AI-102 Practice Test 39 — 15 Questions

Question 1

You are deploying a conversational AI solution using Microsoft Copilot Studio. The solution must comply with organizational data loss prevention (DLP) policies by preventing sensitive data from being sent to the underlying Azure OpenAI model. What should you configure?

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

Accepted Answer

Define DLP policies in Microsoft 365 compliance center and apply to Copilot Studio. Microsoft Copilot Studio integrates with Microsoft 365 DLP policies to prevent sensitive data from being sent to the underlying Azure OpenAI model. By defining DLP policies in the Microsoft 365 compliance center and applying them to Copilot Studio, you can enforce data loss prevention rules that block or restrict the transmission of sensitive information (e.g., credit card numbers, social security numbers) to the generative AI backend. This ensures compliance with organizational security requirements without modifying the AI model itself.

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Configure content filters in Azure OpenAI Studio

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Enable Azure AI Content Safety in the bot's generative AI configuration

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Set the temperature parameter to 0 to reduce variability

Question 2

You are a developer at a global e-commerce company. You are building a multilingual chatbot using Azure AI Language that supports English, French, German, and Spanish. The chatbot must answer frequently asked questions about order status, returns, and shipping. You plan to use Custom Question Answering with a single project containing questions and answers in all four languages. However, during testing, you notice that queries in French and German often return incorrect answers or no answer, while English and Spanish work well. You need to ensure accurate answers across all four languages. What should you do?

Accepted Answer

Create a separate Custom Question Answering project for each language and route user queries to the appropriate project based on language detection.. Custom Question Answering (CQA) projects are language-specific; a single project cannot reliably handle multiple languages due to differences in tokenization, stemming, and stop-word handling. By creating a separate project per language and routing queries based on language detection (e.g., using Azure AI Language's language detection API), you ensure that each project's model is optimized for its respective language, improving answer accuracy for French and German.

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Use Azure Cognitive Search with semantic ranking to index the QnA pairs.

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Add synonyms in the project for French and German terms to improve matching.

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Deploy the same project to multiple regions and use traffic manager.

Question 3

You need to generate a summary of a long article using Azure OpenAI. The article is 10,000 tokens long. What should you do to fit the article within the model's context window?

Accepted Answer

Split the article into smaller sections and summarize each section separately.. The article exceeds the model's context window (typically 4096 or 8192 tokens for GPT-3.5/4). Splitting the article into smaller sections and summarizing each separately allows you to process the entire content within the token limits, then combine the summaries for a final coherent output. This is a standard chunking strategy for long documents when using Azure OpenAI.

Answer

Increase the temperature parameter.

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Use a model with a smaller context window.

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Set max_tokens to a lower value.

Question 4

You are developing an agentic solution that uses multiple agents to handle customer inquiries. You need to ensure that agents can hand off to each other with full context. Which THREE features should you use?

Accepted Answer

Shared Memory. Shared Memory (B) is correct because it allows multiple agents to access and update a common data store, ensuring that when one agent hands off to another, the full conversation history and context are preserved. This is essential for maintaining continuity in multi-agent systems, as each agent can read the shared state to understand what has been discussed and decided.

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KQL

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Function calling

Question 5

You are building a chatbot that uses Azure AI Language to extract intents and entities from user utterances. The bot must recognize custom entities like product names that are not in the default model. Which feature should you use?

Accepted Answer

Custom named entity recognition (NER) component.. Custom named entity recognition (NER) is the correct feature because it allows you to train a model to identify domain-specific entities, such as product names, that are not included in Azure AI Language's prebuilt entity catalog. Unlike prebuilt components, custom NER uses a labeled dataset to learn the exact spans of text that represent your custom entities, enabling the chatbot to extract them accurately from user utterances.

Answer

Prebuilt entity recognition component.

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Key phrase extraction.

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List entity in a conversational language understanding (CLU) project.

Question 6

Your team uses Azure AI Foundry to deploy a custom chat model. The model must meet compliance by explaining its reasoning and citing sources. Which feature should you enable?

Accepted Answer

Groundedness detection with citation. Groundedness detection with citation is the correct feature because it directly addresses the compliance requirement for the model to explain its reasoning and cite sources. This feature, available in Azure AI Foundry, evaluates the model's responses against the provided grounding documents and automatically generates citations, ensuring that the output is factually supported and traceable.

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Fine-tuning with domain-specific data

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Content filtering

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Prompt engineering

Question 7

A legal firm uses Azure AI Language's custom NER to extract party names, dates, and clauses from contracts. The model performs well on English contracts but poorly on French contracts. The firm wants to improve performance without retraining from scratch. What is the most efficient approach?

