AI-102 Practice Test 40 — 15 Questions

Question 1

Your organization is deploying Azure AI Document Intelligence to process invoices. You need to ensure that the solution meets compliance requirements by preventing data from being stored outside the European Union. What should you configure?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Create the Azure AI Document Intelligence resource in a region within the European Union and configure data residency settings.. Creating the Azure AI Document Intelligence resource in an EU region and configuring data residency settings ensures that all data processed by the service remains within the European Union, meeting compliance requirements. Azure AI Document Intelligence does not automatically guarantee data residency based solely on the resource region; explicit data residency configuration is required to prevent data from being stored or processed outside the specified geographic boundary.

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Deploy Azure AI Document Intelligence in a multi-region configuration across EU datacenters.

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Enable the Resource Firewall on the Azure AI Document Intelligence resource.

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Use a Private Endpoint to connect to the Azure AI Document Intelligence resource.

Question 2

Which TWO factors should you consider when choosing between Azure AI Document Intelligence and Azure AI Language for extracting information from documents?

Accepted Answer

Need to process handwritten text. Azure AI Document Intelligence (formerly Form Recognizer) is specifically designed to handle handwritten text through its 'Read' OCR model, which can extract printed and handwritten text from documents. Azure AI Language, on the other hand, focuses on natural language processing (NLP) tasks like sentiment analysis and entity recognition, and does not natively process handwritten content. Therefore, if your document contains handwritten notes, Document Intelligence is the appropriate service.

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Use of REST APIs

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Ability to extract key-value pairs

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Real-time processing requirements

Question 3

A company uses Azure Custom Vision to classify images of defective parts. After deploying the model, the accuracy is low. The team only has 10 images per class. What is the most effective way to improve accuracy?

Accepted Answer

Add at least 50 more images per class with variations.. Azure Custom Vision relies on deep learning models that require a sufficient number of diverse training images to generalize well. With only 10 images per class, the model is severely underfit and prone to overfitting; adding at least 50 more images per class with variations in lighting, angle, and background provides the necessary data diversity to improve accuracy significantly.

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Use a different classification algorithm.

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Reduce the image resolution to speed up training.

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Increase the number of training iterations (epochs).

Question 4

You are designing an NLP solution to analyze legal documents. The solution must identify specific clauses and parties involved. Which Azure AI service is most appropriate?

Accepted Answer

Custom Named Entity Recognition in Azure AI Language. Custom Named Entity Extraction (Custom NER) in Azure AI Language is the correct choice because it allows you to train a model to recognize domain-specific entities like legal clauses and party names from your own labeled data. Pre-built NER only recognizes generic entity types (e.g., person, organization, location) and cannot be customized for legal terminology. This makes Custom NER the only option that meets the requirement to identify specific clauses and parties unique to legal documents.

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Pre-built Named Entity Recognition in Azure AI Language

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Text Analytics for Health

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Immersive Reader

Question 5

You are developing a customer support chatbot using Azure OpenAI Service. The chatbot must only answer questions related to the company's product catalog and policies. You want to minimize the risk of the chatbot generating harmful or off-topic responses. Which approach should you use?

Accepted Answer

Use a system message that instructs the model to only answer product-related questions.. A system message sets the foundational behavior of the model by providing high-level instructions that guide all subsequent responses. By explicitly instructing the model to only answer product-related questions, you establish a clear boundary that minimizes off-topic or harmful outputs. This approach leverages the model's instruction-following capability, which is more effective than parameter tuning alone for content restriction.

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Set the max_tokens parameter to 100.

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Set the temperature parameter to 0.

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Set the top_p parameter to 0.1.

Question 6

Which TWO actions are valid ways to authenticate to Azure AI services?

Accepted Answer

Use an API key.. Azure AI services accept API keys as a straightforward authentication method. Each service generates a pair of keys that must be included in the `Ocp-Apim-Subscription-Key` header of HTTP requests. Option C is correct because Microsoft Entra ID (formerly Azure AD) authentication with a service principal is fully supported for Azure AI services, allowing token-based authentication via the `Authorization` header with a bearer token obtained from the Microsoft identity platform.

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Use a client certificate.

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Use a shared access signature (SAS) token.

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Use a managed identity.

Question 7

You are designing a solution that uses Azure AI Language to analyze customer feedback. The solution must detect sentiment, extract key phrases, and identify named entities. Which feature should you use?

Accepted Answer

Azure AI Language service. The Azure AI Language service provides pre-built capabilities for sentiment analysis, key phrase extraction, and named entity recognition (NER) as part of its text analytics features. These three tasks are directly supported by the service's Analyze API, making it the correct choice for analyzing customer feedback text.

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Azure AI Speech service

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Azure AI Computer Vision

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Translator API

Question 8

Your team is building a mobile app that uses Azure AI Computer Vision to extract text from business cards. The app must handle cards in multiple languages (English, French, German). Which feature of the Computer Vision API should you use?

Accepted Answer

Read API. The Read API is the correct choice because it is the current, optimized version of the Computer Vision OCR service designed specifically for extracting printed and handwritten text from images of documents, including business cards. It supports multiple languages (English, French, German) and provides a higher accuracy and structured output (lines and words with bounding boxes) compared to the legacy OCR API. The Recognize Text operation is deprecated and should not be used for new solutions.

