AI-102 Practice Test 4 — 10 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.

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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 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.

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Sentiment analysis and entity linking

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Custom text classification and key phrase extraction

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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.

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Configure data residency to a specific region

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Enable content filtering

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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.

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Use the 'Form processing' custom extraction model.

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

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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.

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Use the synchronous API for each invoice

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Train a custom model for invoice extraction

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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.

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Configure a single LUIS app with multilingual utterances

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Use Azure AI Search to index translated content

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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.

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Custom categories

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Image moderation

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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.

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Upgrade to a higher pricing tier

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Increase the number of partitions

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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.

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

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The identity type is SystemAssigned, but the hub requires UserAssigned

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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.

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Use a service endpoint and configure a managed identity

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Disable public network access and enable CMK via Azure Key Vault

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Configure IP firewall rules and enable CMK via Azure Key Vault