AI-102 Practice Test 35 — 15 Questions

Question 1

Refer to the exhibit. You send this request to the Azure AI Language Service for custom entity recognition. The response returns no entities. What is the most likely reason?

Accepted Answer

The projectName and deploymentName are missing from the body parameters. The Azure AI Language Service for custom entity recognition requires both `projectName` and `deploymentName` in the request body to identify which trained custom model to invoke. Without these parameters, the service cannot route the request to the correct custom model, so it returns no entities. The standard pre-built entity recognition does not require these fields, but custom entity recognition mandates them.

Answer

The text input is too short for entity recognition

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The language parameter is set incorrectly

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The model version is not specified

Question 2

Which TWO configurations are required to enable Azure AI Search to index content from an Azure SQL database?

Accepted Answer

Enable change tracking on the Azure SQL table. Option C is correct because Azure AI Search's SQL indexer relies on change tracking (or a rowversion/timestamp column) to detect which rows have been inserted, updated, or deleted since the last indexing run, so enabling change tracking on the Azure SQL table is required for incremental indexing. Option D is correct because the indexer must be given a data source object that specifies the connection string, table or view, and change-tracking policy for the Azure SQL database, which is the mandatory link between the search service and the SQL data. Option A is not required because a skillset is only needed for AI enrichment (for example OCR, key phrase extraction, or embedding generation), not for basic SQL-to-index ingestion. Option B is not required because semantic ranking is an optional query-time feature that improves relevance; it does not affect whether content can be indexed. Option E is not required because high availability is a resilience/uptime configuration for the SQL database and is unrelated to the indexer's ability to read and index data.

Answer

Create a custom skillset for data enrichment

Answer

Configure semantic ranking on the index

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Enable high availability on the Azure SQL database

Question 3

Drag and drop the steps to configure an Azure AI Search index with a custom skill into the correct order.

Accepted Answer

Create the search service, then Define the index, then Create the custom skill, then Set up the indexer with the skillset, then Run the indexer.. Start by creating the search service, define the index, then create the custom skill, set up the indexer with the skillset, and finally run it.

Answer

Create the custom skill, then Define the index, then Create the search service, then Set up the indexer with the skillset, then Run the indexer.

Answer

Define the index, then Create the search service, then Create the custom skill, then Set up the indexer with the skillset, then Run the indexer.

Answer

Set up the indexer with the skillset, then Create the custom skill, then Define the index, then Create the search service, then Run the indexer.

Question 4

You need to create a solution that extracts key-value pairs from scanned invoices using Azure AI Document Intelligence. The invoices have varying layouts. Which model should you use?

Accepted Answer

Prebuilt invoice model. The Prebuilt invoice model (Option D) is specifically designed to extract key-value pairs, line items, and other structured fields from invoices, even when layouts vary. It is trained on thousands of invoice samples and uses deep learning to handle diverse formats without requiring custom training, making it the correct choice for this scenario.

Answer

Layout model

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Custom extraction model

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Read model

Question 5

You are designing a solution to extract customer names and addresses from scanned handwritten forms. The forms are stored as images in Azure Blob Storage. The extraction must achieve high accuracy with minimal manual review. Which combination of Azure AI services should you use?

Accepted Answer

Azure AI Document Intelligence with a custom model trained on handwritten forms. Azure AI Document Intelligence's custom model capability allows you to train a model specifically on handwritten forms, enabling it to learn the unique handwriting patterns and layout structures present in your scanned documents. This tailored approach achieves high accuracy with minimal manual review, as the model is optimized for your specific form type rather than generic invoice or receipt templates.

Answer

Azure AI Document Intelligence with prebuilt invoice and receipt models

Answer

Azure AI Language Service with custom Named Entity Recognition (NER)

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Azure AI Computer Vision with OCR and Azure AI Search

Question 6

You are building a knowledge mining solution using Azure AI Search with AI enrichment. Which TWO built-in skills can be used to extract information from images embedded in documents?

Accepted Answer

Image Analysis skill. The Image Analysis skill (B) is correct because it invokes the Computer Vision service to extract visual features from embedded images, such as descriptions, tags, and celebrity or landmark detection, which is exactly the kind of image-derived information a knowledge mining pipeline needs. The OCR skill (C) is also correct because it extracts printed and handwritten text from image files (including images embedded in documents), producing text that can be mapped into the search index. The Entity Recognition skill (A) operates on text to identify entities like people, places, and organizations, not on image content. The Key Phrase Extraction skill (D) analyzes text to surface salient phrases and does not process images. The Text Translation skill (E) translates text between languages and likewise does not extract information from images.

Answer

Entity Recognition skill

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Key Phrase Extraction skill

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Text Translation skill

Question 7

Your company wants to build a custom Copilot for customer support using Microsoft Copilot Studio. The support team needs to query a backend CRM system securely using the Copilot. Which authentication method should you configure for the custom connector to the CRM?

Accepted Answer

Microsoft Entra ID OAuth 2.0. Microsoft Entra ID OAuth 2.0 is the correct authentication method because it provides secure, token-based delegated access to the backend CRM system without exposing long-lived credentials. Copilot Studio connectors that require user context and secure resource access should use OAuth 2.0 with Microsoft Entra ID, which supports modern authentication flows like authorization code grant and can enforce conditional access policies.

Answer

API key authentication

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Basic authentication

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Client certificate authentication

Question 8

A developer is creating a custom question answering project in Azure AI Language. The knowledge base contains product manuals in PDF format. Which step is essential before importing the PDFs?

Accepted Answer

Ensure PDFs are in a supported format and accessible. Before importing PDFs into a custom question answering project in Azure AI Language, the essential step is to ensure the PDFs are in a supported format (e.g., searchable PDF, not scanned images without OCR) and accessible via a valid URL or local path. This is because the import process relies on the service being able to read and extract text from the documents; unsupported or inaccessible files will cause the import to fail.

