AI-102 Practice Test 21 — 15 Questions

Question 1

Which THREE components are required to build a custom skill for Azure AI Search enrichment?

Accepted Answer

A web API endpoint that accepts JSON input and returns JSON output.. A custom skill in Azure AI Search must be implemented as a web API that receives a JSON payload containing the input fields defined in the skill's context and returns a JSON response with the output fields. This API is called by the AI Search enrichment pipeline during indexing, allowing you to inject custom logic (e.g., entity extraction, classification) into the skillset execution.

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A database to store intermediate results.

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A Power Automate flow to orchestrate the skill.

Question 2

You are using Azure AI Language to perform entity recognition on customer feedback. You need to identify the sentiment expressed towards specific entities. Which feature should you use?

Accepted Answer

Sentiment analysis with opinion mining. Sentiment analysis with opinion mining is the correct feature because it not only detects the overall sentiment of a text but also associates specific sentiments with particular entities or aspects mentioned in the text. This allows you to determine, for example, that a customer feels positively about 'product quality' but negatively about 'customer support', which is exactly what the question requires.

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Named Entity Recognition (NER)

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Entity linking

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Key phrase extraction

Question 3

Which TWO capabilities are available in Azure AI Search to improve search relevance? (Choose two.)

Accepted Answer

Scoring profiles. Scoring profiles allow you to boost search results based on specific criteria such as field weight, freshness, or geographic distance, directly influencing relevance. Semantic ranking uses deep neural networks to re-rank results based on the semantic meaning of the query and documents, improving relevance beyond simple keyword matching.

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Filters

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Indexers

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Synonym maps

Question 4

You are building a chatbot using Microsoft Copilot Studio that needs to answer questions based on content from a set of technical manuals stored as PDFs. The content must be indexed and made available to the chatbot. You need to configure the knowledge source for the chatbot. What should you do?

Accepted Answer

Create an Azure AI Search index from the PDFs and connect it to Copilot Studio as a knowledge source.. Azure AI Search provides a dedicated indexing service that can extract and store text from PDFs, enabling Copilot Studio to perform semantic search and retrieval-augmented generation (RAG). This allows the chatbot to answer questions based on the specific content of the technical manuals, rather than relying on external or unstructured data sources.

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Upload the PDFs to a SharePoint library and use SharePoint as the knowledge source.

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Add a web search connector to the chatbot to search for information online.

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Store the content in Azure SQL Database and use it as a knowledge source.

Question 5

Your knowledge mining solution uses Azure AI Search with cognitive skills. During testing, you notice that some documents are not being enriched because the skillset execution fails. Which diagnostic step should you take first?

Accepted Answer

Enable debug mode on the skillset. When a skillset execution fails during enrichment, the first diagnostic step is to enable debug mode on the skillset. Debug mode allows you to step through each skill execution, inspect inputs and outputs, and identify exactly where the failure occurs. This targeted approach is more efficient than reviewing logs or re-running the indexer, because it provides granular, per-document debugging without waiting for a full indexer run.

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Review the indexer execution history in the portal

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Re-run the indexer with a fresh document

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Check the indexer logs in Azure Monitor

Question 6

You are building a knowledge mining solution for legal documents using Azure AI Search. The solution must extract entities like dates, organizations, and persons from PDF files and index them. Which built-in skill should you add to the skillset to perform this extraction?

Accepted Answer

Named Entity Recognition skill. The Named Entity Recognition (NER) skill in Azure AI Search is specifically designed to extract entities such as dates, organizations, and persons from text. When added to a skillset, it processes the content extracted from PDF files and outputs structured entity information that can be indexed and queried. This directly matches the requirement to extract and index named entities from legal documents.

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Language Detection skill

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Optical Character Recognition (OCR) skill

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Key Phrase Extraction skill

Question 7

A retail company uses Azure AI Vision to analyze store shelf images for product availability. The solution uses an object detection model trained on custom products. Recently, the model's performance dropped significantly due to new packaging designs. You need to improve the model's accuracy with minimal manual effort. What should you do?

Accepted Answer

Use Azure AI Vision model customization with active learning. Azure AI Vision model customization with active learning is the correct choice because it allows the model to automatically identify images where it is uncertain (low confidence predictions) and prioritize those for labeling and retraining. This minimizes manual effort while directly addressing the performance drop caused by new packaging designs, as the model iteratively improves on the specific data distribution shift without requiring a full retraining from scratch.

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Adjust the confidence score threshold to reduce false negatives

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Use the Image Analysis 4.0 dense captioning feature

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Collect a new set of images and retrain the model from scratch

Question 8

A company wants to deploy an agent using Azure Bot Service that integrates with Microsoft Teams. Which THREE steps should the team take?

Accepted Answer

Use the Bot Framework SDK to build the bot with Teams-specific features.. The Bot Framework SDK provides the necessary APIs and tools to build bots that can leverage Teams-specific features such as adaptive cards, messaging extensions, and task modules. Without the SDK, you cannot implement the channel-specific logic required for Teams integration.

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Deploy the bot code to an Azure Function.

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Write the bot in C# using Azure SDK for .NET.

