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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Topic 2: Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Topic 3: Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Topic 4: Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Topic 5: Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
>> Relevant Microsoft AI-103 Questions <<
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NEW QUESTION # 40
Case Study 1 - Contoso, Ltd
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?
Answer: C
Explanation:
To prevent an AI agent from revealing customer data even when sensitive information is accidentally uploaded to the underlying storage, you must configure Agent Guardrails with PII Detection and Tool Response Filtering.
Because the data repository contains unstructured product sheets and could accidentally ingest unstructured customer data, relying solely on access controls at the storage level is not enough.
Scenario:
Security and Compliance Requirements
*-> 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.
Reference:
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/securely-build-and-manage-agents-in-azure-ai-foundry/4415186
NEW QUESTION # 41
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
You need to improve response completeness. The solution must be implemented in the logic of the application code before responses are returned.
What should you do?
Answer: D
Explanation:
You should implement an evaluation and retry loop in your application code.
You must wrap the agent call inside a conditional code loop that evaluates the output against required criteria (such as a checklist of regulatory clauses), and programmatically forces a retry if information is omitted.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/ai-search
NEW QUESTION # 42
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 multimodal Al generative model that accepts image uploads and uses extracted image text to generate responses.
You discover that users can upload unsafe images and embed hidden instructions into images to manipulate the model.
You need to implement controls to mitigate the risk.
Solution: You configure a prompt shield for documents.
Does this meet the goal?
Answer: B
Explanation:
The solution does not fully meet the goal. A prompt shield for documents is the correct control for the embedded-instruction portion of the scenario. Microsoft defines Prompt Shields as protection against prompt manipulation, including attacks embedded in third-party or document-like content that is supplied to a generative model. OCR-extracted text from uploaded images is untrusted contextual content, so document attack protection is appropriate for detecting hidden instructions that attempt to override the model's intended behavior.
However, the scenario contains two separate risks: unsafe image uploads and hidden instructions embedded in images. Prompt Shields for documents address indirect prompt injection, but they do not classify or block harmful visual content in the uploaded image itself. Azure AI Content Safety image moderation is the control that scans images for harmful content categories such as sexual content, violence, hate, and self-harm with severity levels that can be used for blocking decisions.
Therefore, document prompt shielding alone is incomplete. A complete mitigation would combine image moderation for unsafe images with Prompt Shields for document attacks, and optionally Spotlighting for lower-trust third-party content. Reference topics: Content Safety image moderation, Prompt Shields, document attacks, indirect prompt injection, and multimodal safety.
NEW QUESTION # 43
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?
Answer: C
Explanation:
The most appropriate solution is Azure Content Understanding in Foundry Tools.
The Azure Content Understanding service natively combines advanced Optical Character Recognition (OCR), deep layout analysis, and pre-built generative capabilities. It handles varied document structures, multi-page tables, and template-generalizing field extraction without requiring custom machine learning model training. It operates as a low-administration, out-of-the- box solution perfectly aligned with document intelligence needs.
Incorrect:
[Not A]
Azure Language in Foundry Tools: Azure AI Language focuses primarily on unstructured text analytics, sentiment analysis, text summarization, and conversational capabilities. It lacks the built-in document layout analysis, table parsing, and visual OCR capabilities necessary to process complex scanned PDF invoice structures.
[Not C]
Azure Machine Learning model: Building, training, deploying, and managing a custom model in Azure Machine Learning requires significant data science expertise. This approach introduces high administrative overhead, complex infrastructure management, and manual pipeline maintenance, which violates the requirement for low administration.
Reference:
https://learn.microsoft.com/en-us/answers/questions/5706482/azure-document-intelligence-and-content-understand
NEW QUESTION # 44
You have an Azure subscription that contains an Azure OpenAI resource.
You plan to build an agent by using the Azure AI Agent Service. The agent will perform the following actions:
- Interpret written and spoken questions from users.
- Generate answers to the questions.
- Output the answers as speech.
You need to create the project for the agent.
What should you use?
Answer: C
Explanation:
Azure AI Foundry is a platform for designing, customizing, managing, and supporting AI applications and agents. It acts as an AI app factory, providing a unified environment with tools, models, and deployment pipelines for various AI tasks. It enables teams to build and operate AI solutions, including those powered by generative AI, while ensuring security, governance, and cost-efficiency.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/what-is-azure-ai-foundry
NEW QUESTION # 45
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