AI-102 Practice Test 2 — 30 Questions

Question 1

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?

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

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.

Answer

Azure AI Speech and Azure OpenAI

Answer

Azure AI Language and Azure AI Document Intelligence

Answer

Azure AI Document Intelligence and Azure Bot Service

Question 2

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 3

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. 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 4

You are working for a healthcare organization that uses Azure AI Document Intelligence to process patient intake forms. The forms are scanned and uploaded as multi-page PDFs. The extraction accuracy for the 'diagnosis code' field is poor. You have a labeled dataset of 200 forms. You need to improve the extraction accuracy without writing custom code. The solution must also handle forms with varying layouts. What should you do?

Accepted Answer

Create a custom neural model and train it with the labeled dataset.. A custom neural model in Azure AI Document Intelligence is specifically designed to handle complex, variable-layout documents like patient intake forms. It uses deep learning to learn from labeled datasets (200 forms) and improves extraction accuracy for fields like 'diagnosis code' without requiring custom code. Neural models are superior to template-based models for varying layouts, as they generalize from patterns rather than relying on fixed spatial positions.

Answer

Use the 'Form processing' custom extraction model.

Answer

Use the US Tax W-2 predefined model as a base and customize it.

Answer

Use the General Document model to extract all text and then parse.

Question 5

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 6

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. 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 7

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.

Answer

Custom categories

Answer

Image moderation

Answer

Prompt Shields

Question 8

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 9

Refer to the exhibit. You are deploying an AI project in Microsoft Foundry using an ARM template. The deployment fails with an error indicating that the hub resource is not in the same region. What is the most likely cause?

Accepted Answer

The hub and project are in different Azure regions. In Azure AI Foundry, the hub and project resources must reside in the same Azure region because the project is a child resource of the hub and relies on the hub's regional endpoint for metadata storage and compute orchestration. The ARM template deployment fails with a region mismatch error when the location property of the project specifies a different region than the hub's location, as Azure enforces regional affinity for child resources to ensure low-latency communication and data residency compliance.

Answer

The resource group for the hub is different from the project's resource group

Answer

The identity type is SystemAssigned, but the hub requires UserAssigned

Answer

The location property is misspelled in the template

Question 10

A healthcare company is using Azure AI Document Intelligence to extract patient data from forms. They need to ensure that all extracted data is encrypted at rest using a customer-managed key (CMK) and that the service endpoint is restricted to a specific virtual network. Which combination of steps should they take?

Accepted Answer

Create a private endpoint and associate a customer-managed key in the resource encryption settings. It combines a private endpoint (which restricts the service endpoint to a specific virtual network by providing a private IP address within that VNet, eliminating public internet exposure) with a customer-managed key (CMK) in the resource encryption settings, which ensures data at rest is encrypted using a key stored in Azure Key Vault that the customer controls. This directly meets both requirements: network isolation via private endpoint and CMK-based encryption at rest.

Answer

Use a service endpoint and configure a managed identity

Answer

Disable public network access and enable CMK via Azure Key Vault

Answer

Configure IP firewall rules and enable CMK via Azure Key Vault

Question 11

Your company is deploying an Azure AI solution that uses multiple AI services. You need to ensure that all API calls are authenticated securely using managed identities. Which of the following steps is required to enable managed identity authentication for an Azure AI service?

Accepted Answer

Assign a managed identity to the Azure AI service and grant it the required RBAC role. Managed identity authentication for Azure AI services requires assigning either a system-assigned or user-assigned managed identity to the resource, and then granting that identity the appropriate RBAC role (e.g., Cognitive Services User) on the target AI service. This eliminates the need for keys or secrets in the code and leverages Azure AD tokens for secure, passwordless authentication.

Answer

Enable system-assigned managed identity at the subscription level

Answer

Store the authentication key in Azure Key Vault and reference it in the application

Answer

Configure a shared access key in the Azure AI service

Question 12

You need to analyze images to detect objects and read text from documents using a single Azure AI service. Which service should you use?

Accepted Answer

Azure AI Vision. Azure AI Vision is the correct choice because it provides both image analysis (object detection) and optical character recognition (OCR) for reading text from documents within a single service. Its Read API and Analyze Image API cover both requirements without needing separate services.

Answer

Azure AI Document Intelligence (Form Recognizer)

Answer

Azure AI Custom Vision

Answer

Azure AI Language Service

Question 13

Your Azure AI Language custom entity recognition model incorrectly extracts 'Microsoft' as an organization when it refers to the company, but fails to extract 'Microsoft' as a product when it refers to the software. How should you improve the model?

Accepted Answer

Label 'Microsoft' as both 'Organization' and 'Product' in different training sentences with appropriate context. Custom entity recognition models in Azure AI Language learn to distinguish entity types based on context. By labeling 'Microsoft' as 'Organization' in sentences where it refers to the company and as 'Product' in sentences where it refers to the software, you provide the model with the contextual clues needed to disambiguate the same token across different uses. This supervised learning approach directly addresses the model's failure to recognize the product entity.

