AI-102 Practice Test 16 — 15 Questions

Question 1

You are building a knowledge mining solution that indexes technical manuals in multiple languages. The solution must enable users to search in their native language and retrieve results in the same language. Which approach should you use?

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

Accepted Answer

Use language-specific analyzers in the Azure AI Search index for each language. Azure AI Search supports language-specific analyzers (e.g., 'de.microsoft' for German, 'fr.microsoft' for French) that apply linguistic rules such as stemming, lemmatization, and stop-word removal tailored to each language. This ensures that queries and documents are processed in the same language, enabling users to search and retrieve results in their native language without translation loss.

Answer

Detect the language of the query using Azure AI Language and then use a generic analyzer

Answer

Translate all queries to English using Azure AI Translator before searching

Answer

Use a single non-language-specific analyzer like 'standard.lucene' for all documents

Question 2

Refer to the exhibit. You are creating a Custom Vision project using the Azure AI Custom Vision API. The training data is stored in Azure Blob Storage with a SAS URI. The project creation fails with an authorization error. What is the most likely reason?

Accepted Answer

The SAS token is expired or has insufficient permissions. The Custom Vision project creation fails because the SAS URI used to access training data in Azure Blob Storage has an expired token or lacks sufficient permissions (e.g., read/list). Custom Vision requires a valid SAS token with at least read and list permissions to import images from the container. An expired or under-permissioned SAS token results in an authorization error when the service attempts to access the blob storage.

Answer

The domain 'general' is invalid

Answer

The exportModelContainerUri is missing

Answer

The project type 'Classification' is not supported

Question 3

Your organization is using Azure AI Document Intelligence to process expense reports. The reports are submitted as images and need to be classified into categories (e.g., travel, office supplies) before extraction. Which feature of Document Intelligence should you use?

Accepted Answer

Custom classification model. Azure AI Document Intelligence's custom classification model is specifically designed to categorize documents (such as expense report images) into user-defined classes (e.g., travel, office supplies) before any extraction occurs. This model uses a trained classifier to assign a document type based on its visual and textual features, enabling downstream processing with the appropriate extraction model.

Answer

OCR capability

Answer

Layout extraction

Answer

Prebuilt expense report model

Question 4

You are building a knowledge mining solution that indexes technical manuals in multiple languages. The solution must enable users to search in their native language and retrieve results in the same language. Which approach should you use?

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

Accepted Answer

PII Detection skill. The PII Detection skill is the correct built-in skill for automatically identifying and redacting sensitive information like credit card numbers from indexed content in Azure AI Search. It uses pre-trained models to detect patterns such as credit card numbers, social security numbers, and other personally identifiable information, and can either mask or remove them from the text before it is stored in the search index.

Answer

Entity Recognition skill

Answer

Conditional skill

Answer

Text Translation skill

Question 5

You are designing a knowledge mining solution that must handle sensitive customer data. The solution must ensure that personally identifiable information (PII) is not returned in search results. What should you do?

Accepted Answer

Use a custom skill in the skillset to detect and redact PII before indexing. A custom skill in the Azure AI Search skillset lets you invoke a custom function (e.g., Azure Function calling Azure AI Language's PII detection or a regex-based redactor) during the enrichment pipeline, before documents are indexed. This ensures PII is detected and redacted/removed at ingestion time, so it never appears in the search index or query results.

Answer

Use Azure AI Search with encryption at rest

Answer

Implement role-based access control on the search index

Answer

Configure field mappings to exclude PII fields

Question 6

Refer to the exhibit. You are configuring an agent in Azure AI Foundry. The agent fails to start because the specified model is not available in the current Azure OpenAI resource. What should you do to resolve the issue?

Accepted Answer

Deploy the gpt-4-0613 model in the Azure OpenAI resource. The agent fails to start because the specified model (likely gpt-4-0613) is not deployed in the Azure OpenAI resource. In Azure AI Foundry, agents require an existing model deployment to invoke; you cannot use a model that hasn't been deployed. Option B correctly resolves this by deploying the required model in the Azure OpenAI resource.

