AI-102 Practice Test 17 — 15 Questions

Question 1

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?

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

Accepted Answer

Azure AI Vision Read API (OCR). Azure AI Vision's Read API (OCR) is a pre-built service specifically designed to extract printed and handwritten text from images, and it supports multiple languages including English and Spanish out of the box. It requires no training and is the correct choice for a general-purpose handwriting-to-text scenario.

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Azure AI Document Intelligence with a custom model

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Azure AI Language with custom entity recognition

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Custom Vision with a custom handwriting recognition model

Question 2

An organization uses Azure AI Search to power an internal knowledge base. They notice that search results are returning irrelevant documents. The index includes a 'content' field with full text and a 'tags' field with metadata. Users often search for specific terms that appear in the 'tags' field. How should you configure the search index to improve relevance?

Accepted Answer

Configure a scoring profile with a higher weight for the 'tags' field.. Configuring a scoring profile with a higher weight for the 'tags' field increases the relevance score of documents where search terms match the tags, thereby prioritizing those results. Option A (freshness-based scoring) would favor newer documents but does not address matching on tags. Option C sets the 'tags' field to use the 'keyword' analyzer, which changes tokenization but does not adjust field weighting. Option D enables semantic search on the 'content' field, which enhances understanding of natural language queries but does not specifically boost the weight of the tags field.

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Add a custom scoring profile based on freshness.

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Set the 'tags' field to use the 'keyword' analyzer.

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Enable semantic search on the 'content' field.

Question 3

You are building an agent using Microsoft Copilot Studio to handle customer returns. The agent must collect the order ID, reason for return, and then provide a return shipping label. The process requires the user to provide information step-by-step. Which type of conversation flow should you implement?

Accepted Answer

Use multiple question nodes in a sequential flow.. Microsoft Copilot Studio uses 'Question' nodes to collect user input one piece at a time in a sequential flow, which matches the step-by-step requirement for order ID, reason, and shipping label. This approach ensures each piece of data is validated before moving to the next, maintaining a guided conversation.

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Use an adaptive card to collect all inputs in one step.

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Use a single question node to collect all information at once.

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Use a generative answers node to parse the user's intent.

Question 4

Your chatbot uses Azure Bot Service and QnA Maker. Users can ask questions in natural language, and the bot returns answers from a knowledge base. Users report that the bot sometimes returns irrelevant answers. What should you do first?

Accepted Answer

Review and edit the QnA pairs to add alternative phrasings. The core issue is that the bot returns irrelevant answers because the QnA Maker knowledge base lacks sufficient alternative phrasings to match the variety of user questions. By reviewing and editing QnA pairs to add alternative phrasings, you directly improve the synonym and paraphrase coverage, which increases the confidence score for correct matches and reduces irrelevant responses. This is the first and most fundamental troubleshooting step before considering more complex integrations.

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Create multiple QnA Maker knowledge bases for different topics

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Integrate LUIS to detect user intent

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Use Azure AI Search to index the knowledge base

Question 5

Your Azure AI Document Intelligence model is failing to extract tables from scanned PDFs. The PDFs are low-quality images. What should you do first?

Accepted Answer

Verify that the Read OCR step is extracting text correctly.. The Read OCR step is the foundational layer for table extraction in Azure AI Document Intelligence. If the OCR cannot accurately recognize text from low-quality images, subsequent table extraction models will fail regardless of training or image enhancement. Verifying OCR output first isolates whether the issue is at the text recognition stage or the table parsing stage, following a systematic troubleshooting approach.

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Use Azure AI Computer Vision to enhance the image.

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Retrain the model with more table examples.

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Use a higher resolution scanner for input PDFs.

Question 6

You need to deploy a generative AI model that can generate images from text descriptions. Which Azure service should you use?

Accepted Answer

Azure OpenAI Service. Azure OpenAI Service provides access to advanced generative AI models like DALL-E, which are specifically designed to generate images from natural language text descriptions. This service offers pre-trained models that can create high-quality images based on textual prompts, making it the correct choice for this task.

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Azure Machine Learning

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Azure AI Vision

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Azure AI Language

Question 7

Your organization has a large corpus of legal documents that need to be analyzed for specific clauses. You need to extract key information such as party names, dates, and monetary amounts. The solution must be able to handle varying document formats (PDF, Word, scanned images). Which combination of Azure AI services should you use?

Accepted Answer

Azure AI Document Intelligence and Custom Entity Extraction. Azure AI Document Intelligence (formerly Form Recognizer) is designed to extract text, structure, and key-value pairs from PDFs, Word documents, and scanned images using OCR and layout analysis. Combining it with Custom Entity Extraction (via Azure AI Language's custom NER) allows you to identify domain-specific entities like party names, dates, and monetary amounts across varying formats. This pairing directly addresses the need for both document parsing and tailored entity recognition.

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Azure AI Computer Vision and Custom Entity Extraction

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Azure AI Translator and Custom Entity Extraction

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Azure AI Speech and Custom Entity Extraction

Question 8

You need to analyze a video stream from a security camera to count the number of people entering a building. Which Azure AI service is most suitable?

