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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Topic 2: Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Topic 3: Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Topic 4: Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Topic 5: Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
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NEW QUESTION # 35
Drag and Drop Question
You are developing an application that will detect faulty components produced on a factory production line. The components are specific to your business.
You need to use the Azure Custom Vision API to help detect common faults.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Step 1: Create a project
You must first set up a new project in the Custom Vision portal or via the SDK to store your data and configuration.
Step 2: Upload and tag images
Next, you need to upload photos of your specific factory components and draw bounding boxes around the faults to tag them.
Step 3: Train the object detection model
Because you need to identify and locate specific, localizable faults on a component, you must train an object detection model rather than a general classifier model.
Reference:
https://thegroundtruth.blog/tag/computervision/
NEW QUESTION # 36
You have a Microsoft Foundry project that contains an agent named PaymentAgent.
PaymentAgent includes a function tool that issues customer refunds by using an external API.
You are creating a workflow in YAML.
You need to ensure that the workflow pauses for human approval and continues with the refund step only after approval is granted.
How should you complete the workflow definition? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
type: ask_question
condition: approval == " approved "
The approval step must use type: ask_question because the workflow must pause and wait for a human response before the refund execution proceeds. Microsoft Foundry workflows support human-in-the-loop patterns where the workflow asks the user a question and awaits input before continuing; this pattern is explicitly intended for approval requests and clarifying questions. The workflow guidance also identifies workflows as declarative sequences that orchestrate agents and business logic, including branching logic and human-in-the-loop steps.
The refund execution step must use condition: approval == " approved " so that the second invocation of PaymentAgent runs only when the approval response matches the required approval value. Using true would always execute the refund, bypassing the approval control. Using propose_refund.output != null would only confirm that the first agent step produced output; it would not prove that a human approved the refund.
data_transformation is also incorrect for the approval node because it sets or parses values rather than pausing for user input. Reference topics: Microsoft Foundry workflows, human-in-the-loop workflow pattern, YAML workflow editing, agent orchestration, conditional execution, and workflow approval gates.
NEW QUESTION # 37
You have an Azure subscription that contains an Azure App Service app named App1.
You provision a Microsoft Foundry Service resource named CSAccount1.
You need to configure App1 to access CSAccount1. The solution must minimize administrative effort.
What should you use to configure App1?
Answer: A
Explanation:
To configure the App Service app to access the Microsoft Foundry Service (part of Azure AI Services) with low administration, you need to configure the app with the endpoint URI and a subscription key.
These credentials can be found in the Azure Portal under the Keys & Endpoint section of your provisioned Foundry resource, allowing the app to seamlessly authenticate API calls.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/authentication
NEW QUESTION # 38
You have a Microsoft Foundry project that uses Azure Al Search to ground an agent in internal documentation.
After a recent content update, users report that the agent ' s answers have become less accurate.
You need to identify whether the retrieved content is negatively influencing the model ' s generated responses.
Which observability signal should you review?
Answer: B
Explanation:
The correct observability signal is B. groundedness evaluation metrics . In a RAG solution, the key diagnostic question is whether the generated answer is supported by the retrieved context. Microsoft Foundry' s built-in evaluator reference defines Groundedness as the metric that measures how grounded the response is in the retrieved context, with scoring that indicates whether the model's claims are supported by the provided source material.
This matches the issue after a content update. If retrieved chunks are stale, misleading, incomplete, or poorly aligned with the user query, groundedness results can show that generated responses are not reliably supported by the retrieved documentation. The RAG evaluator guidance explains that groundedness focuses on whether the response avoids content outside the grounding context, while other process metrics such as retrieval evaluate how relevant the retrieved chunks are. Latency traces are useful for performance troubleshooting, not response accuracy. Indexer status can reveal ingestion failures, but it does not show whether retrieved content is influencing generated answers negatively. Prediction drift is a model monitoring concept and is not the primary signal for RAG grounding quality. Reference topics: Microsoft Foundry observability, RAG evaluators, groundedness, retrieved context, and response quality evaluation.
Topic 1, Case Study Contoso, Ltd
Overview
Contoso, Ltd is a multinational retail company that builds, deploys, and manages generative Al and agent- based solutions by using Microsoft Foundry.
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 Al engineering team named Agent1Dev Team to optimize and maintain existing Al solutions.
The team collaborates with solution architects, DevOps engineers, and security engineers to design, implement, monitor, and secure Al applications.
Contoso also has a team named Agent1Test Team that is responsible for validating Al 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 Al 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 Statement:
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.
Requirement:
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 Al 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.
Safety 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 Information:
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.
NEW QUESTION # 39
Hotspot Question
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.
Answer:
Explanation:
NEW QUESTION # 40
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