AI-102 Practice Exam 64 — 20 Questions

Question 1

You are planning a solution that uses Azure AI Language to analyze customer feedback from social media posts. The solution must: - Detect sentiment (positive, negative, neutral) for each post. - Extract key phrases. - Support English and Spanish languages. - Run asynchronously for a batch of 10,000 posts. - Use the least expensive option that meets requirements. What should you do?

Accepted Answer

Use the Azure AI Language service with the built-in sentiment analysis and key phrase extraction capabilities. Process posts in batches using the async API.. Option A is correct because Azure AI Language's built-in sentiment analysis and key phrase extraction natively support both English and Spanish, and the async batch API is designed for high-volume processing (e.g., 10,000 posts) at a lower cost than per-document calls. This approach meets all requirements without custom models or translation overhead.

Answer

Build a custom text classification model in Azure AI Language to detect sentiment and extract key phrases.

Answer

Use the Azure AI Language service with the single-document API for each post.

Answer

Use Azure AI Translator to translate all posts to English, then use Azure AI Language for analysis.

Question 2

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.. Option B is correct. Copilot Studio allows connecting to SharePoint Online as a knowledge source for generative answers. Setting up authentication with Microsoft Entra ID provides SSO. Publishing to Teams is a built-in channel. This meets all requirements without custom development. Option A is wrong because building a custom Azure AI Language model is unnecessary and violates the 'no custom model training' requirement. Option C is wrong because Azure AI Search requires creating an index and custom skills, which is more complex and not minimal. Option D is wrong because Power Virtual Agents (now part of Copilot Studio) does not natively support generative answers with SharePoint grounding in the same simple way; the recommended approach is Copilot Studio with knowledge sources.

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.

Answer

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

Answer

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 3

A university is developing an app for students to take photos of handwritten notes and convert them to digital text. The app must support multiple languages including English and Spanish. The solution should use a pre-built AI service. Which Azure service should you use?

Accepted Answer

Azure AI Vision Read API (OCR). Azure AI Vision OCR API supports multiple languages and handwritten text. Custom Vision does not support OCR. Azure AI Language requires text input. Azure AI Document Intelligence is for structured documents.

Answer

Azure AI Document Intelligence with a custom model

Answer

Azure AI Language with custom entity recognition

Answer

Custom Vision with a custom handwriting recognition model

Question 4

You are implementing a chatbot using Microsoft Copilot Studio that helps employees find company policies. The chatbot must: - Use generative answers based on a SharePoint Online site. - Only respond with information from approved policy documents. - Include citations in responses. - Be accessible from Microsoft Teams. - Require no custom code. What should you do?

Accepted Answer

In Copilot Studio, create a new copilot. Add the SharePoint site as a knowledge source. Enable generative answers with citations. Publish to Teams.. Option B is correct because Microsoft Copilot Studio natively supports adding a SharePoint Online site as a knowledge source, enabling generative answers that retrieve and cite only approved policy documents. It requires no custom code, automatically includes citations in responses, and can be published directly to Microsoft Teams, fulfilling all stated requirements.

Answer

Use Power Automate to retrieve documents and feed them to Azure OpenAI. Build a custom connector for Teams.

Answer

Build a bot using Azure Bot Service and QnA Maker. Train it with the policy documents. Deploy to Teams.

Answer

Create a custom GPT in Azure OpenAI Studio. Upload the policy documents. Deploy via Azure API Management and expose to Teams.

Question 5

You are designing a solution that uses Azure OpenAI Service to generate product descriptions based on product attributes (name, category, features). The solution must: - Use a GPT-4 model deployed in the West US region. - Implement content filtering to block inappropriate content. - Handle up to 100 requests per second. - Minimize latency. - Use managed identity for authentication. What should you include in the design?

Accepted Answer

Deploy a single GPT-4 model with sufficient capacity (e.g., 100K TPM). Enable content filtering. Use a system-assigned managed identity for authentication.. Option B is correct because deploying a single GPT-4 model with sufficient capacity (e.g., 100K TPM) ensures the solution can handle up to 100 requests per second while minimizing latency by avoiding cross-region calls. Enabling content filtering directly on the Azure OpenAI deployment blocks inappropriate content without additional services, and using a system-assigned managed identity provides secure, keyless authentication that aligns with Azure best practices.

