AI-102 Practice Exam 2 — 20 Questions

Question 1

Which TWO Azure services can be used together with Azure OpenAI to implement a Retrieval-Augmented Generation (RAG) solution?

Accepted Answer

Azure Cosmos DB. Azure Cosmos DB is correct because it can serve as a vector database for storing and querying embeddings generated by Azure OpenAI. When combined with Azure AI Search, which provides hybrid search (vector + keyword) and indexing capabilities, it enables a complete RAG pipeline where relevant documents are retrieved and passed as context to the OpenAI model for grounded generation.

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Azure Blob Storage

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Azure SQL Database

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Azure Functions

Question 2

A company is deploying a solution using Azure AI Vision to analyze images of products on a retail website. They need to ensure that the image analysis is performed within a specific geographic boundary for data residency compliance. What should they configure?

Accepted Answer

Deploy the Azure AI Vision resource in the desired Azure region. Azure AI Vision resources are regional Azure resources, meaning all data processing and storage occur within the Azure region where the resource is deployed. By deploying the resource in the desired geographic region, you ensure that image analysis and any derived data remain within that boundary, satisfying data residency compliance requirements. This is the fundamental mechanism for controlling data location in Azure AI services.

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Create a private endpoint for the Vision resource

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Enable multi-region replication on the Vision resource

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Use the Free tier of Azure AI Vision

Question 3

A company is building an agentic solution using Microsoft Copilot Studio. The agent needs to retrieve customer order status from an external CRM API. The API requires OAuth 2.0 authentication with client credentials. Which connector configuration should the developer use?

Accepted Answer

Use the HTTP connector with OAuth2 client credentials grant type.. The HTTP connector in Microsoft Copilot Studio supports the OAuth 2.0 client credentials grant type, which is exactly what the external CRM API requires. This grant type allows the agent to authenticate as an application (not a user) by sending a client ID and client secret to obtain an access token, making it ideal for server-to-server API calls where no user interaction is needed.

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Use a custom connector with API Key authentication.

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Use a Power Automate flow with a CRM connector that uses service principal.

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Use an AI Builder model to call the API.

Question 4

Which THREE components are required to build a custom chat application using Azure OpenAI Service that can answer questions based on your own private data?

Accepted Answer

The 'Add your data' feature configured in Azure OpenAI Studio.. The 'Add your data' feature in Azure OpenAI Studio provides a no-code interface to connect your private data sources (e.g., Azure Blob Storage, local files) to an Azure OpenAI chat model. It automatically chunks the data, creates an Azure AI Search index, and configures the retrieval-augmented generation (RAG) pipeline, enabling the model to answer questions grounded in your proprietary content without fine-tuning.

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A fine-tuned custom model.

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An Azure OpenAI embeddings model deployment.

Question 5

A healthcare organization uses Azure AI Health Insights to analyze patient data. They must ensure that PHI is not logged by the AI services. Which configuration should you apply?

Accepted Answer

Enable diagnostic settings to stream logs to a Log Analytics workspace with a data masking rule. Azure AI Health Insights processes protected health information (PHI), and enabling diagnostic settings to stream logs to a Log Analytics workspace with a data masking rule allows you to redact sensitive PHI from logs before they are stored. Data masking rules in Log Analytics can be configured to hide patterns like patient IDs or names, ensuring compliance with HIPAA while still retaining operational logs.

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Disable all logging for the Azure AI resource

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Configure the resource to use a system-assigned managed identity

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Use Azure Policy to restrict logging to metadata only

Question 6

Which TWO Azure AI services can be used to extract text from images as part of a knowledge mining pipeline?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is correct because it is specifically designed to extract text, tables, and key-value pairs from scanned documents and images using optical character recognition (OCR) and deep learning models. It is a core service for knowledge mining pipelines that require structured data extraction from unstructured documents.

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Azure AI Language

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Azure AI Video Indexer

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Azure AI Custom Vision

Question 7

A company is building an agent that uses Azure OpenAI Service to answer customer queries by querying a SQL database. The agent must be able to handle complex multi-turn conversations and maintain context. Which approach should the team use to implement the agent?

Accepted Answer

Use a conversational agent framework like AutoGen with a tool that executes SQL queries, and maintain conversation state.. AutoGen is a conversational agent framework designed for multi-turn, stateful interactions. It can maintain conversation context across turns and integrate a tool to execute SQL queries, which directly meets the requirement for complex multi-turn conversations with context retention. The other options either lack state management or rely on stateless prompt engineering, which is insufficient for maintaining context in a multi-turn agent.