Answer

Modify the system_prompt to include the model version

Answer

Change the connection_type to 'Weak'

Answer

Change the provider to 'AzureAI'

Question 7

You are a solution architect at a news agency. The agency publishes thousands of articles daily. You need to build a knowledge mining solution that enables journalists to search for articles by topic, sentiment, key people, and locations mentioned. The articles are stored as HTML files in Azure Blob Storage. The solution must also provide a summary for each article. You plan to use Azure AI Search with cognitive skills and Azure OpenAI. Which combination of skills and features should you include to meet all requirements with the best performance and accuracy?

Accepted Answer

Skillset with Entity Recognition skill, Sentiment skill, and Key Phrase Extraction skill. Use Azure OpenAI service to generate summaries via a custom skill that calls the GPT model. Enable semantic search.. The requirements are topic search, sentiment, key people, locations, and article summaries. Entity Recognition extracts people and locations, Sentiment provides sentiment, Key Phrase Extraction supports topic search, and a custom skill calling Azure OpenAI GPT generates summaries — all wired into an Azure AI Search skillset with semantic search for relevance. This combination directly maps to every requirement without extraneous skills.

Answer

Use Azure AI Document Intelligence to extract content from HTML, then use Azure AI Language to extract entities and sentiment. Index in Azure AI Search with semantic search.

Answer

Skillset with Entity Recognition skill, Sentiment skill, Key Phrase Extraction skill, and Text Translation skill. Enable semantic search.

Answer

Skillset with Entity Recognition skill, Sentiment skill, and Text Analytics for Health skill to extract medical terms. Use Azure OpenAI for summarization as a custom skill.

Question 8

You are designing an agentic solution that uses Microsoft Copilot Studio and Azure AI Search. The agent needs to answer questions based on confidential documents. Which security measure should you implement to ensure the agent only accesses documents the user has permission to read?

Accepted Answer

Implement document-level security using security filters in the search index.. Azure AI Search supports document-level security through security filters, which allow you to restrict search results based on the user's identity. By storing security identifiers (e.g., group memberships or user IDs) as a field in the index and applying an OData filter at query time, the agent can ensure users only see documents they are permitted to read. This is the standard approach for implementing row-level security in Azure AI Search.

Answer

Disable public network access on the Azure AI Search service.

Answer

Use a managed identity for the agent to access the search index.

Answer

Require multi-factor authentication for all users.

Question 9

You are designing a chatbot using Azure AI Language. The chatbot must understand user intents and also extract entities like dates and locations. Which feature combination should you use?

Accepted Answer

Conversational Language Understanding (CLU) with entities. Conversational Language Understanding (CLU) is the correct Azure AI Language feature for building a chatbot that understands user intents and extracts entities like dates and locations. CLU is specifically designed for natural language understanding (NLU) tasks, providing prebuilt and custom entity extraction alongside intent recognition, which directly matches the requirement.

Answer

Sentiment analysis and entity linking

Answer

Custom text classification and key phrase extraction

Answer

Orchestration Workflow and custom text classification

Question 10

You are using Azure AI Search to build a knowledge base for a customer support portal. The index includes a 'sentiment' field that should be populated using the Sentiment skill. However, the sentiment scores are not being written to the index. The skillset runs successfully. What is the most likely cause?

Accepted Answer

The output field mapping for 'sentiment' is missing or incorrectly defined in the indexer.. The Sentiment skill outputs a 'double' value for sentiment score, but the indexer requires an explicit output field mapping to write that value into the index's 'sentiment' field. Even when a skillset runs successfully, without a correct output field mapping in the indexer definition, the skill's output is not transferred to the index. The indexer's field mappings control how enriched data flows from the skillset's output nodes to the index fields.

Answer

The Sentiment skill is not correctly configured in the skillset.

Answer

The indexer is in a failed state and not processing documents.