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OCR API

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Recognize Text

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Describe Image

Question 9

You are a Microsoft AI engineer for a multinational retail company. The company uses Microsoft Copilot Studio to build an agent for employee self-service. The agent must handle three main tasks: (1) look up employee information from an on-premises HR database, (2) submit expense reports, and (3) answer questions about company policies stored in SharePoint Online. The HR database can only be accessed via a REST API that requires NTLM authentication. The expense report submission must be routed to a third-party system that uses OAuth 2.0. The policy documents are in multiple languages and the agent must provide answers in the user's language. The agent is published to Microsoft Teams and must support single sign-on (SSO) for authenticated users. The company has strict security requirements: all backend calls must use the user's identity, not a generic service account. The agent must also log all interactions for audit purposes. You need to design the solution architecture. Which combination of Azure services and configurations should you use?

Accepted Answer

Use an on-premises data gateway with a custom connector for the HR API, a custom connector for the expense system with OAuth 2.0, enable generative answers with SharePoint, and configure SSO with Microsoft Entra ID.. Azure Functions require an on-premises data gateway to securely access the on-premises HR API with NTLM authentication, and the Bot Framework SDK is unnecessary as Copilot Studio natively supports multi-language. Option B is incorrect because Power Automate flows cannot directly access the on-premises API without an on-premises data gateway, and they cannot handle OAuth 2.0 for the expense system without custom connectors. Option C is incorrect because while it uses the on-premises data gateway and custom connector, it does not configure SSO with Microsoft Entra ID, which is required for user identity delegation and single sign-on. Option D correctly combines the on-premises data gateway with a custom connector for NTLM, a custom connector for OAuth 2.0, generative answers with SharePoint for multilingual policies, and SSO with Microsoft Entra ID.

Answer

Use Azure Functions to wrap the on-premises API and the expense system, connect via hybrid connections, and use the Bot Framework SDK to handle multi-language.

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Use Power Automate flows for all backend calls, configure SharePoint as a knowledge source, and enable SSO with Microsoft Entra ID.

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Use Azure Logic Apps with on-premises data gateway for the HR API, custom connector for expense system, and enable generative answers with SharePoint.

Question 10

A company is building a knowledge mining solution using Azure AI Search. They need to extract entities from a large set of PDF documents stored in Azure Blob Storage. The solution must use a built-in AI skill to identify people, organizations, and locations. Which TWO actions should be taken? (Choose two.)

Accepted Answer

Enable OCR (Optical Character Recognition) in the indexer configuration.. OCR (Optical Character Recognition) is required to extract text from scanned PDFs before entity recognition can be applied. Option E is correct because the built-in Entity Recognition skill identifies people, organizations, and locations. Option A is incorrect because custom analyzers affect indexing and querying, not entity extraction. Option B is incorrect because the Key Phrase Extraction skill extracts key phrases, not named entities. Option C is incorrect because a custom skill is unnecessary when a built-in skill already meets the requirement.

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Configure the index to use a custom analyzer.

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Add the Key Phrase Extraction skill to the skillset.

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Deploy a custom skill using Azure Functions to extract entities.

Question 11

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.

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Azure AI Computer Vision

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Azure AI Language Service

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Azure AI Search

Question 12

You deploy a Custom Vision object detection model to classify vehicles. The model works well in good lighting but fails in low-light conditions. What is the most appropriate action?

Accepted Answer

Add images with different lighting conditions to the training set. The core issue is a data distribution mismatch: the model was trained primarily on well-lit images and lacks exposure to low-light examples. Adding images with diverse lighting conditions directly addresses this by enriching the training dataset, enabling the model to learn robust features for low-light scenarios. This aligns with the fundamental principle that Custom Vision models are only as good as the training data they receive.

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Increase the probability threshold

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Increase the number of training iterations

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Use a domain-specific model for vehicles

Question 13

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 provides the framework for building, deploying, and managing conversational bots that can interact with users across multiple channels. Conversational Language Understanding (CLU) is a cloud-based API that applies machine learning to extract user intents and entities from natural language utterances, enabling the bot to understand what the user wants and respond appropriately.

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Azure AI Speech-to-Text

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Azure AI Translator

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Azure AI Search

Question 14

Which TWO actions are required to enable a custom chatbot built with Azure OpenAI to answer questions based on a company's internal PDF documents?

Accepted Answer

Ingest the PDFs into an Azure Cognitive Search index. Azure Cognitive Search provides the indexing and retrieval capabilities needed to make PDF content searchable. By ingesting PDFs into an Azure Cognitive Search index, the chatbot can perform vector or keyword searches over the extracted text, enabling it to retrieve relevant passages to answer user questions. This is the standard approach for grounding a custom chatbot on proprietary documents without modifying the underlying model.

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Use Azure AI Document Intelligence to extract text from PDFs before indexing

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Deploy Azure AI Content Safety to filter responses

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Fine-tune the GPT model on the PDF content

Question 15

You are building a solution to extract custom entities from legal contracts using Azure AI Language. You have a small set of labeled documents. Which two features should you use to build and improve the custom NER model? (Choose two.)

Accepted Answer

Use active learning to automatically suggest new labels from unlabeled documents.. Active learning in Azure AI Language automatically identifies unlabeled documents where the model has low confidence and suggests them for labeling, which improves the custom NER model iteratively with minimal manual effort. This feature is specifically designed to reduce the labeling burden while maximizing model accuracy by focusing on the most informative samples.

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Use the prebuilt NER model as a base and extend it with custom entities.

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Configure an orchestration workflow to route documents to the best model.

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Extract key phrases from the documents and use them as features.