AI-102 Practice Test 36 — 15 Questions

Question 1

Your organization is deploying a generative AI chatbot using Azure OpenAI Service. The chatbot must answer questions based on internal documents stored in Azure Blob Storage. You need to implement a retrieval-augmented generation (RAG) solution. Which THREE components are required? (Select THREE.)

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

Accepted Answer

Azure AI Search index. Azure AI Search is the core indexing and retrieval engine in a RAG solution. It ingests documents from Azure Blob Storage, creates a searchable index, and enables vector or hybrid search to retrieve relevant chunks. The chatbot then uses these retrieved chunks as context for the Azure OpenAI model to generate grounded answers.

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Azure Functions for preprocessing

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Azure SQL Database for metadata

Question 2

You are using Azure AI Language to perform entity recognition on customer feedback. You need to identify the sentiment expressed towards specific entities. Which feature should you use?

Accepted Answer

Sentiment analysis with opinion mining. Sentiment analysis with opinion mining is the correct feature because it not only detects the overall sentiment of a text but also associates specific sentiments with particular entities or aspects mentioned in the text. This allows you to determine, for example, that a customer feels positively about 'product quality' but negatively about 'customer support', which is exactly what the question requires.

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Named Entity Recognition (NER)

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Entity linking

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Key phrase extraction

Question 3

A company is building an agent that needs to perform tasks like sending emails and updating a CRM system. The agent uses Azure OpenAI with function calling. The team defines functions for these tasks. When the agent is tested, it sometimes calls the wrong function or invents function names. What should the team do to improve the reliability of function calling?

Accepted Answer

Provide better function descriptions with examples of when to use each function.. Providing better function descriptions with examples directly improves the model's ability to select the appropriate function. Azure OpenAI's function calling relies on the semantic understanding of the function definitions; clear descriptions and usage examples reduce ambiguity, helping the model map user intent to the correct function signature without hallucinating names.

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Fine-tune the model on a dataset of correct function calls.

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Reduce the number of functions to only the most common ones.

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Set the temperature parameter to 0 for deterministic output.

Question 4

You are implementing a chatbot using Microsoft Copilot Studio that helps employees find company policies. The chatbot must: - Use generative answers based on a SharePoint Online site. - Only respond with information from approved policy documents. - Include citations in responses. - Be accessible from Microsoft Teams. - Require no custom code. What should you do?

Accepted Answer

In Copilot Studio, create a new copilot. Add the SharePoint site as a knowledge source. Enable generative answers with citations. Publish to Teams.. Microsoft Copilot Studio natively supports adding a SharePoint Online site as a knowledge source, enabling generative answers that retrieve and cite only approved policy documents. It requires no custom code, automatically includes citations in responses, and can be published directly to Microsoft Teams, fulfilling all stated requirements.

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Use Power Automate to retrieve documents and feed them to Azure OpenAI. Build a custom connector for Teams.

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Build a bot using Azure Bot Service and QnA Maker. Train it with the policy documents. Deploy to Teams.

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Create a custom GPT in Azure OpenAI Studio. Upload the policy documents. Deploy via Azure API Management and expose to Teams.

Question 5

You manage an Azure AI Search solution that indexes documents from Azure Blob Storage. The index must support real-time updates when documents are added or modified. Which approach should you use?

Accepted Answer

Configure an indexer with a short schedule and change tracking.. Azure AI Search indexers can be configured with a short schedule (e.g., every 5 minutes) and change tracking (using high-water mark or integrated change detection on Azure Blob Storage) to automatically index new or modified documents without manual intervention. This provides near-real-time updates while leveraging the indexer's built-in change detection capabilities.

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Use the push API to manually upload documents.

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Manually reset and rerun the indexer after each change.

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Add a cognitive skillset to process new documents.

Question 6

Your organization needs to analyze customer feedback from social media posts to determine the sentiment (positive, negative, neutral). The solution must process up to 10,000 posts per day and provide a confidence score for each sentiment. Which Azure AI service should you use?

Accepted Answer

Azure AI Language Service. Azure AI Language Service provides pre-built sentiment analysis capabilities that can process up to 10,000 posts per day and return a confidence score for each sentiment (positive, negative, neutral). This service is specifically designed for natural language processing tasks like sentiment analysis, making it the correct choice for analyzing customer feedback from social media posts.

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Azure AI Speech Service

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Azure AI Translator

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Azure AI Language Understanding (LUIS)

Question 7

You need to extract key-value pairs from scanned forms as part of a knowledge mining solution. Which Azure AI service should you use?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is the correct service because it is specifically designed to extract key-value pairs, tables, and structured data from scanned forms and documents using prebuilt and custom models. This aligns directly with the requirement for knowledge mining from scanned forms.

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Azure AI Vision

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Azure AI Language

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Azure AI Search

Question 8

You are building a solution to extract key information from invoices using Azure AI Document Intelligence. The invoices contain fields such as invoice number, date, total amount, and line items. However, the model is not correctly extracting the line items. Which prebuilt model should you use?

