AI-103 Practice Questions — Page 6

You have an app named App1 that uses a Microsoft Foundry multimodal model deployment.

App1 runs optical character recognition (OCR) on uploaded images and appends the OCR output to the prompt as additional context.

Some uploaded images contain embedded text.

You need to prevent potentially malicious instructions from being processed by the model.

What should you use?

You have a Microsoft Foundry project that contains an agent.

You need to process mixed-format documents that contain scanned text, tables, and multicolumn layouts. The extracted content must preserve the document structure and be converted into the Markdown format for downstream reasoning.

What should you configure first?

You have an application that processes scanned PDF invoices. The invoices have varied layouts and include multipage tables.

You have a pipeline that uses optical character recognition (OCR) and extracts totals and invoice numbers. The results are often incorrect because the document structure is ignored.

You need to implement a solution that provides OCR, layout analysis, and template-generalizing field extraction. The solution must NOT require training a custom model. The solution must minimize administrative effort.

What should you include in the solution?

You have a Microsoft Foundry project that contains an agent.

The agent uses a knowledge source built from documents stored in Azure Blob Storage. The documents include digitally scanned PDFs that contain multipage tables.

You have an ingestion job that extracts only plain text, causing loss of table structure, headings, and page-number metadata.

Users frequently ask questions that require the retrieval of specific table rows across the pages.

You need to configure an ingestion job for a Retrieval Augmented Generation (RAG) pipeline that performs optical character recognition (OCR) on scanned PDFs, preserves tables and headings as structure-aware chunks, and stores page-number metadata with each chunk.

How should you configure the ingestion job?

You have a Microsoft Foundry project that contains an agent.

The agent uses Azure AI Search as the retriever.

You plan to ingest PDF into an Azure AI Search index to ensure that the agent can ground responses in texts in both documents and embedded images.

Users require citations that link to the source files.

You need to ensure that during indexing, the images are extracted into a structure that can be used as input for the built-in optical character recognition (OCR) skill.

Which indexing approach should you use?

Case Study

This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.

To answer the case study questions, you will bed to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study.

A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it.

To start the case study

To display the first question in this case study, select the “Next” button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions. When you are ready to answer a question, select the “Question” button to return to the question.

Overview

Company Information

Contoso, Ltd is a multinational retail company that builds, deploys, and manages generative AI and agent-based solutions by using Microsoft Foundry.

Existing Environment

Identity Environment

Contoso uses Microsoft Entra ID for identity management, authentication, and authorization capabilities that enable agents to access organizational resources and services.

Contoso recently formed a new AI engineering team named Agent1Dev Team to optimize and maintain existing AI solutions.

The team collaborates with solution architects, DevOps engineers, and security engineers to design, implement. monitor, and secure AI applications.

Contoso also has a team named Agent1Test Team that is responsible for validating AI solutions before the solution deployments.

Generative Environment

Contoso has a Microsoft Foundry deployment that contains two projects named Project1 and Project2.

Project1

Project1 contains a customer support agent named Agent1 that assists customers with product inquiries and troubleshooting requests.

Agent1 has the following configurations:

Agent1 uses a base model deployment.

A safety evaluation pipeline is NOT enabled.

Tool invocation approval workflows are NOT enabled.

Conversation memory constraints are NOT configured.

Agent1 interacts with customers by using digital support channels and answers general questions about Contoso products.

Project1 is deployed to an Azure region located in the European Union (EU).

Agent1Dev Team will use Project1 to optimize and maintain Agent1.

Project2

Project2 contains a deployed video generation model. The marketing department at Contoso has access to Project2 and plans to use the model to develop a video creation solution.

Development of the solution is incomplete.

Data Environment

Contoso stores product-related information in Azure resources that support AI applications.

The Azure environment contains an Azure Blob Storage account named storage1 that stores product detail sheets for all the Contoso products.

The product sheets include specifications, feature descriptions, and product support information that Agent1 can use to answer customer questions. The product sheets are stored in the PDF format.

Problem Statements

Contoso identifies the following issues:

Agent1 has only general knowledge of the Contoso products.

A recent chat interaction with Agent1 was analyzed for sentiment. The results of the analysis have NOT been processed yet.

Agent1 does NOT use the detailed product information in the product sheets stored in storage1 when responding to customer questions.

