AI-102 Practice Test 43 — 15 Questions

Question 1

You are using Azure AI Document Intelligence to process a large batch of PDF forms. The forms have varying layouts and handwriting. You need to extract text and key-value pairs. Which custom model type should you train?

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

Accepted Answer

Custom neural model. Custom neural model. Azure AI Document Intelligence offers custom template models for forms with fixed layouts and custom neural models for forms with varying layouts and handwriting. Neural models use deep learning to handle variability in structure and handwriting, making them ideal for this scenario. Option A (Custom template model) assumes a fixed layout and fails with varying layouts. Option B (Prebuilt-layout model) is a prebuilt model that extracts text, tables, and selection marks but not customized key-value pairs for your forms. Option D (Custom composed model) is a combination of multiple models, but the primary choice for varying layouts is the neural model, not composed. Therefore, C is the best choice.

Answer

Custom template model

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Prebuilt-layout model

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Custom composed model

Question 2

Which TWO features of Azure AI Search allow you to improve the relevance of search results for users?

Accepted Answer

Semantic search. Semantic search (B) is correct because it uses Microsoft's language understanding models to rerank results with semantic captions and answers, boosting relevance beyond keyword matching. Scoring profiles (D) are correct because they let you define custom ranking functions (e.g., boosting by freshness, magnitude, or tags) that directly influence the relevance score of returned documents. Synonym maps (A) expand queries with equivalent terms but only broaden matching, not rank relevance. Suggesters (C) enable type-ahead autocomplete, which improves query input rather than result relevance. Filterable fields (E) restrict the result set with OData filters but do not affect relevance ranking.

Answer

Synonym maps

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Suggesters

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Filterable fields

Question 3

You are deploying a custom text classification model using Azure AI Language. The model must be retrained monthly with new labeled data. You need to automate the retraining process with minimal manual intervention. Which approach should you use?

Accepted Answer

Use the Azure AI Language REST API to trigger training and deployment on a schedule using Azure Logic Apps. Azure Logic Apps can schedule HTTP requests to the Azure AI Language REST API to trigger training and deployment of a custom text classification model. This approach automates the monthly retraining process without manual intervention, using the API's capabilities for model creation, training, and deployment.

Answer

Create an Azure DevOps pipeline that manually retrains the model every month

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Retrain the model manually using Language Studio each month

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Use Azure Machine Learning to host the custom model and automate retraining

Question 4

Refer to the exhibit. You have created an assistant with the above configuration. When you send a message 'What is the weather in Seattle?', the assistant responds without calling the function. What is the most likely cause?

Accepted Answer

The function is missing the 'description' property. The function definition lacks a 'description' property. In the Assistants API, the 'description' field is critical for the model to understand when and why to invoke a function. Without it, the model may not recognize that the function is relevant to the user's query about weather, causing it to respond directly instead of calling the function.

Answer

The 'instructions' are not being followed

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The 'strict' parameter is set to true

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The 'tool_resources' code_interpreter has empty file_ids

Question 5

A company wants to build a customer support agent using Microsoft Copilot Studio. The agent needs to understand natural language and handle complex queries beyond simple keyword matching. The agent should be able to escalate to a human agent when it cannot resolve the issue. Which feature should the agent use to understand natural language?

Accepted Answer

Enable generative answers and configure a knowledge source.. Generative answers in Microsoft Copilot Studio use large language models (LLMs) to interpret natural language queries and generate responses based on configured knowledge sources (e.g., SharePoint, websites, or custom data). This enables the agent to handle complex, conversational queries beyond simple keyword matching, and it can escalate to a human agent when confidence is low or the issue cannot be resolved.

Answer

Create a Power Automate flow to process queries.

Answer

Create topics with trigger phrases.

Answer

Use entities to extract key information.

Question 6

You are testing an Azure OpenAI Service chat completion with function calling. The assistant returned null content and a tool call. What does this indicate?

