MICROSOFT · AI-103

Microsoft AI-103 Exam Practice Questions

135 questionsPDF by emailUpdated September 2026

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Question 1

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CASE STUDY

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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.

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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 configure Agent1 to meet the security and compliance requirements.

What should you use?

  1. self-harm content filtering
  2. prompt shields
  3. Personally identifiable information (PII) Detection
  4. violence content filtering
Show answer and explanation

Correct answer: B. prompt shields

Prompt shields protect agents from malicious instructions potentially hidden within images and user inputs by detecting and mitigating prompt injection attacks. This directly addresses the requirement that Agent1 must be protected from malicious instructions hidden within images in the product sheets. While PII detection (option C) is also important for protecting customer information, the specific threat mentioned, malicious instructions hidden in images, is the primary concern addressed by prompt shields as a content safety mechanism.

Why the other options are wrong

  • A. Self-harm content filtering prevents responses about self-harm, not malicious instructions in images.
  • C. PII detection protects existing customer data from being revealed, but doesn't prevent malicious instructions hidden in images.
  • D. Violence content filtering prevents violent content responses, not malicious instructions in images.

Question 2

You are planning a Microsoft Foundry project named Project1 that will contain multiple agents. Each agent will access the same Azure AI Search resource.

You need to recommend a solution to centrally manage the Azure AI Search credentials within Project1. The solution must be implemented across all the agents.

What should you recommend?

  1. Enable role-based access control (RBAC) for the Azure AI Search resource.
  2. Disable key-based access control on the Azure AI Search resource.
  3. Add a connection to the Azure AI Search resource.
  4. Create a managed private endpoint that connects to the Azure AI Search resource.
Show answer and explanation

Correct answer: C. Add a connection to the Azure AI Search resource.

Adding a connection to the Azure AI Search resource in Microsoft Foundry provides centralized credential management that can be shared across all agents within Project1. Connections in Foundry securely store and manage credentials, eliminating the need for API keys and enabling all agents to access the same Azure AI Search resource through a single managed credential. This approach follows the principle of least privilege and avoids hardcoding credentials.

Why the other options are wrong

  • A. RBAC controls access permissions but doesn't centrally manage credentials for use across agents.
  • B. Disabling key-based access alone doesn't provide a centralized credential management solution.
  • D. Managed private endpoints secure network connectivity but don't manage credentials centrally.

Question 3

You have a Microsoft Foundry project that contains three agents as shown in the following table.

You need to orchestrate the agents to ensure that the customer requests meet the following requirements:

Support a deterministic, step-based process that uses conditional branching and shared state across the agents.

Optionally trigger a ticket action based on the triage result.

The solution must minimize development effort.

What should you include in the solution?

Exhibit for question 3

  1. a workflow
  2. threads and runs without a workflow
  3. a multi-agent group chat session
  4. separate agent runs coordinated in the application code
Show answer and explanation

Correct answer: A. a workflow

A workflow is the appropriate choice because it provides deterministic, step-based process execution with conditional branching and maintains shared state across the three agents (TriageAgent, PolicyAgent, and ActionAgent). Workflows enable sequential control flow where the triage result can conditionally trigger the ActionAgent to create tickets, while minimizing development effort through declarative composition rather than imperative code. This satisfies all stated requirements: deterministic steps, conditional logic, cross- agent state sharing, and optional ticket action triggering based on triage outcomes.

Why the other options are wrong

  • B. Threads and runs without a workflow lack deterministic step-based orchestration and would require manual state management between agent invocations.
  • C. Multi-agent group chat sessions operate autonomously without the deterministic step- based control and conditional branching required for this use case.
  • D. Coordinating separate agent runs in application code maximizes development effort by requiring manual orchestration logic rather than using a declarative workflow framework.

See all 10 free questions Get the full pack, US$39

135 practice questions for Microsoft Certified: Azure AI App and Agent Developer Associate (AI-103), with full explanations.

Every question comes with the correct answer, the reasoning behind it, and a short note on why each wrong option is wrong. Work through it once with the answers, then again with the questions-only copy under exam conditions.

  • 135 questions mapped to the AI-103 exam objectives
  • Answers and explanations for every question, including the wrong options
  • A questions-only PDF for timed practice runs
  • Instant delivery by email the moment you check out
  • Free monthly updates for as long as the exam is live
  • Pass or your money back

An AI-103 attempt costs US$165 in the US. This pack is US$39, paid once.

Try 10 questions free before you buy.

Last updated September 2026 · 135 questions

What makes the AI-103 hard

AI-102 retired on 30 June 2026. AI-103 is what replaced it, and it is not the same exam with a new number.

Microsoft rebuilt the blueprint around Microsoft Foundry and agents: planning and managing Foundry AI resources, building generative apps with multistep reasoning, RAG over private data, and developing production agents with multi-agent orchestration. The classic Azure AI services, vision, language and speech, are still underneath, but the framing has shifted from calling the API to shipping the agent. Candidates coming across from AI-102 notes consistently find half their material no longer maps.

Because the exam only went live in June 2026, question banks are still small, which is exactly when the practice questions matter most. This pack has 135 practice questions for the AI-103.

About the exam

AI-103 certifies building generative AI applications and agents on Azure: Microsoft Foundry, Azure AI services, RAG patterns and multi-agent orchestration. It replaced AI-102, which retired on 30 June 2026, and renews annually via a free online assessment.

Exam domains

Skills measured as of April 2026.

  • Plan and manage an Azure AI solution: 25 to 30%
  • Implement generative AI and agentic solutions: 30 to 35%
  • Implement computer vision solutions: 10 to 15%
  • Implement text analysis solutions: 10 to 15%
  • Implement information extraction solutions: 10 to 15%

Around 40 to 60 questions, 100 minutes, pass mark 700 out of 1000, US$165 in the US, priced by local currency elsewhere, online proctored or test centre, renews annually for free.

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Questions before you buy

What do I get when I buy the Microsoft AI-103 pack?

135 practice questions as a PDF, each with the correct answer, a full explanation and a note on why the other options are wrong, plus a separate questions-only PDF for timed practice.

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Your PDF is prepared and sent to your email address after checkout, and you get a confirmation as soon as it is on its way.

Is there a free sample?

Yes. Ten questions from this pack, with answers and explanations, are free on this page and as a PDF, so you can judge the quality before you pay.

Are updates included?

Yes. The pack is updated every month for as long as the exam is live, and updates are free for everyone who has bought it.

What if I fail the exam?

We refund the pack. Sit the exam 7 to 30 days after buying, then send your official score report within 7 days of the exam date, as set out in the refund policy.

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Each purchase is licensed to one person. For a team, school or training organisation, email support@certstash.com for a licence that fits.