AI-102 Practice Test 50 — 15 Questions

Question 1

A company is developing an agent that uses Azure AI Vision to analyze images uploaded by users. The agent must identify objects and read text in images. The team uses the Azure AI Vision API. During testing, the agent fails to read text from images with low contrast. What should the team do to improve optical character recognition (OCR) accuracy for such images?

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

Accepted Answer

Pre-process the image to adjust contrast and brightness before calling the OCR API.. Azure AI Vision's OCR API performs best on images with sufficient contrast and brightness. Pre-processing the image (e.g., using OpenCV or PIL to adjust contrast and brightness) enhances text visibility, directly improving OCR accuracy for low-contrast images without changing the API or training a custom model.

Answer

Use a different OCR API from Azure Cognitive Services.

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Train a custom OCR model using Azure Custom Vision.

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Increase the confidence threshold for text detection.

Question 2

Your company uses a custom question answering knowledge base in Azure AI Language to answer employee questions about HR policies. You need to update the knowledge base with a new set of FAQ documents that contain tables and images. What is the best way to ingest the new content?

Accepted Answer

Use the Azure AI Language REST API to add the new QnA pairs programmatically.. The best way because the Azure AI Language REST API allows you to add a source (e.g., a document) to the custom question answering knowledge base, which can include tables and images. This is more efficient than manually adding QnA pairs. Option D is incorrect because Microsoft Foundry Copilot Studio is a separate tool for building copilots and does not directly update Azure AI Language custom question answering knowledge bases. Option A is incorrect because Azure AI Search is used for indexing and searching, not for direct ingestion into the knowledge base. Option B is incorrect because manually adding QnA pairs is time-consuming and does not automatically extract structured content like tables and images from documents.

Answer

Use Azure AI Search to index the documents and then import the index into the knowledge base.

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Manually add the new QnA pairs using the Language Studio portal.

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Use Microsoft Foundry Copilot Studio to automatically ingest the documents into the knowledge base.

Question 3

Which THREE components are part of the Azure AI Foundry SDK for building multi-agent solutions?

Accepted Answer

AIAgentClient. Options A, B, and C are all correct. AIAgentClient is the core client for creating and managing agents, providing agent lifecycle and communication primitives for multi-agent solutions. AIProjectClient is used to manage the project context, including agent configurations and resources, and is essential for orchestrating agents within a project. InferenceClient handles inference calls to the underlying models, enabling agents to generate responses and perform tasks. Together, these three clients form the foundation of the Azure AI Foundry SDK for building multi-agent solutions.

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Azure AI Search client

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Azure OpenAI client

Question 4

You are an AI engineer at Contoso. Contoso has a Microsoft Copilot for Microsoft 365 deployment. They want to build a custom copilot in Microsoft Copilot Studio that can answer questions about their internal IT support knowledge base stored in a SharePoint Online document library. The knowledge base includes hundreds of PDF and Word documents. Requirements: - The copilot must only answer from the approved knowledge base documents. - Responses must be grounded in the documents and include citations. - The solution must use generative answers with a prebuilt AI model (no custom model training). - Authentication must be via Microsoft Entra ID with single sign-on (SSO). - The copilot should be published to a Microsoft Teams channel. You need to recommend the minimal configuration steps. What should you do?

Accepted Answer

In Copilot Studio, create a new copilot. Add the SharePoint document library as a knowledge source. Configure authentication with Microsoft Entra ID. Enable generative answers. Publish to the Teams channel.. Copilot Studio allows you to create a copilot and add a SharePoint Online document library as a knowledge source, which enables generative answers grounded in those documents. Authentication can be configured with Microsoft Entra ID for SSO, and publishing to the Teams channel is a built-in capability. This meets all requirements without custom code or model training. Option A is wrong because building a custom Azure AI Language model requires training, violating the 'no custom model training' requirement. Option B is wrong because Power Virtual Agents (now part of Copilot Studio) does not natively provide the same seamless integration with SharePoint for generative answers with citations; the recommended approach is using Copilot Studio knowledge sources. Option C is wrong because using Azure AI Search requires creating an index and custom connector, which adds complexity beyond the minimal configuration.

Answer

Create a custom Azure AI Language model using the documents. Then build a bot with Azure Bot Service and connect it to Copilot Studio.