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Use a single prompt that includes the entire conversation history and the latest user question, then generate SQL.

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Use a chain-of-thought prompt to generate SQL queries directly from user input, without maintaining conversation history.

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Use embeddings-based retrieval to find relevant past interactions and include them in the prompt.

Question 8

Your organization wants to implement a document processing pipeline that extracts text from scanned PDFs and identifies named entities. Which two Azure AI services should you use?

Accepted Answer

Azure AI Language. Azure AI Document Intelligence (formerly Form Recognizer) is used to extract text from scanned PDFs via OCR, while Azure AI Language provides pre-built named entity recognition (NER) to identify entities like people, organizations, and locations. Together, they form a complete pipeline: Document Intelligence handles the image-to-text conversion, and Language processes the extracted text for entity extraction.

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Azure AI Custom Vision

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Azure AI Translator

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Azure AI Speech

Question 9

You are planning to deploy an Azure AI Content Safety solution. What is the primary requirement for using the service?

Accepted Answer

An active Azure subscription. An active Azure subscription is the primary requirement because Azure AI Content Safety is a cloud-based service that requires a valid subscription for resource provisioning, API access, and billing. Without an Azure subscription, you cannot create the Content Safety resource or authenticate API calls, regardless of other configurations.

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Data must be stored in the same region as the service

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All users must have Microsoft Entra ID P2 licenses

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The application must be written in C#

Question 10

You need to provide a team of developers with access to manage Azure AI resources in a specific resource group. The developers should be able to create, read, update, and delete AI resources, but not manage access control (IAM). Which built-in role should you assign?

Accepted Answer

Contributor. The Contributor role grants full access to manage all resources within a resource group, including creating, reading, updating, and deleting Azure AI resources, but explicitly denies the ability to manage access control (IAM). This matches the requirement exactly, as developers need full resource management without IAM permissions.

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Cognitive Services Contributor

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Owner

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Reader

Question 11

Your organization has a large repository of technical manuals in PDF format. You need to build a chatbot that can answer questions about the content of these manuals. Which combination of Azure services should you use?

Accepted Answer

Azure AI Search and Azure OpenAI. Azure AI Search provides the indexing and retrieval capabilities needed to search through the PDF content, while Azure OpenAI (specifically GPT models) can generate natural language answers based on the retrieved passages. This combination enables a RAG (Retrieval-Augmented Generation) pattern where the search engine finds relevant text chunks from the manuals and the language model formulates a coherent answer.

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Azure AI Speech and Azure OpenAI

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Azure AI Language and Azure AI Document Intelligence

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Azure AI Document Intelligence and Azure Bot Service

Question 12

You are designing a chatbot using Azure AI Language. The chatbot must understand user intents and also extract entities like dates and locations. Which feature combination should you use?

Accepted Answer

Conversational Language Understanding (CLU) with entities. Conversational Language Understanding (CLU) is the correct Azure AI Language feature for building a chatbot that understands user intents and extracts entities like dates and locations. CLU is specifically designed for natural language understanding (NLU) tasks, providing prebuilt and custom entity extraction alongside intent recognition, which directly matches the requirement.

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Sentiment analysis and entity linking

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Custom text classification and key phrase extraction

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Orchestration Workflow and custom text classification

Question 13

A healthcare organization uses Azure AI Language to extract medical entities from clinical notes. The solution must comply with HIPAA. What should you configure?

Accepted Answer

Use Azure AI Language with HIPAA-enabled service and enable encryption. Azure AI Language can be configured with a HIPAA-eligible service tier, and enabling encryption at rest and in transit ensures compliance with HIPAA's security and privacy rules. This configuration meets the healthcare organization's requirement to protect protected health information (PHI) when extracting medical entities from clinical notes.

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Configure data residency to a specific region

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Enable content filtering

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Isolate the service in a virtual network

Question 14

You are working for a healthcare organization that uses Azure AI Document Intelligence to process patient intake forms. The forms are scanned and uploaded as multi-page PDFs. The extraction accuracy for the 'diagnosis code' field is poor. You have a labeled dataset of 200 forms. You need to improve the extraction accuracy without writing custom code. The solution must also handle forms with varying layouts. What should you do?

