AI-102 Exam Dumps - Microsoft Azure AI Engineer Associate Questions and Answers

Comprehensive Detailed Explanation

You’re analyzing a PDF (Test.pdf) that “contains tabular data.” In Azure AI Document Intelligence, the Layout prebuilt model is the right choice when your primary goal is to extract document structure such as tables, lines, words, selection marks, and paragraphs. Microsoft’s documentation clearly states that the Layout model “extracts text, tables, selection marks, and document structure,” which is exactly what you need for tabular content. Microsoft Learn

While the General document (prebuilt-document) model can also return tables, it is intended for broader extraction (including key-value pairs, entities, etc.). For a document whose key requirement is table extraction, Microsoft guidance recommends starting with Layout for structure-first scenarios.

For authentication in the REST call, the correct header to pass your resource key is Ocp-Apim-Subscription-Key . This is explicitly shown in the official REST API reference for the Analyze Document operation under Security (Type: apiKey, In: header). Microsoft Learn

Putting it together, the command shape is:

curl -v -i POST " {endpoint}/formrecognizer/documentModels/prebuilt-layout:analyze?api-version=2023-07-31 " \

-H " Content-Type: application/json " \

-H " Ocp-Apim-Subscription-Key: {yourkey} " \

--data-ascii " {\ " urlSource\ " : \ " test.pdf\ " } "

This uses a single analyze call to the prebuilt-layout model to extract the tables and authenticates with the Ocp-Apim-Subscription-Key header—matching the two selections in the answer area. The model matrix and overview pages also confirm Layout supports tables. Microsoft Learn

References (Microsoft Documentation)

Document layout analysis (Layout model) – capabilities include extracting tables, text, selection marks, and structure. Microsoft Learn

Choose the best Document Intelligence model – guidance on when to use Layout (structure-first; tables). Microsoft Learn

General document (prebuilt-document) model – broader extraction including key-value pairs and tables. Microsoft Learn

Model overview / matrix – shows Layout supports Tables. Microsoft Learn

Analyze Document (REST API) – Ocp-Apim-Subscription-Key header for authentication. Microsoft Learn

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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

Comprehensive Detailed Explanation

The requirement is to build an Adaptive Card for the chatbot that supports multilingual product display and meets accessibility requirements.

Product Name Display

The product JSON stores localized names like " name " : { " en " : " ... " , " es " : " ... " , " pt " : " ... " } .

To dynamically render based on the current language, you must use name[language].

This ensures the product name shown in the card adapts automatically to the user’s preferred language.

Stock Level Warning

Business rules require warnings when stock is low or out of stock.

This means you display the stock warning only when stockLevel is not ' OK ' .

Therefore, the correct condition is:

" $when " : " ${stockLevel != ' OK ' } "

Image Alt Text for Accessibility

Accessibility requirements specify that all images must have alt text in English, Spanish, and Portuguese.

In the product JSON, " alttext " is also localized:

" altText " : { " en " : " Bicycle " , " es " : " Bicicleta " , " pt " : " Bicicleta " }

To render the correct localized alt text dynamically, use image.altText[language].

Correct Selections:

First blank: name[language]

Second blank: " $when " : " ${stockLevel != ' OK ' } "

Third blank: image.altText[language]

Microsoft References

Adaptive Cards Templating

Adaptive Card $when property

Multilingual data in Azure Cognitive Search and Adaptive Cards

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.

When you want to debug a chatbot endpoint remotely that was built using the Microsoft Bot Framework, you need tools that allow you to test and tunnel your bot service to your local development environment.

1. Bot Framework Emulator

This is the official testing and debugging tool for bots built with the Microsoft Bot Framework.

It allows developers to:

Connect to a bot running locally or remotely.

Inspect messages sent and received.

Debug conversations and payloads in detail.

Required for local testing of the bot.

Correct.

2. ngrok

Ngrok is a tunneling tool that exposes your local development server (localhost) to the internet via a secure tunnel.

This is essential when debugging remotely because the Bot Framework Service (Azure Bot Service) requires a publicly accessible HTTPS endpoint for communication.

With ngrok, your local bot endpoint (e.g., http://localhost:3978/api/messages ) is mapped to a secure public endpoint (e.g., https://randomstring.ngrok.io/api/messages ).

Correct.

Incorrect Options

A. Fiddler

A web debugging proxy. Useful for monitoring traffic, but not required for Bot Framework remote debugging.

Incorrect.

B. Bot Framework Composer

A tool for building conversational bots visually. Not required for debugging an existing endpoint.

Incorrect.

D. Bot Framework CLI

Used for bot project management and configuration. Not specifically required for remote debugging endpoints.

Incorrect.

F. nginx

A web server/reverse proxy. Not needed for this scenario.

Incorrect.

Correct Answer: C. Bot Framework Emulator, E. ngrok

Microsoft References

Debug a bot locally using Bot Framework Emulator

Use ngrok with Bot Framework Emulator

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

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.

 HTTP Method: POST

 Visual Feature: imageType

Comprehensive Detailed Explanation

The question requires configuring an Azure AI Vision API request that determines if an image is clipart or a line drawing.

Step 1 – Determine the HTTP Method

The Analyze Image endpoint ( /analyze ) of Azure AI Vision expects an HTTP POST request.

GET is not valid for analysis with an image body.

PATCH is irrelevant here.

POST is correct because you are submitting image content or a URL for analysis.