Free Practice Questions for Microsoft AI-102 Exam

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

Defender for Cloud Apps (A): Focused on monitoring SaaS app usage and cloud security, not image content moderation → ❌

Custom Vision (B): Can classify images, but requires building/training your own model → more development effort → ❌

Azure AI Vision (C): Can analyze images, including adult/racy content detection, but Content Safety is now the preferred service for moderation. Still valid → ✅

Azure AI Content Safety (D): Specifically designed for detecting and blocking harmful/inappropriate content in text and images → ✅

Document Intelligence (E): Extracts text/structure from documents, not relevant here → ❌

Correct Answer:

C. Azure AI Vision and D. Azure AI Content Safety

Comprehensive Detailed Explanation

When an indexer runs in Azure AI Search, it processes each document through a fixed series of stages:

Document cracking

The indexer opens the item (file/row) and extracts text, images, and metadata, creating the root of the enriched document (/document). This is always the first stage.

Field mappings

Next, source fields are mapped directly to target index fields before any transformations . Field mappings occur after document cracking but before skillset execution . Microsoft Learn

Skillset execution

Built-in or custom skills (for example, language and sentiment analysis in your scenario) run and write outputs into the in-memory enriched document. Microsoft Learn

Output field mappings

Because skill outputs live only in memory, you must define output field mappings to route selected nodes of the enriched document into index fields. This step comes after skillset execution.

Push to index

Finally, the indexer hands off raw and enriched content to the search index (the physical data structures) so it becomes searchable—i.e., the content is “pushed to the index.” Microsoft Learn

This order— document cracking → field mappings → skillset execution → output field mappings → push to index —ensures raw content is extracted, direct source-to-index mappings are applied, AI enrichment is performed, enriched outputs are projected to fields, and the completed documents are written into the index.

Microsoft Azure AI References

Indexers in Azure AI Search – Stages of indexing (Document cracking; Field mappings; Skillset execution; Output field mappings, with field mappings occurring before transformations). Microsoft Learn

Skillset concepts (Output of skillset is routed to the index via output field mappings after skillset execution). Microsoft Learn

AI enrichment concepts (Field mappings move raw source content to the index; output field mappings move enriched content; indexing ingests raw and enriched content into the physical index). Microsoft Learn

Annotations & enriched document tree (Enriched document is created during document cracking at /document). Microsoft Learn

You are creating an SSML file for a text-to-speech app using a custom neural voice.

Requirements:

Expresses a calm tone → corresponds to style (e.g., " gentle " ).

Imitates the voice of a young adult female → corresponds to role (e.g., " YoungAdultFemale " ).

Key Knowledge:

In Speech Synthesis Markup Language (SSML) with Microsoft Azure TTS:

< mstts:express-as > supports attributes such as style, role, styledegree, voice, etc.

role → controls persona/age/gender (e.g., " YoungAdultFemale " ).

style → controls the speaking tone/intonation (e.g., " gentle " , " calm " , " chat " ).

Code Completion:

< mstts:express-as role = " YoungAdultFemale " style = " gentle " >

How can I assist you?

< / mstts:express-as >

Verified Answer:

First dropdown: role

Second dropdown: style

Microsoft References:

Azure Speech SSML – express-as element

Speech synthesis styles and roles

✅ Final Answer:

role = " YoungAdultFemale "

style = " gentle "

In Question Answering (Custom question answering in Azure AI Language), you improve coverage for different user phrasings by adding alternative questions to the same answer. For example, adding “How much does < product > cost?” as an alternate question to the existing Q/A about “What is the price of < product > ?” enables the service to match both user queries to the same answer. After adding alternates, you must retrain and republish the project for changes to take effect. This directly addresses the gap and meets the goal.

References

Add and manage alternative questions in custom question answering; retrain/republish for updates.

https://learn.microsoft.com/azure/ai-services/language-service/question-answering/how-to/contribute-to-project

Custom question answering concepts and authoring workflow.

https://learn.microsoft.com/azure/ai-services/language-service/question-answering/overview

Within a search service, synonym maps are a global resource that associate equivalent terms, expanding the scope of a query without the user having to actually provide the term. For example, assuming " dog " , " canine " , and " puppy " are mapped synonyms, a query on " canine " will match on a document containing " dog " .

Create synonyms: A synonym map is an asset that can be created once and used by many indexes.

Step 1: Create a new project and load sample documents

Create a new project. Projects store your configurations and settings.

Step 2: Label the sample documents

When you create or open a project, the main tag editor window opens.

Step 3: Train a custom model.

Finally, train a custom model.

The requirements state:

All AI solution responses must have a confidence score ≥ 70%.

If the response confidence score is < 70%, the response must be improved by human input.

Members of the Consultant-Bookkeeper group must be able to process financial documents, which includes performing manual review when the AI confidence is below threshold.

Looking at the provided JSON:

" fields " : {

" ReceiptType " : {

" type " : " string " ,

" valueString " : " Itemized " ,

" confidence " : 0.672

},

" MerchantName " : {

" type " : " string " ,

" valueString " : " Tailwind " ,

" confidence " : 0.913

}

}

ReceiptType.confidence = 0.672 → this is below 0.7.

MerchantName.confidence = 0.913 → this is above 0.7.

Therefore, the correct condition for triggering manual review is:

documentResults.fields. < field > .confidence < 0.7

Now, evaluating the options:

A. documentResults.docType == " prebuilt:receipt "

Always true for receipts, does not check confidence. Not correct.

B. documentResults.fields.*.confidence < 0.7

This is the correct general expression: trigger manual review whenever any field confidence is below 0.7.

C. documentResults.fields.ReceiptType.confidence > 0.7

This would bypass manual review when ReceiptType has high confidence. The requirement is to trigger review when confidence < 0.7, so this is the opposite.

D. documentResults.fields.MerchantName.confidence < 0.7

This only checks one field (MerchantName). In the JSON, MerchantName has confidence 0.913 ( > 0.7), so this condition would not trigger, but ReceiptType clearly needs review. Too narrow.

Correct Answer: B. documentResults.fields.*.confidence < 0.7

Microsoft References

Azure AI Document Intelligence – Confidence scores

Human-in-the-loop for Document Intelligence

C: Analyze Receipt - Get Analyze Receipt Result.

Query the status and retrieve the result of an Analyze Receipt operation.

Request URL: https://{endpoint}/formrecognizer/v2.0-preview/prebuilt/receipt/analyzeResults/{resultId}

E: POST {Endpoint}/vision/v3.2/read/analyze

Use this interface to get the result of a Read operation, employing the state-of-the-art Optical Character Recognition (OCR) algorithms optimized for text-heavy documents.

Scenario: Contoso plans to develop a document processing workflow to extract information automatically from PDFs and images of financial documents

The document processing solution must be able to process standardized financial documents that have the following characteristics:

- Contain fewer than 20 pages.

- Be formatted as PDF or JPEG files.

- Have a distinct standard for each office.

*The document processing solution must be able to extract tables and text from the financial documents.

The document processing solution must be able to extract information from receipt images.