Accepted Answer

Create a custom neural model and train it with the labeled dataset.. A custom neural model in Azure AI Document Intelligence is specifically designed to handle complex, variable-layout documents like patient intake forms. It uses deep learning to learn from labeled datasets (200 forms) and improves extraction accuracy for fields like 'diagnosis code' without requiring custom code. Neural models are superior to template-based models for varying layouts, as they generalize from patterns rather than relying on fixed spatial positions.

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Use the 'Form processing' custom extraction model.

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Use the US Tax W-2 predefined model as a base and customize it.

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Use the General Document model to extract all text and then parse.

Question 15

You are designing a solution that uses Azure AI Document Intelligence to extract data from invoices. The solution must handle high throughput and process documents in batch. Which TWO configuration options should you use?

Accepted Answer

Provision a standard (S0) tier Document Intelligence resource. The standard (S0) tier is required for production-grade throughput because it offers higher transactions per second (TPS) and better scalability compared to the free (F0) tier. For batch processing of invoices, the async document analysis API is the correct choice because it supports submitting multiple documents in a single call and returns results via a polling mechanism, which is designed for high-volume scenarios.

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Use the synchronous API for each invoice

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Train a custom model for invoice extraction

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Store output directly in Azure Blob Storage without API calls

Question 16

A team is building a chatbot using Azure Bot Service and Language Understanding (LUIS). The chatbot must handle multiple languages. What should you configure?

Accepted Answer

Create separate LUIS applications for each language. LUIS does not natively support multilingual models within a single app. Each LUIS application is designed for a single language, so to handle multiple languages, you must create separate LUIS apps—one per language—and route user utterances to the appropriate app based on the detected language. This ensures accurate intent and entity recognition tailored to each language's linguistic patterns.

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Configure a single LUIS app with multilingual utterances

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Use Azure AI Search to index translated content

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Use Azure Translator to translate utterances before calling LUIS

Question 17

You plan to use Azure AI Content Safety to detect hate speech in user-generated content. Which type of content safety is most appropriate for this scenario?

Accepted Answer

Text moderation. Text moderation is the correct choice because Azure AI Content Safety's text moderation API is specifically designed to detect and filter hate speech, along with other harmful content categories like violence and self-harm, in user-generated text. It uses machine learning classifiers trained on a vast corpus to assign severity scores across predefined categories, making it the direct and most appropriate tool for this scenario.

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Custom categories

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Image moderation

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Prompt Shields

Question 18

You manage an Azure AI Search service that indexes legal documents. The search latency is high, and you need to improve query performance without reducing index size. Which action should you take?

Accepted Answer

Increase the number of replicas. Increasing the number of replicas distributes query load across multiple copies of the index, which directly improves query throughput and reduces latency. Replicas are designed for scaling query operations without changing the index size or storage capacity.

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Upgrade to a higher pricing tier

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Increase the number of partitions

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Reduce the number of searchable fields

Question 19

Refer to the exhibit. You are deploying an AI project in Microsoft Foundry using an ARM template. The deployment fails with an error indicating that the hub resource is not in the same region. What is the most likely cause?

Accepted Answer

The hub and project are in different Azure regions. In Azure AI Foundry, the hub and project resources must reside in the same Azure region because the project is a child resource of the hub and relies on the hub's regional endpoint for metadata storage and compute orchestration. The ARM template deployment fails with a region mismatch error when the location property of the project specifies a different region than the hub's location, as Azure enforces regional affinity for child resources to ensure low-latency communication and data residency compliance.

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The resource group for the hub is different from the project's resource group

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The identity type is SystemAssigned, but the hub requires UserAssigned

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The location property is misspelled in the template

Question 20

A healthcare company is using Azure AI Document Intelligence to extract patient data from forms. They need to ensure that all extracted data is encrypted at rest using a customer-managed key (CMK) and that the service endpoint is restricted to a specific virtual network. Which combination of steps should they take?

Accepted Answer

Create a private endpoint and associate a customer-managed key in the resource encryption settings. It combines a private endpoint (which restricts the service endpoint to a specific virtual network by providing a private IP address within that VNet, eliminating public internet exposure) with a customer-managed key (CMK) in the resource encryption settings, which ensures data at rest is encrypted using a key stored in Azure Key Vault that the customer controls. This directly meets both requirements: network isolation via private endpoint and CMK-based encryption at rest.

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Use a service endpoint and configure a managed identity

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Disable public network access and enable CMK via Azure Key Vault

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Configure IP firewall rules and enable CMK via Azure Key Vault