Answer

Deploy multiple GPT-4 models across different regions to reduce latency.

Answer

Use API key authentication stored in Azure Key Vault. Deploy two GPT-4 models to load balance requests.

Answer

Use Azure AI Content Safety in addition to Azure OpenAI to filter content.

Question 6

You are a generative AI engineer at a financial services company. The company uses Azure OpenAI Service to generate investment summaries. You have deployed a GPT-4 model with a content filter set to 'Low' for hate speech. The model frequently generates summaries that include biased language against certain demographics. You need to reduce biased outputs while maintaining the ability to generate detailed financial analysis. You cannot afford to retrain the model. You have the following options: A) Change the content filter severity to 'High' for all categories, B) Add a system message instructing the model to avoid bias and provide examples of unbiased summaries in the prompt, C) Use the Azure AI Language service to detect bias in the output and regenerate if bias is found, D) Deploy a different model like GPT-3.5 which has less bias. Which course of action should you take?

Accepted Answer

Add a system message instructing the model to avoid bias and provide examples of unbiased summaries in the prompt.. Option B is correct because adding a system message and examples in the prompt (few-shot learning) can effectively reduce biased outputs without retraining. Option A is wrong because increasing content filter severity may block legitimate financial analysis content. Option C is wrong because detecting bias after generation and regenerating is inefficient and may still produce biased output. Option D is wrong because GPT-3.5 may also exhibit bias and may not provide the same quality of financial analysis.

Answer

Use the Azure AI Language service to detect bias in the output and regenerate if bias is found.

Answer

Change the content filter severity to 'High' for all categories.

Answer

Deploy a different model like GPT-3.5 which has less bias.

Question 7

You are a senior AI engineer at a financial services company. You are building a generative AI solution to assist financial advisors with client portfolio recommendations. The solution must use Azure OpenAI Service. The following requirements must be met:

1. Responses must be based on the latest market data and client profiles stored in Azure SQL Database.
2. The solution must not generate investment advice that is not backed by the data.
3. The solution must be cost-effective and minimize API calls.
4. The system must provide citations for the data used in the response.

You design a RAG pattern with Azure AI Search indexing the portfolio data. You also implement a system message instructing the model to only use provided context. However, the model occasionally generates advice that contradicts the data or invents new facts. You need to modify the solution to ensure responses are strictly grounded in the retrieved data. What should you do?

Accepted Answer

Set temperature to 0 in the Azure OpenAI completion request.. Option B is correct because setting temperature to 0 makes the model deterministic and less likely to invent. Option A is wrong because lowering top_k still allows creativity. Option C is wrong because adjusting chunk size affects retrieval but not the model's adherence. Option D is wrong because integrating with the data doesn't prevent hallucination if temperature is high.

Answer

Reduce the top_k parameter in the search query to retrieve fewer documents.

Answer

Increase the chunk size in the index to provide more context per document.

Answer

Use Azure OpenAI Service on your own data integration to directly query the SQL database.

Question 8

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.. Option D is correct because it addresses all requirements: on-premises data gateway for NTLM-secured API, custom connector for OAuth 2.0, generative answers with SharePoint for multi-language policies, and SSO with Microsoft Entra ID. Option A is wrong because Power Automate flows cannot directly use NTLM without a gateway. Option B is wrong because Azure Functions cannot directly access on-premises without a gateway. Option C is wrong because Logic Apps need a gateway and custom connectors are better for OAuth 2.0.

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.

Answer

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

Answer

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 9

A retail company uses Azure AI Vision to analyze in-store video feeds. They want to detect when employees are not wearing required safety vests. The solution must send real-time alerts to store managers. Video feeds are captured from existing CCTV cameras. Which approach should you recommend?

Accepted Answer

Use Azure AI Video Indexer with a custom object detection model to detect vests in video frames. Azure AI Video Indexer supports real-time video analysis with custom models. Azure AI Vision Image Analysis works on still images, not real-time video. Custom Vision is for image classification. Azure Video Analyzer for Media (now part of Video Indexer) is correct.

