AI-102 Practice Exam 57 — 20 Questions

Question 1

Refer to the exhibit. You are using Azure AI Document Intelligence with a layout model. The pipeline returns an empty tables array even though the document contains tables. The OCR step extracts text correctly. What is the most likely issue?

Accepted Answer

The layout extraction step is not correctly identifying table structures.. The layout model in Azure AI Document Intelligence performs OCR and then uses a layout extraction step to identify structural elements like tables. If the OCR extracts text correctly but the tables array is empty, it indicates that the layout extraction step failed to detect the table boundaries or cell structure, not that OCR missed the text. Option D correctly identifies this as the most likely issue.

Answer

The OCR step is not recognizing table cells.

Answer

The table extraction step is misconfigured.

Answer

The output field mapping for tables is missing.

Question 2

Your Azure AI Search index is experiencing high query latency. You have enabled semantic search and custom scoring profiles. You need to reduce latency without degrading search quality. Which action should you take?

Accepted Answer

Increase the number of replicas.. Increasing the number of replicas distributes query load across multiple copies of the index, allowing parallel processing of search requests. This directly reduces query latency without altering the search logic, scoring profiles, or semantic enrichment, thus preserving search quality.

Answer

Remove custom scoring profiles.

Answer

Reduce the number of partitions.

Answer

Disable semantic search.

Question 3

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

Answer

Configure a sentiment analysis endpoint

Answer

Use the prebuilt entity extraction model

Question 4

Your organization is using Azure OpenAI Service to generate content. You need to ensure that the content meets safety guidelines by filtering harmful outputs. What should you configure?

Accepted Answer

Configure the content filters in the Azure OpenAI Studio.. Option B is correct because content filters in Azure OpenAI Studio allow you to define severity levels (safe, low, medium, high) for categories like hate, sexual, violence, and self-harm, which are enforced at the inference API level to block or flag harmful outputs before they reach the user. This is the primary configuration for filtering model-generated content in Azure OpenAI Service.

Answer

Enable the Responsible AI dashboard.

Answer

Use Azure AI Content Safety APIs to analyze outputs.

Answer

Set the system message to instruct the model to avoid harmful content.

Question 5

You are deploying an Azure AI Search solution that indexes customer support tickets. The solution must support fuzzy search for misspelled keywords and boost results from premium customers. Which two features should you configure?

Accepted Answer

Scoring profiles. Scoring profiles allow you to boost search results based on specific criteria, such as customer tier, by assigning higher weights to certain fields or adding functions that increase scores for premium customers. This directly supports the requirement to prioritize results from premium customers.

Answer

Synonym maps

Answer

Suggesters

Answer

Custom analyzers

Question 6

Your organization wants to implement a document processing pipeline that extracts text from scanned PDFs and identifies named entities. Which two Azure AI services should you use?

Accepted Answer

Azure AI Language. Azure AI Document Intelligence (formerly Form Recognizer) is used to extract text from scanned PDFs via OCR, while Azure AI Language provides pre-built named entity recognition (NER) to identify entities like people, organizations, and locations. Together, they form a complete pipeline: Document Intelligence handles the image-to-text conversion, and Language processes the extracted text for entity extraction.

Answer

Azure AI Custom Vision

Answer

Azure AI Translator

Answer

Azure AI Speech

Question 7

You manage an Azure AI Search service that indexes legal documents. The search latency is high, and you need to improve query performance without reducing index size. Which action should you take?

Accepted Answer

Increase the number of replicas. Increasing the number of replicas distributes query load across multiple copies of the index, which directly improves query throughput and reduces latency. Replicas are designed for scaling query operations without changing the index size or storage capacity.

Answer

Upgrade to a higher pricing tier

Answer

Increase the number of partitions

Answer

Reduce the number of searchable fields

Question 8

You deploy a Custom Vision object detection model to classify vehicles. The model works well in good lighting but fails in low-light conditions. What is the most appropriate action?

Accepted Answer

Add images with different lighting conditions to the training set. Option A is correct because the core issue is a data distribution mismatch: the model was trained primarily on well-lit images and lacks exposure to low-light examples. Adding images with diverse lighting conditions directly addresses this by enriching the training dataset, enabling the model to learn robust features for low-light scenarios. This aligns with the fundamental principle that Custom Vision models are only as good as the training data they receive.

Answer

Increase the probability threshold

Answer

Increase the number of training iterations

Answer

Use a domain-specific model for vehicles

Question 9

You are designing a solution that uses Azure AI Document Intelligence to extract data from invoices. The solution must handle high throughput and process documents in batch. Which TWO configuration options should you use?