Answer

The sentiment field in the index is of type 'Collection(Edm.String)' but the skill outputs a double.

Question 11

A developer is tasked with integrating Azure OpenAI Service into an application that generates product descriptions. The developer needs to ensure that the generated content does not contain offensive language. Which Azure AI service should be used in addition to Azure OpenAI?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety (D) is the correct service because it provides built-in content moderation capabilities that can detect and filter offensive, inappropriate, or harmful language in text and images. By integrating Azure AI Content Safety with Azure OpenAI, the developer can automatically screen generated product descriptions for profanity, hate speech, or other offensive content before they are displayed to users, ensuring compliance with content policies.

Answer

Azure AI Search

Answer

Azure AI Vision

Answer

Azure AI Language

Question 12

You are deploying a conversational AI solution using Microsoft Copilot Studio. The solution must provide responses based on data from an internal knowledge base stored in SharePoint. Which feature should you configure?

Accepted Answer

Enable Generative Answers and add SharePoint as a data source.. Microsoft Copilot Studio natively supports Generative Answers, which allows the AI to dynamically generate responses from specified data sources without requiring a separate QnA or custom question-answering service. By adding SharePoint as a data source, the solution can directly query the internal knowledge base stored in SharePoint and produce contextually relevant answers. This is the most straightforward and integrated approach within Copilot Studio for this scenario.

Answer

Configure Azure AI Language with custom question answering.

Answer

Create an Azure Bot Service with a QnA Maker knowledge base.

Answer

Use Azure OpenAI Service with data integration (Bring Your Own Data).

Question 13

Which TWO Azure AI services can be used to build a multilingual question-answering bot that retrieves answers from a knowledge base of documents?

Accepted Answer

Azure OpenAI Service with a RAG pattern. Azure OpenAI Service with a RAG (Retrieval-Augmented Generation) pattern is correct because it combines a large language model with a retrieval layer that fetches relevant passages from a document knowledge base (e.g., via Azure AI Search) and generates grounded, multilingual answers. Azure AI Language - Custom Question Answering is correct because it is purpose-built to create a knowledge base from documents and FAQs and return precise answers to natural-language questions, including multilingual support. Azure AI Language Understanding (LUIS) is not correct because it only performs intent and entity extraction, not document retrieval or answer generation. Azure AI Translator is not correct because it only translates text between languages and does not retrieve or answer from a knowledge base. Azure AI Document Intelligence is not correct because it only extracts structured data (text, tables, key-value pairs) from documents and does not provide question-answering retrieval.

Answer

Azure AI Language Understanding (LUIS)

Answer

Azure AI Translator

Answer

Azure AI Document Intelligence

Question 14

You need to analyze the sentiment of social media posts in real time using Azure AI Language. Which approach should you use?

Accepted Answer

Call the sentiment analysis REST API for each post. The sentiment analysis REST API in Azure AI Language is designed for real-time, per-document analysis. By calling the API for each social media post as it arrives, you achieve the lowest latency and can process posts individually without batching or streaming overhead, which is essential for real-time sentiment analysis.

Answer

Use Azure AI Search with cognitive skills

Answer

Use the batch processing feature in Azure AI Language

Answer

Send posts to an Event Hub and use Stream Analytics

Question 15

You run the Azure CLI command shown in the exhibit. After a few minutes, the deployment fails with a quota error. What is the most likely cause?

Accepted Answer

The requested capacity of 10 exceeds the available quota for the gpt-4 model in that region.. The quota error indicates that the requested capacity (10 units) for the gpt-4 model exceeds the available quota in the target region. Azure OpenAI deployments require sufficient model-specific quota, which is region- and model-specific. The error is not related to SKU name validity, model version deprecation, or resource group existence.

Answer

The SKU name 'Standard' is invalid for Azure OpenAI deployments.

Answer

The model version '0613' is deprecated and no longer available.

Answer

The resource group name 'myResourceGroup' does not exist.