Answer

Use Azure AI Vision Image Analysis with a custom model to analyze each frame offline

Answer

Train a Custom Vision classification model and deploy it to the edge on CCTV cameras

Answer

Use Azure Video Analyzer for Media to index videos and detect vests after recording

Question 10

A retail company uses the Computer Vision Image Analysis API to generate tags for product images in their e-commerce catalog. They want to automatically tag images with product categories such as 'electronics', 'clothing', and 'home goods'. The prebuilt tags often misclassify items. For example, a smartphone is tagged as 'communication device' instead of 'electronics'. You need to improve the tagging accuracy for the company's specific product categories without building a completely new model. What should you do?

Accepted Answer

Train a Custom Vision classification model with images labeled with the company's product categories.. Option D is correct because Custom Vision can be trained on the company's product images to produce custom tags that match their categories. Option A is wrong because increasing confidence threshold may reduce false positives but does not add custom categories. Option B is wrong because adding more general tags does not help. Option C is wrong because the dense captioning feature provides descriptions, not structured tags.

Answer

Use the Dense Captioning feature to generate detailed descriptions and parse them for categories.

Answer

Increase the confidence threshold for tags to reduce false positives.

Answer

Use the 'brands' feature to identify product brands and map them to categories.

Question 11

You are a developer for a healthcare startup. They are building a mobile app that allows users to take photos of prescription labels and extract medication names, dosages, and frequencies. The app must run on iOS and Android devices. The solution should use a pre-built AI service with minimal custom code. What should you recommend?

Accepted Answer

Use Azure AI Vision OCR API to extract text from the label images. Azure AI Vision OCR provides pre-built text extraction from images. Custom Vision requires custom training. Form Recognizer is for forms. Azure AI Language is for text analytics, not image text extraction.

Answer

Use Azure AI Document Intelligence (Form Recognizer) to analyze labels

Answer

Train a Custom Vision object detection model to locate and read text

Answer

Use Azure AI Language to extract entities from the label text

Question 12

A financial services company is building a computer vision solution to automatically extract data from scanned checks. The solution must recognize handwritten amounts, printed account numbers, and signature presence. The company has a large dataset of labeled check images. They need high accuracy and the ability to retrain with new data. Which Azure service should they use?

Accepted Answer

Azure AI Document Intelligence (Form Recognizer) with a custom model trained on check images. Azure AI Document Intelligence (Form Recognizer) is optimized for document extraction, supports custom models, and handles handwriting and printed text. Custom Vision is for object detection. Azure AI Vision OCR is for general text extraction. Azure AI Language is for text analytics.

Answer

Azure AI Vision OCR with a custom dataset using Custom Vision

Answer

Azure AI Language with custom entity recognition

Answer

Azure AI Vision Image Analysis with a custom model

Question 13

You are a senior AI engineer at a manufacturing company. The company has a production line that uses cameras to capture images of assembled products. The current system uses a set of rule-based heuristics to detect defects, but it has high false-positive rates. You have been tasked to design a new computer vision solution using Microsoft Azure AI services. The solution must:

- Detect defects such as scratches, dents, and misalignments in real-time as products move on the conveyor belt (frame rate of 30 fps).
- Support continuous learning: when a new defect type is discovered, the model should be updated without retraining the entire model from scratch.
- Operate with low latency (<100 ms per inference) to keep up with the production speed.
- Use only fully managed services (no custom containers or edge devices).
- The factory network has limited internet bandwidth, so the solution must minimize data transfer.

Which approach should you recommend?

Accepted Answer

Train an object detection model using Azure AI Custom Vision, export it as a Docker container, and deploy it on an on-premises server with GPU. Azure AI Custom Vision with object detection can be exported and deployed to a Docker container on-premises for low latency. Continuous learning is supported by retraining with new images. However, Option C suggests using Azure AI Video Indexer, which is for video analysis and not real-time defect detection. Option A uses Azure AI Vision Spatial Analysis, which is for people tracking. Option B uses Azure AI Document Intelligence, which is for documents. Option D is the only one that meets all requirements: Custom Vision allows retraining, container deployment for low latency, and minimal bandwidth (only model updates).