Accepted Answer

Provision a standard (S0) tier Document Intelligence resource. The standard (S0) tier is required for production-grade throughput because it offers higher transactions per second (TPS) and better scalability compared to the free (F0) tier. For batch processing of invoices, the async document analysis API is the correct choice because it supports submitting multiple documents in a single call and returns results via a polling mechanism, which is designed for high-volume scenarios.

Answer

Use the synchronous API for each invoice

Answer

Train a custom model for invoice extraction

Answer

Store output directly in Azure Blob Storage without API calls

Question 10

A team is building a chatbot using Azure Bot Service and Language Understanding (LUIS). The chatbot must handle multiple languages. What should you configure?

Accepted Answer

Create separate LUIS applications for each language. Option D is correct because LUIS does not natively support multilingual models within a single app. Each LUIS application is designed for a single language, so to handle multiple languages, you must create separate LUIS apps—one per language—and route user utterances to the appropriate app based on the detected language. This ensures accurate intent and entity recognition tailored to each language's linguistic patterns.

Answer

Configure a single LUIS app with multilingual utterances

Answer

Use Azure AI Search to index translated content

Answer

Use Azure Translator to translate utterances before calling LUIS

Question 11

Refer to the exhibit. A developer creates an agent in Azure AI Foundry with a code_interpreter tool. The agent is supposed to generate plots but returns errors. What is the most likely cause?

Accepted Answer

The code interpreter tool does not support visualization libraries. The code_interpreter tool in Azure AI Foundry is designed for executing Python code in a sandboxed environment, but it does not include or support visualization libraries such as Matplotlib, Seaborn, or Plotly. Therefore, any attempt to generate plots using these libraries will result in errors, making option C the correct answer.

Answer

The instructions are too generic

Answer

The JSON syntax is incorrect

Answer

The agent name is invalid

Question 12

You are building a custom copilot using Microsoft Foundry. The copilot must answer questions based on internal documents stored in SharePoint. Which two components are essential for this solution?

Accepted Answer

Azure AI Search with a SharePoint indexer. Azure AI Search with a SharePoint indexer is essential because it ingests and indexes the internal documents from SharePoint, enabling full-text and vector search capabilities that the copilot uses to retrieve relevant content. This indexer automates the crawling of SharePoint sites and keeps the search index synchronized with document changes, which is critical for answering questions based on up-to-date internal data.

Answer

Azure AI Language conversational language understanding (CLU)

Answer

Azure AI Document Intelligence (Form Recognizer)

Question 13

You are deploying an Azure AI Search solution for a global e-commerce platform. The index must support real-time updates from a product catalog stored in Azure Cosmos DB, with a maximum indexing latency of 10 seconds. The solution must also handle 5000 queries per second (QPS) during peak hours. What should you configure?

Accepted Answer

Use Standard S3 tier with 12 replicas and enable incremental indexing.. Option C is correct because the Standard S3 tier supports high query volumes (up to 5000 QPS) with 12 replicas, and incremental indexing enables near-real-time updates from Cosmos DB by processing only changed documents, keeping latency under 10 seconds. The combination of sufficient replicas for throughput and incremental indexing for low-latency indexing meets both requirements.

Answer

Use the Storage Optimized L2 tier with 6 partitions and 4 replicas.

Answer

Use the Free tier and enable change tracking on Cosmos DB.

Answer

Use Standard S2 tier with 3 replicas and indexer scheduling every 5 minutes.

Question 14

Your organization uses Azure AI Document Intelligence to extract data from invoices. The solution must identify custom fields not present in the prebuilt models, such as 'purchase order number' located in varying positions across documents. What should you do?

Accepted Answer

Train a custom extraction model using labeled sample invoices.. Option D is correct because Azure AI Document Intelligence (formerly Form Recognizer) allows you to train a custom extraction model using labeled sample invoices. This approach enables the model to learn custom fields like 'purchase order number' that appear in varying positions, which prebuilt models cannot handle. By providing labeled examples, the model generalizes to extract the field accurately from new documents.

Answer

Use the layout model and apply manual post-processing.

Answer

Use Azure AI Forms Recognizer with prebuilt receipt model.

Answer

Use the prebuilt invoice model with field merging.

Question 15

A healthcare organization uses Azure AI Language to extract medical entities from clinical notes. The solution must comply with HIPAA. What should you configure?