Accepted Answer

The model is using a tool to answer the question, so content is null. In Azure OpenAI Service function calling, when the model determines that it needs to invoke a tool (e.g., an external API or database query) to fulfill the user's request, it returns a `tool_calls` object with `content` set to `null`. This is the expected behavior—the model is signaling that it is delegating the response to the tool, not that it has failed or is blocked.

Answer

The model is unable to answer the question and returned an error

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The system message is preventing the model from responding

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The function call syntax is invalid and the model skipped it

Question 7

You need to build a solution that detects whether a person is wearing a hard hat in a construction site image. Which Azure AI service should you use?

Accepted Answer

Azure AI Custom Vision. Azure AI Custom Vision is the correct service because it allows you to train a custom object detection model to identify specific objects—such as a hard hat—in images. Unlike pre-built services, Custom Vision lets you upload labeled images of workers with and without hard hats, train a model, and then use it to detect hard hat presence in construction site photos. This tailored approach is necessary because hard hat detection is a specialized use case not covered by generic vision APIs.

Answer

Azure AI Video Indexer

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Azure AI Face

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Azure AI Document Intelligence

Question 8

You are using Azure AI Content Safety to moderate user-generated content in a social media app. The solution must detect and block hate speech and self-harm content in real time. Which Content Safety feature should you use?

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 block hate speech and self-harm content in real time. It analyzes text for severity levels across multiple categories, including hate and self-harm, and returns a severity score to trigger blocking actions. This directly meets the requirement for real-time detection of these specific content types in user-generated text.

Answer

Custom category management

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Severity analysis

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Image moderation

Question 9

A company uses Azure AI Language Service for custom text classification. The model is trained to classify support tickets into categories. After deployment, the model performs well on the test set but poorly on new incoming tickets. Which action should be taken to improve generalization?

Accepted Answer

Add more labeled data from actual production tickets. Adding more labeled data from actual production tickets helps the model learn the true distribution of real-world inputs, reducing overfitting to the test set. The model's poor performance on new tickets indicates it memorized patterns specific to the training data rather than generalizing. Incorporating production data directly addresses the distribution shift between the test set and live traffic.

Answer

Switch to a prebuilt text classification model

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Increase the number of training epochs

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Reduce the confidence threshold for classification

Question 10

A logistics company uses Azure AI Vision to analyze images of packages on conveyor belts. They need to detect damaged packages and read tracking numbers. The solution must process high throughput (1000 images per minute) with low latency (<500ms per image). The images are captured by fixed cameras. Which approach should you recommend?

Accepted Answer

Use Azure AI Vision OCR Read API for tracking numbers and a separate Custom Vision model for damage detection. It combines Azure AI Vision OCR Read API (for text extraction) with a separate Custom Vision model (for damage detection). This leverages the specialized strengths of each service: the OCR Read API is optimized for text reading with high accuracy, while Custom Vision excels at object detection. The latency requirement (<500ms per image) can be met through parallel processing or edge deployment, and throughput can be achieved by scaling API calls. Option B is incorrect because Custom Vision does not include built-in OCR capability; it cannot read tracking numbers. Options A and C are less suitable: Document Intelligence is designed for structured documents, not real-time package analysis, and Video Indexer is for video streams, not still images.

Answer

Use Azure AI Document Intelligence to process package labels

Answer

Train a single Custom Vision model that detects damage and reads tracking numbers using OCR

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Use Azure AI Video Indexer to analyze the video stream from cameras

Question 11

A research organization uses Azure AI Language to process large volumes of scientific papers. They need to extract specific entities such as gene names, protein names, and chemical compounds. The entity types are highly specialized and not covered by prebuilt models. The organization has a labeled dataset of 10,000 documents. You need to recommend the most efficient approach to build the entity extraction solution. What should you do?

Accepted Answer

Train a Custom Named Entity Recognition (NER) model using the labeled dataset in Azure AI Language.. Custom Named Entity Recognition (NER) in Azure AI Language allows you to train a model on your own labeled dataset (10,000 documents) to extract highly specialized entity types like gene names, protein names, and chemical compounds that are not covered by prebuilt models. This approach is the most efficient as it leverages the labeled data directly, avoiding the need for complex post-processing or hybrid solutions.