Accepted Answer

Prebuilt-invoice model. The prebuilt-invoice model is specifically trained to extract key fields from invoices, including invoice number, date, total amount, and line items. Unlike other prebuilt models, it has dedicated field extraction for line item details such as description, quantity, unit price, and total, making it the correct choice for this scenario.

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Prebuilt-receipt model

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Prebuilt-idDocument model

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Prebuilt-layout model

Question 9

A company wants to use Azure AI services to extract text from scanned PDF documents. Which Azure AI service should they use?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is the correct service because it is specifically designed for extracting text, tables, and key-value pairs from scanned PDFs and images using optical character recognition (OCR) and deep learning models. Unlike general OCR APIs, Document Intelligence can handle complex layouts and preserve document structure, making it ideal for this use case.

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Azure AI Language Understanding (LUIS)

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Azure AI Computer Vision API

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Azure Cognitive Search

Question 10

A legal compliance team needs to automatically redact personally identifiable information (PII) from legal documents before sharing them with external auditors. The documents are stored in Azure Blob Storage. The solution must use Azure AI Language to detect PII and then redact the identified entities. The redaction must be performed on the original documents, and the redacted versions must be saved to a separate container. You need to design a serverless solution with minimal latency. What should you do?

Accepted Answer

Create an Azure Function triggered by Blob Storage events to detect PII, redact, and save to a separate container.. An Azure Function triggered by Blob Storage events is the canonical serverless pattern for event-driven document processing: the blob-created event fires the function, which calls the Azure AI Language PII detection/redaction API and writes the redacted output to a separate container. This minimizes latency because processing starts immediately on upload, with no orchestration overhead and automatic scaling. It also satisfies the requirement to keep the original intact and save redacted copies elsewhere.

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Use Azure Data Factory to copy documents to a processing location and call an Azure Function.

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Use Azure Batch Service to process documents in parallel and redact PII.

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Use Azure Logic Apps with a Blob trigger to call the PII detection API and write redacted documents.

Question 11

Your company uses Azure AI Vision to analyze images. You receive an alert that the number of 429 (Too Many Requests) errors has increased significantly. What is the most likely cause?

Accepted Answer

The application is exceeding the transactions-per-second (TPS) limit.. HTTP 429 (Too Many Requests) is a rate-limiting response from Azure AI Vision when the client exceeds the allowed transactions-per-second (TPS) for the chosen pricing tier. The alert indicates the application is sending requests faster than the service's capacity, triggering throttling to protect backend resources.

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The endpoint URL is incorrect.

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The API key has expired.

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The service principal does not have the correct role assignment.

Question 12

Your Azure AI Search solution uses a custom skill to call an external API. The skill runs locally but fails when deployed to the search service. What is the most likely cause?

Accepted Answer

The skill endpoint is not publicly accessible via HTTPS.. When a custom skill runs locally but fails after deployment to Azure AI Search, the most common cause is that the skill's endpoint is not publicly accessible via HTTPS. Azure AI Search indexers execute skills in the cloud and must be able to reach the external API over the internet using a secure HTTPS connection; localhost or HTTP endpoints will fail.

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The skill's output field mappings are missing.

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The skill's input field mappings are incorrect.

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The indexer name is misspelled in the skillset.

Question 13

A company wants to use Azure AI Language to automatically summarize large documents. The summarization must extract the most important sentences from each document. Which feature should they use?

Accepted Answer

Extractive summarization. Extractive summarization selects the most important sentences directly from the source document to create a concise summary, preserving the original wording. This aligns with the requirement to extract key sentences without generating new text, making it the correct choice for this scenario.

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Abstractive summarization

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Key phrase extraction

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Entity recognition

Question 14

Which TWO statements about Azure OpenAI Service content filters are true?

Accepted Answer

They can be configured with severity levels (low, medium, high). Azure OpenAI Service content filters can be configured with severity levels (low, medium, high) to control the strictness of filtering for each content category. This allows administrators to fine-tune the filter sensitivity based on their application's risk tolerance and compliance requirements.

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They only filter the output of the model

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They cannot be customized for specific use cases

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They are bypassed when using PTU deployments

Question 15

You have a computer vision solution that analyzes security camera feeds to detect people and vehicles. The solution uses Azure AI Vision Spatial Analysis. You need to ensure compliance with privacy regulations by blurring detected faces. Which feature should you enable?

Accepted Answer

Enable face blurring in the Spatial Analysis configuration. Azure AI Vision Spatial Analysis includes a built-in face blurring feature that can be enabled directly in the Spatial Analysis configuration. This allows you to automatically blur detected faces in the video feed at the edge or in the cloud, ensuring compliance with privacy regulations without requiring additional services or post-processing steps.

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Use Azure AI Content Safety to filter faces

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Post-process frames with Azure AI Face client SDK

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Enable face detection and redact faces using Azure AI Video Indexer