Accepted Answer

Use Azure AI Language with HIPAA-enabled service and enable encryption. Option A is correct because Azure AI Language can be configured with a HIPAA-eligible service tier, and enabling encryption at rest and in transit ensures compliance with HIPAA's security and privacy rules. This configuration meets the healthcare organization's requirement to protect protected health information (PHI) when extracting medical entities from clinical notes.

Answer

Configure data residency to a specific region

Answer

Enable content filtering

Answer

Isolate the service in a virtual network

Question 16

You are designing a responsible AI solution for a healthcare application that uses Azure AI Vision to analyze medical images. The solution must minimize bias across demographic groups and provide explainability for predictions. Which combination of services should you use?

Accepted Answer

Azure AI Content Safety for content moderation and Fairlearn with Azure Machine Learning interpretability.. Option D is correct because Azure AI Content Safety helps filter harmful content and reduce bias in model outputs, while Fairlearn with Azure Machine Learning interpretability provides tools to assess fairness across demographic groups and explain model predictions. This combination directly addresses the requirements of minimizing bias and providing explainability for medical image analysis.

Answer

Azure AI Document Intelligence for extracting metadata and Azure AI Language for sentiment analysis.

Answer

Azure AI Translator for multilingual support and Azure AI Metrics Advisor for monitoring.

Answer

Azure AI Search for indexing results and Azure AI Anomaly Detector for outliers.

Question 17

You are planning a multi-region deployment of Azure AI services to ensure high availability and low latency for global users. You need to decide how to manage API keys and endpoint URLs across regions. What is the recommended approach?

Accepted Answer

Use Azure API Management to expose a single endpoint that routes to regional Azure AI services resources, each with its own key stored in Key Vault.. Option C is correct because it combines Azure API Management as a unified gateway with regional Azure AI Services resources, each secured by individual keys stored in Azure Key Vault. This architecture provides global load balancing, regional failover, and centralized key management without exposing keys in application code. API Management can route requests based on latency or geography, ensuring high availability and low latency while Key Vault rotates keys securely.

Answer

Create separate Azure AI services resources per region and hardcode the keys in application configuration.

Answer

Create a single Azure AI services resource in one region and share the key across all applications.

Answer

Use Microsoft Entra ID authentication with managed identities and have each application call the region-specific endpoint directly.

Question 18

You are planning an Azure AI solution that uses Azure AI Document Intelligence to extract data from scanned PDFs. The solution must support both structured and unstructured documents. The extracted data must be validated against a predefined schema before being stored in Azure Cosmos DB. What should you use for schema validation?

Accepted Answer

Azure AI Document Intelligence custom extraction model with a defined schema.. Option B is correct because Azure AI Document Intelligence custom extraction models allow you to define a schema (field names, types, and relationships) that the service uses to extract and validate data from both structured and unstructured documents. This built-in schema validation ensures extracted data conforms to your predefined structure before it is stored in Azure Cosmos DB, eliminating the need for additional validation logic.

Answer

Azure Functions to parse and validate the extracted data.

Answer

Azure AI Document Intelligence prebuilt model for invoices.

Answer

Azure Logic Apps with a JSON schema validation step.

Question 19

Your Azure AI Search index stores customer support tickets. You need to implement a search feature that returns semantically similar results even if the query uses different wording. Which configuration should you enable?

Accepted Answer

Enable semantic search and configure a semantic configuration. Semantic search in Azure AI Search uses deep neural networks to understand the intent and context of a query, returning results that are semantically similar even when the wording differs. By enabling semantic search and configuring a semantic configuration, you define which fields are used for summarization and ranking, which directly addresses the requirement for meaning-based matching rather than keyword matching.

Answer

Use simple query parsing with searchMode=any

Answer

Add a synonym map with custom entries

Answer

Enable fuzzy search on the index

Question 20

You deploy a custom vision model using Azure AI Custom Vision. After deployment, you notice the model has high accuracy on training data but low accuracy on new images. What is the most likely cause?

Accepted Answer

The model is overfitted to the training data. High accuracy on training data but low accuracy on new images is the classic symptom of overfitting, where the model has memorized the training examples (including noise and irrelevant patterns) rather than learning generalizable features. In Azure AI Custom Vision, this typically occurs when the training dataset is too small, too homogeneous, or lacks sufficient variation, causing the model to fail on unseen data.

Answer

The training time was too short

Answer

The training dataset has too few images

Answer

The wrong domain was selected during training

AI-102 Practice Exam 57 — 20 Questions

Exhibit

AI-102 Practice Exam 57 — 20 Questions

Exhibit