Accepted Answer

Azure AI Spatial Analysis. Azure AI Spatial Analysis is the most suitable service because it is purpose-built for real-time spatial analysis of video streams, including counting people entering a building. It can process live camera feeds and detect when people cross a line or enter a zone, providing real-time counts. In contrast, Azure AI Video Indexer is designed for analyzing recorded video files, not live streams, making it less suitable for real-time security camera analysis. Other options like Custom Vision and Computer Vision are for image classification and image analysis, respectively, not optimized for video streams.

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Azure AI Custom Vision

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Azure AI Computer Vision

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Azure AI Video Indexer

Question 9

You need to detect if a photo contains adult or racy content. Which Azure AI Computer Vision feature should you use?

Accepted Answer

Analyze Image API with the 'adult' parameter. The Analyze Image API with the 'adult' parameter is the correct feature because it specifically detects adult, racy, and gory content in images. When you call the Analyze Image API and include the 'adult' visual feature, Azure AI Computer Vision returns a boolean flag and a confidence score for adult and racy content classification, enabling content moderation.

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Describe Image API

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OCR API

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Tag Image API

Question 10

You see the exhibit representing an Azure Bot resource configuration. The bot is not responding to user messages. What should you verify first?

Accepted Answer

Check that the endpoint URL is correct and the web app is running. The most common reason a bot fails to respond to user messages is that the endpoint URL configured in the Azure Bot resource does not match the actual URL of the running web app, or the web app itself is stopped or unresponsive. Without a correct and reachable endpoint, the Bot Framework Service cannot forward messages to the bot's message handler, causing a complete communication breakdown.

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Ensure that LUIS app IDs are provided

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Verify that the Application Insights key is valid

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Confirm that the msaAppId is a valid GUID

Question 11

Your organization is building a chatbot using Azure OpenAI Service. The chatbot must provide citations from a set of internal documents stored in Azure Blob Storage. You need to configure the solution to minimize token usage while ensuring citations are accurate. Which approach should you use?

Accepted Answer

Use Azure OpenAI on your data with Azure Cognitive Search for hybrid retrieval. Azure OpenAI on your data with Azure Cognitive Search for hybrid retrieval combines vector search and keyword search to efficiently find relevant document chunks from Azure Blob Storage, minimizing token usage by only sending the most pertinent content to the model for citation generation. This approach ensures accurate citations without embedding all documents into the prompt or relying on model memory.

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Embed all document content into the system prompt

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Fine-tune a model on the documents so it can recall them from memory

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Use a large context window model (e.g., 32K) and include all documents in the prompt

Question 12

Your company uses Microsoft 365 Copilot to generate meeting summaries. Some users report that summaries include information from meetings they did not attend. What is the most likely cause?

Accepted Answer

Users have access to meeting artifacts via shared calendars or transcripts.. Microsoft 365 Copilot generates meeting summaries by aggregating content from meeting artifacts such as transcripts, recordings, and shared calendars. If a user has access to a meeting's transcript or recording (e.g., via a shared calendar or because the meeting was recorded and stored in a location the user can access), Copilot can include that meeting's information in summaries even if the user did not attend. This behavior is by design, as Copilot respects existing permissions on the underlying data.

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The meeting organizer granted everyone in the organization view access.

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Copilot is using Bing search results to augment summaries.

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Copilot is incorrectly configured to ignore meeting permissions.

Question 13

You are using Azure OpenAI to generate product descriptions. You notice that the descriptions are often too similar to each other. Which parameter should you adjust to increase diversity?

Accepted Answer

Increase the temperature value.. Increasing the temperature parameter makes the model more creative by raising the probability of sampling lower-probability tokens, which increases diversity in the generated text. A higher temperature (e.g., 0.9) flattens the probability distribution, so the model is less likely to always pick the most probable next word, resulting in more varied outputs.

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Decrease the top_p value.

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Increase the max_tokens value.

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Increase the frequency_penalty value.

Question 14

Refer to the exhibit. An administrator runs this Azure CLI command to deploy a GPT-4 model in Azure AI Foundry. The command fails with an error that the deployment name already exists. What should the administrator do to resolve the issue?

Accepted Answer

Use a different deployment name or delete the existing deployment.. The error message indicates that a deployment with the same name already exists in the Azure AI Foundry workspace. In Azure AI Foundry, deployment names must be unique within a workspace. The correct resolution is to either choose a different deployment name or delete the existing deployment before re-running the command. This aligns with the Azure CLI behavior where resource names (including AI model deployments) must be unique per scope.

Answer

Specify a different resource group.

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Remove the --sku-name parameter.

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Use a different model version.

Question 15

You are deploying a Custom Vision model to a production environment. The model must handle 100 predictions per second with low latency. Which deployment option should you choose?

Accepted Answer

Use a paid tier prediction endpoint with sufficient capacity.. A paid tier prediction endpoint in Azure Custom Vision is designed to handle production-scale workloads with dedicated compute resources, supporting up to 100 predictions per second with low latency. The Free tier is rate-limited and cannot sustain this throughput, while exporting as a Docker container introduces additional overhead and scaling complexity that may not guarantee the required latency or throughput without manual orchestration.

Answer

Use the Free tier prediction endpoint.

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Export the model as a Docker container and run it on Azure Container Instances.

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Use the Training API to make predictions.