AI-102 Practice Test 29 — 15 Questions

Question 1

Your Azure AI Search indexer is failing to index documents from an Azure Blob Storage container. The error message shows 'AccessDenied'. What is the most likely cause?

Accepted Answer

The search service's managed identity lacks 'Storage Blob Data Reader' role.. The 'AccessDenied' error indicates that the Azure AI Search service lacks the necessary permissions to read data from the Azure Blob Storage container. The most likely fix is to assign the 'Storage Blob Data Reader' role to the search service's system-assigned managed identity, which grants read access to blob containers and their contents.

Answer

The blob container name is misspelled in the datasource.

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The indexer execution interval is set too high.

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The index schema does not match the blob metadata.

Question 2

Which TWO Azure services can be used to perform optical character recognition (OCR) on images?

Accepted Answer

Azure Computer Vision Read API. Azure Computer Vision Read API is correct because it provides a dedicated OCR capability that extracts printed and handwritten text from images and documents. It uses deep learning models to detect text regions, recognize characters, and return structured output with bounding boxes and confidence scores.

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Azure Face API

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Azure Video Indexer

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Azure Custom Vision

Question 3

Your knowledge mining solution uses Azure AI Search. Users complain that search results are not relevant. You have enabled semantic search but results still lack context. What should you do to improve relevance?

Accepted Answer

Ensure the index includes a semantic configuration with title and content fields. Semantic search in Azure AI Search requires a semantic configuration that explicitly maps the title and content fields to be used for semantic ranking. Without this configuration, the search engine cannot apply the deep neural network models that understand context and intent, so results remain based on keyword matching even when the semantic search feature is enabled.

Answer

Increase the number of partitions to handle more data

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Configure a scoring profile with boosting based on metadata

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Increase the number of replicas to improve query performance

Question 4

A company uses Azure AI Language's custom text classification to categorize support tickets. The model was trained with 5000 labeled examples and achieves 90% accuracy. However, for a specific category (e.g., 'billing'), the model frequently misclassifies tickets that contain both billing and technical issues. Which action should you take to improve classification for this category?

Accepted Answer

Add more labeled examples for the 'billing' category, especially those that are mixed with other categories.. Adding more labeled examples for the 'billing' category, especially those that are mixed with other categories, will help the model learn to distinguish them better. Option A is wrong because reducing the number of categories may not address the specific confusion. Option C is wrong because increasing the training epochs may lead to overfitting. Option D is wrong because using a different algorithm is not an option in Azure AI Language's custom text classification.

Answer

Reduce the number of categories to simplify the classification.

Answer

Increase the number of training epochs to further train the model.

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Use a different classification algorithm, such as a neural network.

Question 5

Your Azure AI solution uses multiple AI services including Computer Vision and Language. To reduce costs, you want to share a single key and endpoint across services. Which Azure resource type should you deploy?

Accepted Answer

Azure AI services multi-service resource. D is correct because Azure AI services multi-service resource provides a single endpoint and key that can be used across multiple AI services (e.g., Computer Vision, Language, Face, etc.), reducing management overhead and cost by consolidating billing. This resource type is designed specifically for scenarios where you want to share credentials across services without deploying separate single-service instances.

Answer

Separate single-service resources for each AI service

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Azure Key Vault for storing keys

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Azure API Management gateway

Question 6

You want to use the Azure AI Language service to summarize long customer support conversations into a short summary. Which feature should you use?

Accepted Answer

Conversational Summarization. Conversational Summarization is the correct feature because it is specifically designed to condense multi-turn dialogues, such as customer support conversations, into concise summaries. Unlike generic text summarization, it understands the conversational flow, speaker turns, and context to produce a coherent summary of the interaction.

Answer

Sentiment Analysis

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Entity Extraction

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Key Phrase Extraction

Question 7

You are using Azure AI Language to analyze social media comments. You need to identify the language of each comment and then extract key phrases. Which TWO features should you use? (Select TWO.)

Accepted Answer

Language detection. Language detection is the correct feature because it identifies the language of each comment, which is a prerequisite for further analysis. Key phrase extraction is the second correct feature because it extracts important terms from the text, directly addressing the requirement to 'extract key phrases' after language identification.

Answer

Sentiment analysis

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Summarization

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Entity recognition

Question 8

You plan to use Azure AI Content Safety to detect hate speech in user-generated content. Which type of content safety is most appropriate for this scenario?

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 filter hate speech, along with other harmful content categories like violence and self-harm, in user-generated text. It uses machine learning classifiers trained on a vast corpus to assign severity scores across predefined categories, making it the direct and most appropriate tool for this scenario.

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Custom categories

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Image moderation

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Prompt Shields

Question 9

A healthcare organization uses Azure AI Language to extract medical entities from clinical notes. The solution must comply with HIPAA and data residency requirements. Which configuration is essential?