Accepted Answer

Use the multilingual option in Azure AI Language custom NER to extend the existing project to include French.. Azure AI Language's custom NER supports a multilingual option that allows you to extend an existing project to include additional languages without retraining from scratch. By enabling this option and adding French labeled data, the model learns to recognize entities in French while retaining its English performance, making it the most efficient approach.

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Create a separate custom NER project for French and train from scratch using French contracts.

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Retrain the English model with a mix of English and French contracts.

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Use Azure AI Translator to translate French contracts to English, then use the English model.

Question 8

Which THREE components are part of the Azure AI Bot Service?

Accepted Answer

Azure Bot Service (hosting). Azure Bot Service is the hosting component that provides a managed environment for deploying and running bots, integrating with channels like Teams, Slack, and Web Chat. It handles authentication, state management, and scaling, making it a core part of the Azure AI Bot Service.

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Azure AI Search

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Azure AI Language (CLU)

Question 9

You are building an agentic solution that needs to perform actions on behalf of the user, such as sending emails and updating calendars. Which authentication approach should you use for the agent to access Microsoft Graph API with delegated permissions?

Accepted Answer

OAuth 2.0 authorization code flow with PKCE. The authorization code flow with PKCE is the correct approach because the agent needs to act on behalf of a signed-in user, requiring delegated permissions. This flow securely exchanges an authorization code for an access token, and PKCE adds a cryptographic challenge to prevent interception attacks, making it ideal for public clients like agent applications.

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OAuth 2.0 client credentials flow

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OAuth 2.0 implicit flow

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OAuth 2.0 device code flow

Question 10

Your application needs to determine whether two photos of the same person are of the same individual, even if they are from different angles. Which Azure AI service should you use?

Accepted Answer

Azure AI Face. Azure AI Face provides face verification APIs that compare two faces and return a confidence score indicating whether they belong to the same person. It uses deep learning models trained to handle variations in pose, lighting, and expression, making it ideal for matching photos of the same individual from different angles.

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Azure AI Video Indexer

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Azure AI Custom Vision

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Azure AI Vision OCR

Question 11

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?

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 12

A developer wants to deploy a custom generative AI model using Azure Machine Learning. Which compute target should they choose for low-latency real-time inference?

Accepted Answer

Azure Kubernetes Service (AKS). Azure Kubernetes Service (AKS) is the correct compute target for low-latency real-time inference because it supports horizontal pod autoscaling, GPU acceleration, and can be configured with a low-latency ingress controller (e.g., NGINX or Azure Application Gateway) to route inference requests directly to model containers. AKS also integrates with Azure Machine Learning's real-time inference endpoint, which uses a gRPC or HTTP-based scoring protocol to achieve sub-100ms response times.

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Local deployment

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Azure Batch

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Azure Functions

Question 13

A company is developing an agent that uses Azure AI Language to extract entities and intents from user queries. The agent receives a query: 'Book a flight to Paris on Friday.' The agent should extract the intent as 'BookFlight' and entities as 'Paris' (destination) and 'Friday' (date). The team uses a custom entity extraction model. After testing, the model extracts 'Paris' as location but fails to extract 'Friday' as date. What should the team do to fix this?

Accepted Answer

Train the entity extraction model with more examples of dates.. The custom entity extraction model in Azure AI Language requires sufficient labeled examples for each custom entity type to learn patterns. Since the model extracts 'Paris' (location) but fails on 'Friday' (date), the issue is specifically with the date entity's training data, not the location. Adding more diverse examples of date expressions (e.g., 'next Monday', 'tomorrow', 'March 5th') will improve the model's ability to recognize 'Friday' as a date entity.

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Increase the training data for location entities.

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Add a prebuilt entity component for date.

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Use a different intent classification model.

Question 14

You are building a chatbot using Azure AI Bot Service and Language Service. The bot must recognize user intent for 'check order status'. How should you configure the Language Service?

Accepted Answer

Create a custom intent classification project. To recognize user intent for 'check order status', you need a custom intent classification project in Azure Language Service. This project type uses a trained model to map utterances to specific intents, such as 'CheckOrderStatus', which is exactly what the chatbot requires. Prebuilt models or QnA Maker do not provide custom intent recognition.

Answer

Deploy a QnA Maker knowledge base

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Configure a sentiment analysis endpoint

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Use the prebuilt entity extraction model

Question 15

You are deploying an Azure AI Language service solution for a multilingual customer support chatbot. The solution must support real-time translation between English, Spanish, and French. Which Azure resource should you provision?

Accepted Answer

Azure AI Translator. Azure AI Translator is the correct resource because it provides real-time text translation across multiple languages, including English, Spanish, and French, via a dedicated REST API. The scenario specifically requires translation between languages, not speech recognition or generative AI, making Azure AI Translator the precise service for this task.

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Azure AI Speech

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Azure OpenAI Service

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Azure AI Language