Question 9

You are a data scientist at a healthcare research organization. You have been tasked with building a knowledge mining solution to extract key information from thousands of medical journal articles stored as PDFs in an Azure Blob Storage container. The articles are in English and contain tables, figures, and structured text. Your organization uses Microsoft Purview for data governance. You need to design a solution that uses Azure AI Search and Azure AI Services to extract and index the following: article title, authors, publication date, abstract, and key findings (as key phrases). The solution must also detect any mentions of drugs and dosages. The extracted information must be indexed and searchable via a custom web application. Which approach should you take?

Accepted Answer

Use Azure AI Search with a blob indexer and a skillset that includes Document Layout skill, Entity Recognition skill to extract drug names, Key Phrase Extraction skill, and custom skill to extract title/authors/date from the first page. Create an index with fields for each required element.. It combines the Document Layout skill (to extract text from PDFs including tables and figures), Entity Recognition skill (to detect drug names), Key Phrase Extraction skill (to identify key findings), and a custom skill (to parse the first page for title, authors, and publication date). This approach handles the unstructured nature of medical journal articles while meeting all extraction requirements and indexing them into a custom searchable index.

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Use Azure AI Search with a skillset that includes OCR skill, Text Translation skill to translate, Entity Recognition skill for drugs, and Key Phrase Extraction. Index the results.

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Use Azure AI Search with a blob indexer that includes a skillset with Document Layout skill to extract text, Key Phrase Extraction skill to extract key findings, and map built-in metadata for title, authors, date. Use a custom index to store the extracted fields.

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Use Azure AI Search with a skillset that includes OCR skill, Entity Recognition skill, Sentiment skill, and Key Phrase Extraction. Use a knowledge store to project the enriched data.

Question 10

You are implementing an agentic solution using Azure AI Agent Service. The agent needs to maintain conversation context across multiple turns. You configure the agent with a custom prompt that includes a 'system message' and 'few-shot examples'. However, after a few turns, the agent starts repeating the same responses. What is the most likely cause?

Accepted Answer

The 'max_context_length' is set too low, causing earlier turns to be truncated. When the 'max_context_length' is set too low, the agent's conversation history is truncated after a few turns, removing earlier user inputs and assistant responses. This loss of context causes the model to lose track of the conversation flow and revert to repeating responses, as it no longer has the full context to generate varied replies.

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The 'max_tokens' parameter is set too low for the response

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The agent is using a 'context recycling' feature that resets after each turn

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The temperature is set too high, causing the model to become deterministic

Question 11

You are deploying a chatbot using Azure OpenAI Service with a custom dataset indexed in Azure AI Search. Users report that the chatbot frequently responds with 'I don't know' for questions that the dataset should cover. What is the most likely cause?

Accepted Answer

The confidence threshold in the retrieval configuration is set too low, filtering out relevant chunks.. When the confidence threshold is set too low, Azure AI Search filters out retrieved chunks that don't meet the minimum confidence score, even if those chunks are relevant. This causes the chatbot to respond with 'I don't know' because no sufficiently confident context is passed to the Azure OpenAI model for answer generation. The issue is specifically in the retrieval configuration, not in the model's generation parameters.

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The search scope is limited to a small number of documents.

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The temperature setting in the model deployment is set too high.

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The chunk size in the index is too large, causing irrelevant chunks to be retrieved.

Question 12

You need to analyze a live video stream from a security camera to detect people entering a restricted area. Which Azure AI service should you use?

Accepted Answer

Azure Video Indexer. Azure Video Indexer supports live video stream analysis for detecting people in restricted areas using pre-built AI models. It can process live streams and generate alerts in real time. Azure Video Analyzer for Media is a deprecated predecessor and is not the optimal choice. Custom Vision requires custom model training, and Face API focuses on facial recognition rather than general person detection.

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Azure AI Custom Vision

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Azure Video Analyzer for Media (deprecated)

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Azure AI Face API

Question 13

Refer to the exhibit. You are testing a conversational language understanding (CLU) application in Azure AI Language. You send the JSON request to the prediction endpoint and receive a 200 response but with an empty topIntent. What is the most likely cause?

Accepted Answer

The conversationItem is missing a required 'role' field.. B is correct because the JSON request for a conversational language understanding (CLU) application requires a 'role' field within the 'conversationItem' object when using the conversation analysis endpoint. Without this field, the service cannot properly interpret the utterance's context (e.g., user vs. system role), leading to a successful HTTP 200 response but an empty 'topIntent' as the model fails to classify the intent.

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The projectName or deploymentName is incorrect.

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The language parameter is not supported.

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The utterance text is too short.

Question 14

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 15

A company uses Azure Custom Vision to build a classifier for defect detection on a manufacturing line. They have labeled images of products with and without defects. Which TWO actions should they take to improve model performance?

Accepted Answer

Use images with balanced numbers of defect and non-defect samples.. Balanced datasets prevent the model from becoming biased toward the majority class (e.g., non-defect images), which is critical for defect detection where defects are rare. Azure Custom Vision uses a weighted loss function during training, and class imbalance can cause the model to predict the majority class for most inputs, reducing recall for defects. Balanced samples ensure the model learns discriminative features for both classes equally.

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Train for more iterations without validation.

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Set the learning rate manually using the Custom Vision API.

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Reduce the number of images per tag to avoid overfitting.