Answer

Use Azure AI Vision Spatial Analysis to detect defects in real-time by analyzing video feeds

Answer

Use Azure AI Video Indexer to index and search for defects in recorded videos

Answer

Use Azure AI Document Intelligence to analyze images of products

Question 14

You are developing an Azure AI Language solution to analyze customer support tickets. Each ticket has a subject and a description. You need to automatically classify tickets into categories (e.g., 'billing', 'technical', 'account') and extract the product name mentioned. You have a labeled dataset of 10,000 tickets with category labels and product name annotations. The solution must be cost-effective and easy to retrain as new categories emerge. You want to use a single Azure AI Language resource. Which approach should you use?

Accepted Answer

Use custom text classification for category and custom named entity recognition for product name extraction.. Option C is correct because custom text classification can handle the classification task, and custom NER can extract product names, both within the same Azure AI Language resource. Option A is wrong because the prebuilt NER cannot extract custom product names. Option B is wrong because key phrase extraction may not reliably extract product names. Option D is wrong because conversational language understanding is designed for conversational flows, not single-turn ticket classification.

Answer

Use custom text classification for category and key phrase extraction for product name.

Answer

Use conversational language understanding (CLU) to handle both classification and entity extraction in a single model.

Answer

Use custom text classification for category and prebuilt named entity recognition for product name extraction.

Question 15

A legal compliance team needs to automatically redact personally identifiable information (PII) from legal documents before sharing them with external auditors. The documents are stored in Azure Blob Storage. The solution must use Azure AI Language to detect PII and then redact the identified entities. The redaction must be performed on the original documents, and the redacted versions must be saved to a separate container. You need to design a serverless solution with minimal latency. What should you do?

Accepted Answer

Create an Azure Function triggered by Blob Storage events to detect PII, redact, and save to a separate container.. Azure Functions can be triggered by Blob Storage events, process each document using the PII detection API, redact the entities, and save the redacted version to a separate container. This is serverless and efficient. Option C is correct. Option A is wrong because Logic Apps has higher latency due to connectors. Option B is wrong because Data Factory is for data movement. Option D is wrong because Batch Service is for large-scale batch processing, not real-time.

Answer

Use Azure Data Factory to copy documents to a processing location and call an Azure Function.

Answer

Use Azure Batch Service to process documents in parallel and redact PII.

Answer

Use Azure Logic Apps with a Blob trigger to call the PII detection API and write redacted documents.

Question 16

Your company uses Azure AI Language to process legal documents. You have built a custom NER model to extract parties, dates, and obligations. The model performs well on English documents but now you need to support French and German documents. You have no labeled data in those languages. You want to use the existing English model as a starting point. The solution must be cost-effective and avoid manual labeling as much as possible. You also need to ensure that the model can be retrained quickly when new document types are added. Which approach should you take?

Accepted Answer

Enable the multilingual option in the custom NER project and retrain the model using the existing English labeled data.. Option C is correct because the multilingual option in custom NER allows training on English data and generalizing to other languages without additional labeled data. Option A is wrong because building separate models requires labeling in each language. Option B is wrong because translation of training data may not capture legal nuances. Option D is wrong because using the prebuilt NER would not extract the custom entities (parties, dates, obligations in the specific format needed).

Answer

Use the prebuilt NER model for French and German to extract entities like dates and organizations, and then map them to your custom entity types.

Answer

Use the English custom NER model as-is and translate the French and German documents to English before processing.

Answer

Build separate custom NER models for French and German by translating the English labeled data using Azure AI Translator and training new models.

Question 17

You are an AI engineer at a global e-commerce company. The company uses Azure AI Language to analyze product reviews in English, Spanish, and French. The current solution calls the sentiment analysis API for each review individually, resulting in high latency and cost. You need to design a new solution that processes reviews in batches, reduces the number of API calls, and still supports multiple languages. The solution must also extract key phrases and detect the language automatically. You have the following options:

Option A: Use the Azure AI Language synchronous API with the 'multi-language' parameter set to true. Send reviews one by one.

Option B: Use the Azure AI Language asynchronous batch API. Combine all reviews into a single batch request, but only for one language at a time.

Option C: Use the Azure AI Language asynchronous batch API. Send a single batch request with all reviews, setting the 'language' parameter to 'multi' to auto-detect language, and specify sentiment analysis, key phrase extraction, and language detection as tasks.

