: Computer Vision API

Scenario: Provide users with the ability to search insight gained from the images, manuals, and videos associated with the products.

The Computer Vision Read API is Azure ' s latest OCR technology (learn what ' s new) that extracts printed text (in several languages), handwritten text (English only), digits, and currency symbols from images and multi-page PDF documents.

Box 3: Translator API

Scenario: Product descriptions, transcripts, and all text must be available in English, Spanish, and Portuguese.

Box 4: Azure Files

Scenario: Store all raw insight data that was generated, so the data can be processed later.

Parameter type: stream

Visual features list: VisualFeatureTypes.Description

Result to use: results.Description.Captions[0]

To generate accessible alt text , you should use the caption produced by Azure Computer Vision’s Description feature (it produces a human-readable sentence with a confidence score). Therefore:

The image input should be a stream , because you’re uploading images (not just passing URLs) during product creation and AnalyzeImage…Async supports image streams .

Request the Description feature in VisualFeatureTypes so the service returns results.Description.Captions.

Use results.Description.Captions[0] and return the caption text if its confidence is high enough (e.g., > 0.5) to meet the accessibility requirement that all images must have relevant alt text .

Other features (Tags, Objects, Brands) are useful for enrichment but do not directly return natural-language captions suitable for alt text.

Microsoft Azure AI Solution References

Computer Vision (Image Analysis) – Description/Captions and features: Microsoft Docs, Image Analysis – VisualFeatureTypes.Description returns description.captions.

https://learn.microsoft.com/azure/ai-services/computer-vision/concept-image-analysis

SDK usage (analyze image from a stream): Microsoft Docs, Analyze an image by using the Computer Vision client library .

https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/call-analyze-image?tabs=version-3-2#analyze-an-image-from-a-stream

Box 1: api-nam.cognitive.microsofttranslator.com

https://docs.microsoft.com/en-us/azure/cognitive-services/translator/reference/v3-0-reference

Box 2: /translate