The finance department at Contoso reports that vendor invoices must be reviewed manually to ensure that the invoices match the terms defined in the vendor contracts. The invoices contain tables, logos, and varied layouts that make the documents difficult to process consistently.

Requirements

Planned Changes

Contoso plans to implement the following changes:

Implement a solution for Project1 that analyzes the vendor invoices by evaluating both the visual layout and the textual content of the invoices, so that the invoice details can be verified against the vendor contract terms.

Update the base model deployment used by Agent1 and standardize the model version to ensure continuity and consistent responses.

Enable Agent1 to retrieve and use the detailed product information from the product sheets stored in storage1.

Implement an indexing solution for the product sheets that Agent1 can use to answer customer questions.

Complete the development of the video creation solution.

Technical Requirements

Contoso identifies the following technical requirements:

The model deployment used by Agent1 must support scalable, high-throughput generative AI workloads and dynamically scale to handle variable customer support traffic, without requiring reserved throughput capacity.

The product sheets must be processed by using an indexing pipeline that enables semantic and vector search, so that Agent1 can retrieve the relevant product information.

Responses generated by using the product sheet information must be relevant, complete, and accurate.

Agent1 must be able to use the product sheets to answer natural language questions about product details.

The model version used by Agent1 must remain consistent to ensure stable responses.

The data processed by the model must remain within the EU.

Security and Compliance Requirements

Contoso identifies the following security and compliance requirements:

API keys must NOT be used to access Foundry-deployed models.

Access to the Azure resources must follow the principle of least privilege.

The developers at Contoso must authenticate to Microsoft Foundry resources by using Microsoft Entra authentication.

Access to Project1 must be assigned to the members of Agent1Dev Team by using a security group named SC_Agent1_Dev.

Access to Project1 must be assigned to the members of Agent1Test Team by using a security group named SC_Agent1_Test.

Agent1 must never reveal customer information, even if a document that contains customer data is added erroneously to the product sheet repository in storage1.

The product sheets might contain images that include embedded text. Agent1 must be protected from malicious instructions potentially hidden within the images.

Business Requirements

Contoso identifies the following business requirements:

Users that interact with Agent1 must have a personalized experience in future interactions, including the ability for Agent1 to retain conversation context and recall relevant information from previous interactions.

Agent1 must answer questions only about the products sold by Contoso.

You need to recommend an invoice review solution that resolves the issue reported by the finance department.

What should you include in the recommendation?

You have a Microsoft Foundry project that contains an agent.

The knowledge source for the agent is a set of scanned PDF troubleshooting guides stored in Azure Blob Storage. The guide pages contain two-column layouts and tables.

You use Azure Content Understanding in Foundry Tools to process the PDFs.

You plan to ingest the processed content into an index for Retrieval Augmented Generation (RAG) and store extracted fields for downstream automation.

Stakeholders must be able to verify where each extracted field value came from in the original PDF and route low-reliability extractions for manual review.

You need to ensure that the Content Understanding document analyzer output includes a per-field confidence score and source grounding to locations within the source document.

What should you do?

You are building a speech processing solution in Microsoft Foundry for a customer support platform.

The platform will transcribe live phone calls, so that supervisors at your company can view call transcripts and detect issues while the calls are in progress. The call audio will arrive as a continuous stream from the telephony system.

You need to ensure that the call transcripts appear within only a few seconds of the audio stream.

What should you do?

You are creating an agent workflow in a Microsoft Foundry project to support natural voice interactions.

The agent must receive continuous audio input, convert the input into text for reasoning, and then return spoken responses to a user. The workflow must meet the following requirements:

Support turn-taking dynamics, where the agent begins to generate the speech output before the user finishes speaking.

Operate with low latency to maintain conversational experience.

You need to enable both speech to text and text to speech in a real-time agent interaction.

What should you do?

You have an application named App1 that uses Azure Speech in Foundry Tools to transcribe live calls.

Transcript segments often contain both English and Spanish. App1 sends each segment to Azure Translator in Foundry Tools to translate to another language.

Sometimes, mixed-language segments result in incomplete or incorrect translations.

You need to reduce translation errors. The solution must ensure that the entire transcript is translated successfully.

What should you do before sending the segments to Translator?