AI-103 Free Practice Questions — Page 3

Question 21

You have a Microsoft Foundry project that ingests scanned PDF invoices stored in Azure Blob Storage. Each invoice contains printed fine items and has a table-based layout. Extracted results are stored as structured JSON and used as grounding data for an agent in a Retrieval Augmented Generation (RAG) solution. You need to create a single analyzer that meets the following requirements: Extracts the invoice number, invoice date, vendor name, and total amount across varying templates Returns confidence scores so that results with confidence below 0.80 can be routed for supervisor review What should you use?

Question 22

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?

Question 23

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?

Question 24

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?

Question 25

You are deploying a support agent that enables users to upload photos. You need to automatically classify uploaded images for harmful content. The solution must block content based on severity levels. What should you do?

Question 26

Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem. After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen. You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents. Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content. You need to improve response completeness. Solution: You run an evaluation flow that scores responses for completeness and blocks responses that fall below a defined threshold. Does this meet the goal?

Question 27

HOTSPOT - You need to recommend a plan to create a customer support agent by using the Microsoft Foundry Agent Service. The agent must meet the following requirements: Retain user preferences across multiple conversations. Enable users to provide contextual grounding by directly uploading documents during a chat. Which Foundry capability should you recommend for each requirement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Question 28

You have a chat app in a Microsoft Foundry project and an Azure AI Search vectorized index. You need to connect to the index to meet the following requirements: Complex questions must retrieve information from multiple chunks. Multi-turn conversations must influence retrieval planning. Retrievals must run in parallel to reduce latency. Which retrieval approach should you use?

Question 29

HOTSPOT - You have a Microsoft Foundry project that contains a deployed chat model. You have a Python service that sends API requests to the model. The service is integrated with an automated validation system that compares generated outputs against approved response patterns. Stakeholders report that small wording differences are causing validation mismatches. You need to update the request parameters to improve output stability. The solution must maximize reasoning quality. How should you complete the Python code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Question 30

You have a customer support agent that uses the Microsoft Foundry Agent Service. Sometimes, customers return to a session days later to continue the same support case, and the agent must resume with the full historical context. The agent must provide the following: Multi-turn continuity within the session Cross-session continuity for the same case Access to the full interaction history, including user messages, agent messages, tool calls, and tool outputs You need to ensure that the agent automatically reloads the complete history on each new turn. What should you do?