Accepted Answer

Create the AI resource in the required Azure region.. Data residency requirements dictate that the Azure AI Language resource must be physically located in the specific Azure region where the clinical notes and extracted medical entities are permitted to reside. Creating the resource in the required Azure region ensures that all data at rest and in transit stays within that geographic boundary, which is a fundamental compliance step for HIPAA and data residency. Other configurations like encryption keys or private endpoints enhance security but do not satisfy the core residency requirement.

Answer

Enable diagnostic logging for all operations.

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Use a customer-managed key (CMK) for encryption.

Answer

Enable private endpoint for the AI resource.

Question 10

You are developing an app that analyzes images of restaurant receipts. The app must extract the merchant name, transaction date, and total amount from each receipt. You want to minimize development effort and use a prebuilt Azure AI service. Which service should you use?

Accepted Answer

Azure AI Document Intelligence with the prebuilt-receipt model. Azure AI Document Intelligence provides a prebuilt receipt model that is purpose-built to extract merchant name, transaction date, and total from receipt images. It returns structured data without requiring custom model training or text parsing, which aligns with the goal of minimizing development effort. Other services either return unstructured text or require custom training.

Answer

Azure AI Language with custom named entity recognition

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Azure AI Vision with the Read API

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Azure AI Custom Vision with a classification model

Question 11

Refer to the exhibit. A developer is testing the Text Analytics sentiment analysis API and receives a 401 error. What is the most likely cause?

Accepted Answer

The subscription key is invalid or expired.. HTTP 401 Unauthorized specifically means the request lacked valid authentication credentials — for Azure Cognitive Services Text Analytics, that means the Ocp-Apim-Subscription-Key header is missing, malformed, or the key has been regenerated/expired. The endpoint and API version are validated separately (404/400), and resource group naming is irrelevant to runtime API calls. Therefore the invalid or expired subscription key is the most likely cause.

Answer

The API version is not supported.

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The endpoint URL is incorrect.

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The resource group is misspelled.

Question 12

You are developing an application that uses Azure AI Vision to analyze images of products on an assembly line. The application must identify the presence of specific objects, such as screws, bolts, and washers, and return their bounding boxes. You have a limited set of labeled images for each object type. Which Azure service should you use to train a model that meets these requirements?

Accepted Answer

Azure Custom Vision object detection. Azure Custom Vision object detection is the correct service because it enables you to train a custom model using your labeled images to detect specific objects and return bounding boxes. It is designed for scenarios where you need to identify custom objects with a limited dataset.

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Azure AI Vision Face API

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Azure AI Vision Image Analysis with the 'objects' feature

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Azure Video Indexer

Question 13

Match each Azure Cognitive Services endpoint to its purpose.

Accepted Answer

Computer Vision: Analyze images to extract visual features, descriptions, and objects.. The correct matches are: Computer Vision -> image analysis, Text Analytics -> sentiment analysis, Form Recognizer -> form data extraction, Face API -> face detection and attributes. Common confusions include mixing up Computer Vision with Translator, and Text Analytics with Content Moderator.

Answer

Computer Vision: Translate text between different languages.

Answer

Text Analytics: Moderate text content for offensive language.

Question 14

You are building an Azure AI Vision solution that analyzes live video from a camera mounted on a delivery truck. The solution must read street signs in real time and return the recognized text with bounding box coordinates. You need to minimize latency and cost. Which Azure AI Vision feature should you use?

Accepted Answer

Optical character recognition (OCR) synchronous API. The synchronous OCR API is the correct choice because it extracts printed text with bounding boxes in a single call, which suits low-latency, real-time video frame analysis. The Read API and Document Intelligence are better for documents and asynchronous processing, and Custom Vision detects objects rather than reading text. Using synchronous OCR minimizes latency and cost for live street sign recognition.

Answer

Azure AI Document Intelligence prebuilt-read model

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Custom Vision object detection model

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Read API with asynchronous processing

Question 15

You are building an agent with Azure AI Agent Service. The agent must consult a product catalog stored in an Azure AI Search index before answering questions, and you want the retrieval to happen automatically on every run without writing any orchestration code. You have already created the index and a project connection to it. Which configuration should you apply to the agent?

Accepted Answer

Attach the Azure AI Search index as a knowledge tool on the agent and let the service invoke it during runs.. Attaching the Azure AI Search index as a knowledge tool is the native way Azure AI Agent Service performs retrieval-augmented generation. The service manages query formulation, execution against the index through the project connection, and injection of results into the model context, satisfying the no-orchestration-code requirement while reusing the existing index.

Answer

Enable grounding by uploading the catalog documents to the agent's file storage and referencing them in the system message.

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Create a function tool that calls the Azure AI Search REST API and register it as a tool on every run.

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Add the search index endpoint and admin key to the agent's instructions so the model can call it directly.