AI-102 Practice Test 13 — 15 Questions

Question 1

You are building a knowledge mining solution for a legal firm that needs to extract key clauses from thousands of scanned contract PDFs. The solution must identify parties, effective dates, and termination conditions. Which Azure AI service should you use as the primary component?

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

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is purpose-built for extracting structured data — key-value pairs, entities, tables, and clauses — from scanned documents and PDFs. Its prebuilt and custom models (including the prebuilt contract model) can identify parties, dates, and clauses, making it the correct primary component for contract clause extraction.

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Azure AI Vision

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Azure AI Language

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Azure AI Search

Question 2

You are designing a knowledge mining solution for a publishing company that needs to extract metadata from thousands of book manuscripts in various formats (PDF, Word, EPUB). The solution must identify authors, publication dates, and chapter titles. You are using Microsoft Foundry with Azure AI Search and Azure AI Document Intelligence. The manuscripts are stored in Azure Blob Storage. You need to ensure that the solution can handle all file formats. You have configured a skillset with a Document Intelligence skill for the PDFs and Word documents. However, the EPUB files are not being processed. What should you do to include EPUB files in the enrichment pipeline?

Accepted Answer

Develop a custom skill that converts EPUB files to plain text and add it to the skillset.. Azure AI Document Intelligence does not natively support EPUB files, so the built-in Document Intelligence skill cannot process them. To include EPUB files in the enrichment pipeline, you must create a custom skill that converts EPUB to plain text (or another supported format) and then add it to the skillset. This allows the pipeline to handle EPUB content alongside PDFs and Word documents.

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Use Azure AI Document Intelligence to extract text from EPUB files directly.

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Modify the Document Intelligence skill to accept EPUB files.

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Register a new data source type for EPUB in Azure AI Search.

Question 3

Refer to the exhibit. You are calling the Azure AI Language API for entity linking. What is the primary purpose of this request?

Accepted Answer

To identify entities in the text and link them to a knowledge base.. The request is configured for entity linking, which is a specific capability of the Azure AI Language API that identifies named entities in the text and resolves them to a unique identifier in a knowledge base (such as Wikipedia or a custom knowledge graph). This goes beyond simple named entity recognition (NER) by providing a canonical link, enabling disambiguation of entities with the same name (e.g., 'Washington' as a state vs. a person). The response includes both the entity name and a URL to the knowledge base entry, confirming the primary purpose is linking to a knowledge base.

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To extract named entities from the text without linking.

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To extract key phrases from the text.

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To analyze the sentiment of the text.

Question 4

Which TWO actions are required to enable a custom chatbot built with Azure OpenAI to answer questions based on a company's internal PDF documents?

Accepted Answer

Ingest the PDFs into an Azure Cognitive Search index. Azure Cognitive Search provides the indexing and retrieval capabilities needed to make PDF content searchable. By ingesting PDFs into an Azure Cognitive Search index, the chatbot can perform vector or keyword searches over the extracted text, enabling it to retrieve relevant passages to answer user questions. This is the standard approach for grounding a custom chatbot on proprietary documents without modifying the underlying model.

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Use Azure AI Document Intelligence to extract text from PDFs before indexing

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Deploy Azure AI Content Safety to filter responses

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Fine-tune the GPT model on the PDF content

Question 5

Your organization is migrating on-premises machine learning models to Azure. The models are used for real-time inference. You need to choose a service that provides managed endpoints with autoscaling and supports custom containers. Which service should you use?

Accepted Answer

Azure Machine Learning managed online endpoints. Azure Machine Learning managed online endpoints are the correct choice because they provide fully managed, autoscaling endpoints specifically designed for real-time inference. They support custom container images, allowing you to deploy any model packaged as a Docker container, and handle traffic splitting, health checks, and scaling automatically without managing underlying infrastructure.

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Azure Functions

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Azure Kubernetes Service (AKS) with manual scaling

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Azure AI Services custom vision

Question 6

You are using Azure AI Custom Vision to classify images of animals. The training set has 1000 images of cats and 1000 images of dogs. After training, the model performs well on the test set. However, when deployed, it misclassifies images of wolves as dogs. What is the most likely cause?

Accepted Answer

The training set does not include enough negative examples that look like dogs but are not.. The model misclassifies wolves as dogs because the training set lacks negative examples that are visually similar to dogs but belong to a different class. Custom Vision learns to distinguish classes based on the features present in the training images; without images of wolf-like canines labeled as 'not dog,' the model has no basis to reject wolves. This is a classic case of insufficient hard negative mining, where the model generalizes too broadly for the 'dog' class.

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The probability threshold is set too low.

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The model is overfitted to the training data.

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The training set has class imbalance.

Question 7

Your organization has a large repository of technical manuals in PDF format. You need to build a chatbot that can answer questions about the content of these manuals. Which combination of Azure services should you use?

Accepted Answer

Azure AI Search and Azure OpenAI. Azure AI Search provides the indexing and retrieval capabilities needed to search through the PDF content, while Azure OpenAI (specifically GPT models) can generate natural language answers based on the retrieved passages. This combination enables a RAG (Retrieval-Augmented Generation) pattern where the search engine finds relevant text chunks from the manuals and the language model formulates a coherent answer.