Answer

Create an Azure AI Search index

Answer

Deploy a QnA Maker service

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Translate PDFs to English

Question 9

You need to monitor the performance of an Azure AI Language service custom entity recognition model. Which metric should you track to evaluate the model's ability to correctly identify entities?

Accepted Answer

F1 score. The F1 score is the standard metric for evaluating custom entity recognition models in Azure AI Language, as it balances precision (correctly identified entities) and recall (missed entities). Unlike accuracy, which can be misleading due to class imbalance in entity labeling, F1 provides a harmonic mean that reflects the model's ability to correctly identify entities without bias toward the majority class.

Answer

Throughput

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Response latency

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Accuracy

Question 10

Your organization has a large set of PDF invoices stored in Azure Blob Storage. You need to extract line-item details (product names, quantities, prices) and store them in Azure SQL Database for downstream reporting. The invoices have varied layouts. Which Azure AI service should you use?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is the correct service because it is specifically designed to extract structured data (like line-item details) from documents with varied layouts, such as invoices. Its prebuilt invoice model can parse product names, quantities, and prices from PDFs without requiring manual template configuration, and it outputs the data in a structured format that can be ingested into Azure SQL Database.

Answer

Azure AI Computer Vision

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Azure AI Language Service

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Azure AI Search

Question 11

You are reviewing an index definition created with PowerShell. The index is used for a knowledge mining solution that extracts people and organizations from documents. Users report that when they type partial names in the search bar, the suggester does not return suggestions. What is the most likely reason?

Accepted Answer

The suggester sourceFields do not include people or organizations. A suggester in Azure Cognitive Search only returns suggestions for fields explicitly listed in its `sourceFields` property. If the `people` and `organizations` fields are not included in `sourceFields`, the suggester cannot match partial names typed in the search bar, even if those fields are indexed and searchable. The suggester relies on prefix matching against the specified source fields to generate suggestions.

Answer

The people and organizations fields should be Edm.String instead of Collection(Edm.String)

Answer

The id field is not defined as a key in the index

Answer

The suggester searchMode should be 'analyzingInfixMatching' which is incorrect

Question 12

A healthcare organization uses Azure Document Intelligence to process patient intake forms. They notice that the confidence scores for field extraction are low. What is the most likely cause?

Accepted Answer

The custom model was trained with only 10 labeled forms. Custom models in Azure Document Intelligence require a minimum of five labeled forms for training, but low confidence scores typically indicate insufficient training data. With only 10 labeled forms, the model lacks enough examples to generalize well across variations in handwriting, formatting, and field values, leading to poor extraction confidence.

Answer

The document resolution is too low

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The document layout is not analyzed

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The batch processing size is too large

Question 13

Your company is building a knowledge base for customer support using Azure AI Search. You have a large dataset of customer emails stored in Azure Blob Storage. The solution must extract key phrases, detect sentiment, and identify customer intents (e.g., complaint, inquiry, feedback). You plan to use built-in AI skills for key phrase extraction and sentiment detection. For intent identification, you need a custom solution because the intents are specific to your business. You have trained a custom Language Understanding (LUIS) model and published it. How should you integrate the LUIS model into the Azure AI Search enrichment pipeline to extract intents?

Accepted Answer

Create a custom skill in the skillset that calls the LUIS endpoint and returns the top intent.. Azure AI Search enrichment pipelines support built-in skills (key phrase extraction, sentiment, entity recognition, OCR, etc.) and custom skills. A custom skill is a Web API endpoint that the skillset calls during enrichment, receiving a JSON payload and returning enriched fields. To integrate a published LUIS model, you create a custom skill (typically an Azure Function) that calls the LUIS prediction endpoint with the document text and returns the top intent and score, which the indexer then maps into the search index.

Answer

Add a Document Intelligence skill to classify intents.

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Configure the index to use a custom analyzer to parse intents.

Answer

Use the built-in Entity Recognition skill to extract intents.

Question 14

Which TWO Azure services can be used to implement a conversational AI solution that understands user intent and responds appropriately?

Accepted Answer

Azure Bot Service. Azure Bot Service (A) is correct because it provides the bot framework and channel integration needed to host a conversational AI solution that receives user messages and returns appropriate responses across channels like Teams, web chat, and Slack. Conversational Language Understanding (B), part of Azure AI Language, is correct because it is specifically designed to extract user intent and entities from natural language utterances, which is the core capability required for understanding what the user wants. Together, these services directly address intent recognition and conversational response handling. Azure AI Speech-to-Text (C) only transcribes spoken audio into text and does not determine intent or generate conversational responses. Azure AI Translator (D) performs language translation and does not provide intent understanding or dialogue management. Azure AI Search (E) is a search indexing and retrieval service, not a conversational intent or bot response service.

Answer

Azure AI Speech-to-Text

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Azure AI Translator

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Azure AI Search

Question 15

A financial services company uses Azure AI Language to analyze customer support transcripts. They want to identify the main topics discussed in each conversation and generate a summary of the key points. The solution must minimize development effort and use prebuilt functionality. You need to recommend the appropriate Azure AI Language features. What should you use?

Accepted Answer

Key phrase extraction and conversation summarization.. Key phrase extraction identifies the main topics in text, and conversation summarization produces a summary of key points from a conversation. Both are prebuilt Azure AI Language features requiring no custom training, minimizing development effort. Together they meet the requirement to identify topics and summarize transcripts.

Answer

Custom Named Entity Recognition (NER) and conversation summarization.

Answer

Entity linking and conversation summarization.

Answer

Sentiment analysis and key phrase extraction.