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Use Power Virtual Agents to create a bot. Add a custom entity to map document content. Use Power Automate to retrieve documents.

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Use Azure AI Search to index the documents. Create a custom connector in Copilot Studio to query the search index. Configure authentication and publish to Teams.

Question 5

An e-commerce company wants to build an agent that helps users track orders, initiate returns, and answer FAQs. The agent should be available on the company's website and mobile app. Which Azure service should the team use to deploy the agent?

Accepted Answer

Azure Bot Service. Azure Bot Service is the correct choice because it provides a managed environment for building, deploying, and scaling conversational AI agents that can be integrated with multiple channels, including websites and mobile apps. It supports the Bot Framework SDK, which enables the agent to handle order tracking, returns, and FAQs through natural language understanding (NLU) with LUIS or the newer CLU service.

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Azure Logic Apps

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Azure API Management

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Azure Functions

Question 6

You are developing a content moderation solution that uses Azure Content Moderator to review images uploaded by users. The solution must flag images for potential adult content. Which TWO actions should you take to achieve this goal?

Accepted Answer

Configure a human review loop using the Review tool. Azure Content Moderator's Review tool enables human-in-the-loop review, which is essential for accurately flagging adult content when automated confidence scores are borderline. This allows human moderators to confirm or override the automated classification, ensuring compliance with content policies. Option D is correct because the Evaluate operation on the image endpoint directly analyzes images for adult and racy content using pre-trained models, returning a confidence score that can trigger further action.

Answer

Train a custom image classifier using Content Moderator's training API

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Use the List Management API to add images to a blocklist

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Use the OCR operation to extract text from images

Question 7

A company is developing a conversational AI solution using Microsoft Copilot Studio. They want the copilot to answer questions based on a knowledge base of technical documents. Which data source integration should they use?

Accepted Answer

Azure AI Search. Azure AI Search is the correct data source because it provides a search index that can be queried by Copilot Studio using the 'Azure AI Search' connector. This allows the copilot to perform semantic or keyword-based retrieval over indexed technical documents, enabling accurate question-answering from a knowledge base. Copilot Studio natively supports Azure AI Search as a data source for generative answers, making it the optimal choice for this scenario.

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Azure Blob Storage

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Azure SQL Database

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Microsoft Lists

Question 8

You are a developer at a large financial institution. The compliance team needs to automatically analyze quarterly earnings call transcripts to extract forward-looking statements (e.g., 'we expect revenue to grow') and flag any that are overly optimistic or lack necessary disclaimers. The transcripts are stored as text files in Azure Blob Storage. You need to design a solution using Azure AI Language services that meets the following requirements: 1) Extract all forward-looking statements from each transcript. 2) For each statement, determine if it contains optimistic language (e.g., 'strong growth', 'excellent performance') and if it includes a disclaimer (e.g., 'this is a forward-looking statement'). 3) Output a structured JSON file per transcript with the statements, optimism score, and disclaimer presence. 4) Minimize development effort and avoid custom machine learning model training. Which approach should you take?

Accepted Answer

Use key phrase extraction to identify important phrases, then run sentiment analysis on each sentence to detect optimism.. It uses prebuilt Azure AI Language features (key phrase extraction and sentiment analysis) that require no custom ML model training, meeting the requirement to avoid custom training. Key phrase extraction can identify potential forward-looking phrases, and sentiment analysis on each sentence can provide a per-statement optimism score. Disclaimer presence can be inferred by checking for specific phrases like 'forward-looking statement' using simple text matching or by incorporating the sentiment analysis result for that sentence. This approach minimizes development effort while providing structured output per transcript.

Answer

Use the prebuilt named entity recognition (NER) to identify entities related to financial terms, then apply sentiment analysis to the entire transcript to determine overall optimism.

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Build a custom NER model to extract forward-looking statements, then use a custom text classification model to classify each extracted statement for optimism and disclaimer presence.

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Use custom question answering to create a knowledge base of typical forward-looking statements and query the transcript for matches.

Question 9

You are using Azure AI Document Intelligence to extract data from invoices. The invoices have varying layouts, and you need to build a custom model that can handle multiple document formats. Which type of model should you train?

