AI-102 Exam Dumps - Designing and Implementing a Microsoft Azure AI Solution

Step 1: The Authorization Header

When calling Azure AI Document Intelligence REST API, the correct header to authenticate is:

-H " Ocp-Apim-Subscription-Key: {yourKey} "

Key1 and Secret are not valid header names.

Subscription-Key is also not the standard header.

Correct choice: Ocp-Apim-Subscription-Key

Step 2: Selecting the Prebuilt Model

Since the file contains tabular data (tables, structured document), the right prebuilt model is:

prebuilt-document → extracts text, tables, structure, key-value pairs.

prebuilt-contract → specialized for contracts.

prebuilt-layout → extracts layout, lines, and words but not tables in structured form.

prebuilt-read → simple OCR only.

So the correct choice is: prebuilt-document

Final Completed Command

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

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

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

-d " { ' urlSource ' : ' https:// < your-storage > /test.pdf ' } "

Verified Answer:

Left box: Ocp-Apim-Subscription-Key

Right box: prebuilt-document

Microsoft References:

Azure AI Document Intelligence REST API

Prebuilt Document model

The requirement is to analyze video content to identify mentions of specific company names. This is essentially an audio transcript analysis task (speech-to-text followed by entity recognition). Azure Video Analyzer for Media integrates with Content model customization to detect such entities.

Step-by-step reasoning:

Sign in to the Azure Video Analyzer for Media website.

Azure Video Analyzer for Media (formerly Video Indexer) is the service used for analyzing videos and extracting insights such as speech, text, brands, and entities.

From Content model customization, select Language.

To recognize specific company names in transcripts, you must customize the Language model.

Customization allows you to add vocabulary (e.g., company names) so that speech recognition and transcription are tuned to correctly identify them.

Add the specific company names to the include list.

Adding them to the include list ensures the system actively looks for and recognizes those company names during transcript analysis.

The exclude list would suppress terms, not detect them, so it is not correct in this scenario.

Why not other options?

Sign in to the Custom Vision website → Custom Vision is for image classification/object detection, not audio/video transcript entity recognition.

From Content model customization, select Brands → The Brands model helps detect logos and existing brand mentions, but for custom company names not in the brand catalog, you must customize the Language model.

Exclude list → Would filter out terms, which is opposite of the requirement.

Correct Answer Order:

Sign in to the Azure Video Analyzer for Media website.

From Content model customization, select Language.

Add the specific company names to the include list.

Microsoft References

Customize models in Video Indexer

Azure Video Analyzer for Media overview

Content model customization in Video Indexer

Azure AI Content Safety supports custom blocklists —dictionaries of terms/phrases you define to detect obscure or domain-specific offensive terms in user content. Creating a blocklist is the minimal-effort, built-in way to meet this requirement; you don’t need to build a classifier.

References

Create and use blocklists in Azure AI Content Safety.

https://learn.microsoft.com/azure/ai-services/content-safety/concepts/blocklists

Text moderation overview (using built-in classifiers with optional blocklists).

https://learn.microsoft.com/azure/ai-services/content-safety/concepts/text-moderation

For consistent, deterministic answers about policy PDFs with minimal effort, use Azure AI Language – Question Answering (Custom Question Answering). You can ingest PDFs into a knowledge base and define/curate the exact answers. The service then returns the same answer for the same question every time (subject to your KB content and configuration), avoiding the variability of generative models and custom ML. Microsoft Azure +2

Microsoft References:

What is Custom Question Answering? (Language service). Microsoft Learn

Azure AI Language – Question Answering product page. Microsoft Azure

Azure AI Language overview (includes Question Answering feature)

For a table projection in a knowledge store (skillset definition), the table node must define:

tableName : the table to project into (physical expression).

generatedKeyName : the name of the system-generated key column.

source : an object-shaping expression (projection path) describing what document content flows into the table.

dataSource/dataSourceConnection are service-level objects used by the indexer, not properties of a table projection node.

References

Knowledge store and projections schema: table projections require tableName, generatedKeyName, and source in the skillset JSON.

Use " definition " for the first field and " sampleBlobUrl " for the second field

When creating a custom text category in Azure AI Content Safety, you call the PUT /contentsafety/text/categories/{categoryName} endpoint and provide a JSON body that includes:

categoryName : the identifier of your category.

definition : a natural-language description of what the category covers (e.g., “text prompts about learning advice while preparing for an exam”).

sampleBlobUrl : a pointer to labeled sample data (typically a JSONL file in Azure Storage) used to help the service understand the category.

Keys like prompt or text are used when analyzing content (for example, text:detect ), not when defining a category. Likewise, blocklistName applies to blocklist operations, and outputType is not part of the category-creation schema. Therefore, the correct payload fields to complete the cURL are definition and sampleBlobUrl .

Microsoft Azure AI References (titles only)

Azure AI Content Safety – Create or update custom text categories (REST)

Azure AI Content Safety – Custom categories concepts and training data (JSONL in blob storage)

Azure AI Content Safety – Text analysis vs. category management endpoints

You are asked to build an app that translates speech from English into French, German, and Spanish using Azure AI Speech Translation.

Step 1 – Define target languages

The SpeechTranslationConfig object requires language codes (not full names like " French " , " German " , " Spanish " ).

Valid language codes:

French → " fr "

German → " de "

Spanish → " es "

Therefore, the correct syntax for the output languages is:

var languages = new List < string > { " fr " , " de " , " es " };

Step 2 – Choose the recognizer type

IntentRecognizer : Used for intent recognition with LUIS, not translation.

SpeakerRecognizer : Used for speaker identification/verification, not translation.

SpeechSynthesizer : Used for text-to-speech, not translation.

TranslationRecognizer : Used specifically for speech-to-speech or speech-to-text translation.

Therefore, the correct recognizer is TranslationRecognizer.

Correct Answer:

Output Languages: { " fr " , " de " , " es " }

Recognizer: TranslationRecognizer

Microsoft References

Speech Translation with Azure AI Speech

TranslationRecognizer Class (C# API)

Supported language codes