Option D: Use the Azure AI Translator service to translate all reviews to English, then use the Azure AI Language batch API for English-only sentiment and key phrase extraction.

Accepted Answer

Option C. Option C is correct. The asynchronous batch API supports multiple tasks (sentiment, key phrases, language detection) in a single request, and setting language to 'multi' enables auto-detection. This reduces API calls and latency. Option A is wrong because synchronous one-by-one calls increase latency/cost. Option B is wrong because it processes only one language per batch, requiring multiple batches. Option D is wrong because it adds translation cost and latency, and may lose nuances.

Answer

Option B

Answer

Option A

Answer

Option D

Question 18

A financial services company uses Azure AI Language to analyze customer support transcripts. They want to identify the main topics discussed in each conversation and generate a summary of the key points. The solution must minimize development effort and use prebuilt functionality. You need to recommend the appropriate Azure AI Language features. What should you use?

Accepted Answer

Key phrase extraction and conversation summarization.. Conversation summarization is a prebuilt feature that generates summaries of conversations, and key phrase extraction identifies main topics. Both are available in Azure AI Language without custom training. Option C combines these correctly. Option A is wrong because custom NER requires labeling. Option B is wrong because sentiment analysis does not summarize. Option D is wrong because entity linking is not for summarization.

Answer

Custom Named Entity Recognition (NER) and conversation summarization.

Answer

Entity linking and conversation summarization.

Answer

Sentiment analysis and key phrase extraction.

Question 19

You are a data scientist for Contoso Pharmaceuticals. The company has thousands of research documents in PDF format stored in Azure Blob Storage. You need to build an Azure Cognitive Search solution that enables researchers to search for documents based on chemical compound names, disease mentions, and experimental results. The solution must extract these entities using a custom AI model built in Azure AI Language. Additionally, the solution must support semantic search for natural language queries. The search index must be updated daily with new documents. You have an existing Azure AI Language custom entity extraction model that recognizes chemical compounds and diseases. The model is deployed as an endpoint. You need to configure the enrichment pipeline. What should you do?

Accepted Answer

Create a custom skill in the skillset that calls the custom entity extraction endpoint via HTTP.. Option B is correct because you need to create a custom skill in the Azure Cognitive Search skillset that calls the Azure AI Language custom entity extraction endpoint. The built-in skills do not support custom models directly. Option A is wrong because built-in skills only cover prebuilt entities. Option C is wrong because the custom model is already deployed. Option D is wrong because a custom skill is needed before adding to the index.

Answer

Deploy the custom model to Azure AI Document Intelligence and use a Document Intelligence skill.

Answer

Add the custom entity extraction as a field mapping in the indexer.

Answer

Use the built-in Entity Recognition skill and configure it to use your custom model endpoint.

Question 20

You are a machine learning engineer at a retail company. The company wants to build a product knowledge base by extracting information from product manuals, specifications sheets, and customer reviews. The data sources include PDFs, Word documents, and plain text files stored in Azure Blob Storage. The solution must: (1) extract product name, model number, price, and key features; (2) analyze customer reviews to extract sentiment and common issues; (3) enable natural language queries like 'Which products have the best reviews under $100?'; (4) handle documents in English and Spanish. You need to design a solution using Azure AI Search and Azure AI Services. Which approach meets all requirements with the least development effort?

Accepted Answer

Use a single Azure AI Search pipeline with a skillset that includes Document Layout skill, Text Translation skill (to English), Sentiment skill, and Key Phrase Extraction skill. Enable semantic search.. Option D is correct because one skillset can handle all document types (using Document Layout skill), translate Spanish to English, extract sentiment from reviews, and extract key phrases for features. Document Intelligence is not needed for reviews. Option A misses sentiment analysis. Option B uses two pipelines unnecessarily. Option C misses translation.

Answer

Use Azure AI Document Intelligence custom model to extract product info from manuals/specs. Use a separate Azure AI Search pipeline for customer reviews with sentiment analysis. Enable semantic search.

Answer

Use Azure AI Search with a blob indexer and a skillset that includes OCR skill (for scanned PDFs), Text Translation skill, Sentiment skill, and Entity Recognition skill. Enable semantic search.

Answer

Use Azure AI Document Intelligence to extract product info from all documents, then feed into Azure AI Search. Enable semantic search.