Accepted Answer

Custom neural model. Custom neural models are designed to learn from diverse layouts without requiring manual labeling of field positions, making them ideal for invoices with varying formats. They use deep learning to understand document structure and extract fields accurately even when layouts differ significantly. This is the recommended approach for handling multiple invoice formats in Azure AI Document Intelligence.

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Custom template model

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Prebuilt invoice model

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Custom composed model

Question 10

Which TWO actions should you take to ensure compliance with data residency requirements when using Azure AI services across multiple regions?

Accepted Answer

Configure the AI service's data residency settings to restrict data to the region. Azure AI services provide data residency settings that allow you to restrict data processing to a specific region, ensuring compliance with data residency requirements. Option E is correct because deploying separate instances of the AI service in each required region gives you full control over where data is stored and processed, which is a common pattern for meeting strict data residency mandates.

Answer

Use a global Azure AI service resource and configure geo-replication

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Enable Azure Front Door to route traffic to the nearest region

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Disable data replication for the AI service

Question 11

Your team is building a custom ChatGPT-like copilot using Azure AI Foundry that answers questions based on internal HR policies stored in SharePoint. The solution must retrieve only the most relevant documents to minimize token usage. Which Azure AI Search feature should you configure?

Accepted Answer

Semantic ranking. Semantic ranking is the correct feature because it uses deep neural networks to re-rank search results based on semantic relevance to the query, ensuring only the most contextually appropriate documents are returned. This directly minimizes token usage by reducing the number of irrelevant documents passed to the copilot, which is critical for cost and performance in a ChatGPT-like system.

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Synonyms

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Scoring profiles

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Filters

Question 12

You are using Azure OpenAI Service to generate marketing copy. The marketing team reports that the generated content sometimes contains factual inaccuracies. You need to improve the factual accuracy of the generated content. What should you do?

Accepted Answer

Include relevant context and facts in the prompt. Providing relevant context in the prompt gives the model factual information to base its response on. Option A is wrong because increasing max_tokens only allows longer responses, not more accurate ones. Option C is wrong because decreasing temperature reduces randomness, but does not directly improve factual accuracy; it may reduce creativity. Option D is wrong because disabling content filtering does not address factual inaccuracies.

Answer

Increase the max_tokens parameter

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Decrease the temperature parameter

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Disable content filtering

Question 13

You are designing an agentic solution in Microsoft Foundry that uses a custom agent to answer questions about internal policies. The agent uses GPT-4o with retrieval augmented generation (RAG) on documents stored in Azure AI Search. Users report that the agent sometimes provides answers that contradict the retrieved documents. Which two actions should you take to improve response fidelity?

Accepted Answer

Set the 'strict grounding' parameter to true and limit the number of source documents to 3.. Setting 'strict grounding' to true forces the model to rely exclusively on the provided source documents, and limiting the number of source documents to 3 reduces the chance of conflicting or irrelevant information being included. Option D is also correct because including the retrieved text directly in the prompt ensures the model has the exact context, and setting temperature to 0 makes the output deterministic, reducing hallucinations. Together, these actions improve response fidelity by enforcing strict grounding and reducing randomness.

Answer

Increase the temperature parameter to 0.9.

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Increase the chunk size in Azure AI Search to 2000 tokens.

Answer

Add a system message instructing the model to only use provided context.

Question 14

A company needs to implement a chatbot that answers customer queries using a knowledge base. Which Azure AI service should be used to build the knowledge base?

Accepted Answer

Azure AI Language (Question Answering). Azure AI Language's Question Answering feature is specifically designed to create a knowledge base from structured or unstructured content (e.g., FAQs, product manuals, support documents). It uses a custom question-answering model that can be trained and published as a REST API endpoint, which a chatbot can then query to retrieve precise answers. This makes it the correct service for building the knowledge base itself.

Answer

Azure Bot Service

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Azure AI Translator

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Azure AI Speech

Question 15

A healthcare organization needs to redact personally identifiable information (PII) from patient records before using them for research. They have large volumes of unstructured text in multiple languages. Which Azure AI service should they use?

Accepted Answer

Azure AI Language (PII detection feature). Azure AI Language's PII detection feature is specifically designed to identify and redact personally identifiable information from unstructured text. It supports multiple languages and can handle large volumes of text, making it the correct choice for redacting PII from patient records before research use.

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Azure AI Content Safety

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Azure AI Document Intelligence (formerly Form Recognizer)

Answer

Azure AI Translator