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Azure AI Speech and Azure OpenAI

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Azure AI Language and Azure AI Document Intelligence

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Azure AI Document Intelligence and Azure Bot Service

Question 8

A company is planning to use Azure AI services. They require the ability to audit all API calls for compliance. Which THREE components should they enable?

Accepted Answer

Azure Monitor. Azure Monitor (A) is correct because it is the platform that collects, stores, and surfaces activity and resource logs, enabling the audit trail of API calls required for compliance. A Log Analytics workspace (B) is correct because it is the destination where diagnostic logs are ingested and queried with KQL, allowing retention and analysis of all API call records. Diagnostic settings for the AI service (C) is correct because it is the mechanism that routes the service's resource logs (e.g., Audit, RequestResponse) and metrics to Azure Monitor/Log Analytics, which is what actually captures the API calls. Azure RBAC roles (D) only control authorization to the resource and do not record API call history, and a managed identity (E) only provides an authentication credential for the service, so neither produces the audit data required.

Answer

Azure RBAC roles for the service

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Managed identity for the service

Question 9

Your knowledge mining solution ingests documents from multiple tenants. Each tenant's data must be isolated and searchable only by that tenant. You have a single Azure AI Search service. How should you implement multi-tenancy?

Accepted Answer

Use a single index with a tenant ID field and filter queries by that field. Azure AI Search supports multi-tenancy within a single service by using a shared index with a tenant ID field. Each document is tagged with a tenant identifier, and queries are scoped using OData `$filter` expressions (e.g., `$filter=tenantId eq 'tenant123'`). This ensures data isolation while keeping costs low and management simple, as only one search service and one index are needed.

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Use separate skillsets for each tenant

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Create a separate search service for each tenant

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Use separate data sources within the same index

Question 10

You are building an agent using Azure AI Agent Service that must execute code in a sandbox environment. The code should be able to install Python packages. Which action type should you use?

Accepted Answer

code_interpreter. The code_interpreter action type in Azure AI Agent Service provides a sandboxed environment where code can be executed, and it supports installing Python packages via pip. This allows the agent to run code that requires additional libraries not pre-installed.

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function

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openApi

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httpRequest

Question 11

Which TWO Azure AI services can be used together to build a solution that transcribes customer service calls and detects sentiment?

Accepted Answer

Azure AI Language (sentiment analysis). Azure AI Speech (option B) is correct because its speech-to-text capability transcribes the audio of customer service calls into text, which is the required first step for this solution. Azure AI Language (option A) is correct because its sentiment analysis feature evaluates the transcribed text and returns sentiment scores/labels (positive, negative, neutral, mixed), satisfying the detection requirement. Together, B feeds transcription output into A for sentiment evaluation. Option C (Azure AI Translator) is not needed since the scenario does not require language translation. Option D (text-to-speech) is the reverse of what is needed—it synthesizes speech from text rather than transcribing calls. Option E (conversational language understanding) extracts intents and entities for conversational apps, not sentiment, so it does not fulfill the requirement.

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Azure AI Translator

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Azure AI Speech (text-to-speech)

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Azure AI Language (conversational language understanding)

Question 12

You are designing a solution that reads handwritten notes from patient intake forms. The solution must handle various handwriting styles. Which Azure AI capability should you use?

Accepted Answer

Azure AI Document Intelligence Read model. Azure AI Document Intelligence Read model is specifically designed to extract printed and handwritten text from documents, including patient intake forms. It uses advanced OCR capabilities optimized for varied handwriting styles and document layouts, making it the correct choice for this scenario.

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Azure AI Custom Vision

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Azure AI Vision OCR

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Azure AI Language

Question 13

You are designing a generative AI solution using Azure OpenAI Service. The solution must support multiple languages and provide consistent quality across languages. Which THREE actions should you take?

Accepted Answer

Use a model that supports multiple languages (e.g., GPT-4). GPT-4 is a multilingual model pre-trained on diverse language corpora, enabling it to generate coherent and contextually appropriate responses across many languages without additional fine-tuning. This ensures consistent quality by leveraging the model's inherent cross-lingual capabilities, which is essential for a generative AI solution that must support multiple languages.

Answer

Fine-tune the model on a dataset of a single language

Answer

Set the temperature to 0 for all requests

Question 14

You are implementing a knowledge mining solution using Azure AI Search. The data source is a large Azure Cosmos DB collection containing customer support tickets. Each ticket has fields: ticket_id, description, category, and resolution. You need to ensure that the search index can support fuzzy search and autocomplete suggestions. What should you configure in the index definition?

Accepted Answer

Set the 'searchable' attribute on the description field and define a suggester. Fuzzy search requires the 'searchable' attribute on fields to enable full-text search, and autocomplete suggestions require a 'suggester' configured on the index. The suggester defines which fields are used to generate suggestion candidates, and the 'searchable' attribute allows the description field to be tokenized and matched against partial or misspelled queries.

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Set the 'filterable' attribute on the description field

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Set the 'sortable' attribute on the ticket_id field

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Set the 'facetable' attribute on the category field

Question 15

Your company wants to moderate user-uploaded images for adult content. Which Azure AI service should you use?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the correct service because it is specifically designed to detect and moderate inappropriate content, including adult content, in images and text. It provides severity-based classifications (safe, low, medium, high) for categories such as hate, self-harm, sexual, and violence, making it ideal for user-uploaded image moderation.

Answer

Azure AI Face

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Azure AI Document Intelligence

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Azure AI Vision Image Analysis