Answer

Use the prebuilt NER model and map the recognized entities to the required types.

Answer

Use Azure Logic Apps to call the Text Analytics API and post-process the results.

Answer

Train a custom NER model for genes and use prebuilt NER for chemicals.

Question 12

A company plans to deploy a Copilot Studio agent to Microsoft Teams. The agent should be available to all employees in the company. The security team requires that only authenticated users from the company's Microsoft Entra ID tenant can access the agent. Which channel configuration should be used?

Accepted Answer

Publish the agent to the Teams channel and configure authentication to require Microsoft Entra ID with the company's tenant ID.. Publishing the Copilot Studio agent to the Teams channel and configuring authentication to require Microsoft Entra ID with the company's tenant ID ensures that only authenticated users from that specific tenant can access the agent. This meets the security requirement by restricting access to the company's Entra ID tenant, while the Teams channel provides native integration for all employees.

Answer

Publish the agent to the Direct Line channel and embed it in a Teams tab.

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Publish the agent to the Web channel and share the link in Teams.

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Publish the agent to the Teams channel and turn off authentication.

Question 13

Which TWO options are valid ways to index content from Azure SQL Database into Azure AI Search? (Select TWO.)

Accepted Answer

Use the Push API to send data directly to the search index.. Option A is correct because the Push API (the REST/SDK endpoint that accepts JSON documents directly into an index) lets an application read rows from Azure SQL Database and send them straight to Azure AI Search, bypassing any intermediate storage. Option E is correct because Azure AI Search provides a built-in Azure SQL indexer (created via the portal, REST, or the Azure.Search.Documents SDK) that connects to Azure SQL Database using a change-tracking column (or high-water mark) to pull and index rows on a schedule. Option B is not a direct indexing method for Azure SQL Database; it inserts an unnecessary Data Factory copy to Blob Storage and then relies on a Blob indexer, which is a different data source. Option C is wrong because Azure AI Document Intelligence extracts text from documents (PDFs, images, forms), not from relational Azure SQL tables. Option D is wrong because Event Hubs is a streaming ingestion service and Azure AI Search has no Event Hubs indexer; streaming into the index would still require the Push API.

Answer

Use Azure Data Factory to copy data to Blob Storage, then index from Blob.

Answer

Use Azure AI Document Intelligence to extract data and push to index.

Answer

Use Azure Event Hubs to stream data into the search index.

Question 14

Your team is building a custom question-answering solution using Azure AI Language. The solution must be able to answer questions based on a set of PDF documents. You need to import the documents and create a knowledge base. What should you do first?

Accepted Answer

Use the Azure AI Language service with the custom question answering feature and import the documents. The custom question answering feature of Azure AI Language is specifically designed to ingest documents (including PDFs) and build a knowledge base that can be used for question-answering. This feature provides a built-in pipeline to extract question-answer pairs from documents, create a knowledge base, and deploy it as a service without needing additional search indexing or bot orchestration.

Answer

Use Azure AI Foundry to create a project and upload the documents

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Create an index in Azure AI Search and upload the documents

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Deploy an Azure AI Bot Service and connect it to the documents

Question 15

Your organization uses Azure AI Language for custom text classification. You have deployed a model to a dedicated endpoint. After updating the training data, you retrain and redeploy the model. Users report that the endpoint still returns predictions from the old model. What is the most likely cause?

Accepted Answer

The new model is not yet deployed; you must deploy it to the endpoint. In Azure AI Language, retraining a custom text classification model does not automatically update the deployed endpoint. After training, you must explicitly deploy the new model to the endpoint using the 'Deploy model' action. Until that step is completed, the endpoint continues to serve predictions from the previously deployed model.

Answer

The training data changes are not saved

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The project needs to be rebuilt from scratch

Answer

The endpoint has a caching issue