Answer

Reduce the amount of training data to avoid confusion

Answer

Remove the 'Organization' entity type from the model

Answer

Add more training sentences without labeling the entity type

Question 14

You are designing a conversational AI solution using Microsoft Copilot Studio. The solution must: - Allow users to ask questions about company policies stored in SharePoint Online. - Use generative answers with grounding data. - Ensure that responses are based only on approved documents. - Support authentication via Microsoft Entra ID. Which THREE components must be configured as part of the solution?

Accepted Answer

Create a new data source in Copilot Studio pointing to SharePoint Online.. Copilot Studio allows you to create a new data source that connects directly to SharePoint Online, enabling the generative answers feature to retrieve and ground responses on approved policy documents stored there. This ensures that only content from the specified SharePoint site is used, meeting the requirement for grounding data from approved documents.

Answer

Create a Power Automate flow to retrieve documents from SharePoint.

Answer

Train an AI Builder model to classify policy documents.

Question 15

You are using Azure AI Content Safety to moderate user-generated content in a social media app. The solution must detect and block hate speech and self-harm content in real time. Which Content Safety feature should you use?

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 block hate speech and self-harm content in real time. It analyzes text for severity levels across multiple categories, including hate and self-harm, and returns a severity score to trigger blocking actions. This directly meets the requirement for real-time detection of these specific content types in user-generated text.

Answer

Custom category management

Answer

Severity analysis

Answer

Image moderation

Question 16

You are a solution architect at a financial services company. You need to implement a knowledge mining solution that extracts information from annual reports (PDF) of publicly traded companies. The reports contain financial tables, executive summaries, and legal disclaimers. The solution must: (1) extract the company name, fiscal year, revenue, net income, and CEO name; (2) redact any personally identifiable information (PII) like email addresses and phone numbers before indexing; (3) index the extracted data in Azure AI Search; (4) allow users to query using natural language (e.g., 'Which company had the highest revenue in 2023?'). The reports are uploaded to an Azure Blob Storage container. You have access to Azure AI Services and Azure OpenAI. Which combination of services and configurations should you use?

Accepted Answer

Use Azure AI Document Intelligence custom extraction model trained on annual reports to extract fields. In the Azure AI Search pipeline, add a PII detection skill to redact PII. Enable semantic search for natural language queries.. Azure AI Document Intelligence can be trained with a custom extraction model to accurately extract specific financial fields like company name, fiscal year, revenue, net income, and CEO name from annual report PDFs. The PII detection skill in the Azure AI Search enrichment pipeline redacts sensitive information such as email addresses and phone numbers before indexing. Enabling semantic search allows users to query using natural language. Option B relies on Azure AI Vision OCR and Azure AI Language entity extraction, which are less precise for structured table extraction and cannot guarantee the specific fields needed. Option C uses generic skills like Document Layout and Entity Recognition, which are not tailored for financial data extraction and may miss critical fields. Option D uses GPT-4, which can be inconsistent for structured data extraction from tables and does not include built-in PII redaction within the search pipeline.

Answer

Use Azure AI Vision OCR to extract text from PDFs, then use Azure AI Language to extract entities and key phrases. Index in Azure AI Search with semantic search.

Answer

Use Azure AI Search with blob indexer, include a skillset with Document Layout skill, Entity Recognition skill (for financial entities), and Key Phrase Extraction. Enable semantic search.

Answer

Use Azure OpenAI GPT-4 to process each report via a custom extraction prompt, then send extracted JSON to Azure AI Search. Enable semantic search.

Question 17

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.

Answer

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

Answer

Enable private endpoint for the AI resource.

Question 18

Your Azure AI Search index contains millions of documents. Users report that search results are slow for complex queries. You need to improve query performance without reducing result quality. Which action should you take?

Accepted Answer

Increase the number of replicas. Increasing the number of replicas in Azure AI Search distributes query load across multiple copies of the index, enabling parallel processing of complex queries. This directly improves query throughput and latency without altering the index schema or reducing result quality, as replicas provide dedicated resources for query execution.

Answer

Reduce the maximum number of results returned per query

Answer

Remove all facet fields from the index

Answer

Disable complex query types such as fuzzy and regex

Question 19

Which THREE of the following are capabilities of Azure AI Content Safety?

Accepted Answer

Sexual content detection. Azure AI Content Safety is specifically designed to detect harmful content across four categories: sexual content, hate speech, self-harm, and violence. Option A is correct because sexual content detection is one of the four core content categories that the service is trained to identify, using multi-class classification models to flag explicit or suggestive material.

Answer

Sentiment analysis

Answer

Personally identifiable information (PII) detection

Question 20

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.