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| Section | Objectives |
|---|---|
| Knowledge Mining and Information Retrieval | - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns - Indexing and semantic search |
| Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Model selection and lifecycle management - Responsible AI principles and governance |
| Develop Generative AI Applications and Agents | - AI agents architecture
|
| Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Implement Natural Language Processing Solutions | - Text analytics and summarization - Translation and multilingual support - Language understanding and intent recognition |
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NEW QUESTION # 103
You need to configure the model deployment for Agent1 to meet the technical requirements.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Deployment type: Standard
Version update policy: Once the current version expires
The correct deployment type is Standard . The case study specifies that Project1 is deployed in an EU Azure region and that model-processed data must remain within the EU. It also requires scalable, high-throughput generative AI workloads that dynamically handle variable customer support traffic without reserved throughput capacity. In Microsoft Foundry Models, Standard is a pay-per-token deployment type that processes data in a single Azure region, while Global Standard can process requests across regions and Global Provisioned uses reserved provisioned throughput. Microsoft's deployment-type guidance identifies Standard as single-region, pay-per-token, whereas Global Provisioned is cross-region with reserved capacity.
The correct version update policy is Once the current version expires . This keeps Agent1 on the selected model version during its supported lifecycle, which supports stable and consistent responses, but still preserves continuity by automatically moving to a supported replacement when the current version is retired.
Microsoft's model versioning guidance states that this policy updates only when the current model version expires, while upgrading when a new default is available changes the deployment sooner and opting out can cause the deployment to stop working after retirement. Reference topics: deployment types, regional data processing, model versioning, throughput capacity, and stable production deployments.
NEW QUESTION # 104
You have an Azure subscription.
You plan to build an app that will use the Azure AI DALL-E model.
You need to deploy the model.
What should you use?
Answer: C
Explanation:
To deploy the DALL-E model, you need Microsoft Foundry and the Azure Command Line Interface (CLI).
Deployment vs. Application Logic: The Azure CLI along with Microsoft Foundry handles the administrative infrastructure layer required to provision, configure, and manage model deployments within your subscription.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/openai/how-to/create-resource
NEW QUESTION # 105
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 # 106
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 answer customer questions about only the Contoso products.
The solution must meet the business requirements. What should you do?
Answer: C
Explanation:
Scenario: Contoso identifies the following business requirements:
Agent1 must answer questions only about the products sold by Contoso.
System Message Instructions: This dictates the agent's persona, boundaries, and rules. By explicitly configuring the system prompt (e.g., instructing the agent: "You are a product assistant.
Only answer questions using the provided product detail sheets. If you do not know the answer based on the provided documents, state that you do not know"), you prevent the model from answering with its general pre-trained knowledge.
Incorrect:
[Not B]
Few-shot examples: While useful for enforcing output formats or response tone, this does not explicitly stop a model from retrieving outside knowledge on unfamiliar topics.
[Not C, Not D]
Temperature & top-p: These parameters control the creativity and randomness of the model's responses. Decreasing them makes the model's outputs more deterministic and factual, but they do not actively restrict the model from generating information outside of the intended domain.
Reference:
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/announcing-safety-system-messages-in-azure-ai-studio-and-azure-openai-studio/4146991
NEW QUESTION # 107
A custom NER model uses one ContactInfo entity for phone numbers, email addresses, and social-media handles. Precision is low.
What should you do before retraining?
Answer: B
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
The existing ContactInfo entity combines several distinct entity patterns into one heterogeneous label. Phone numbers, email addresses, and social-media handles have substantially different syntax, contextual clues, and boundary patterns.
Splitting the broad entity into Phone , Email , and SocialMedia gives the custom NER model a clearer and more internally consistent training signal. Every existing tagged span should then be relabeled using the correct specialized entity class before retraining.
This can improve precision because the model no longer needs to learn one extremely broad decision boundary that covers unrelated forms of contact data. More precise labels also help the model learn the correct start and end boundaries of each entity, addressing the reported false positives in which adjacent text is included.
Adding more examples under the same ambiguous ContactInfo schema may reinforce the underlying modeling ambiguity. Auto-labeling reduces annotation effort but does not correct the schema problem.
Lowering a confidence threshold would generally cause more uncertain predictions to be accepted, tending to lower rather than improve precision.
Study Guide alignment: implement solutions that extract entities and customize language-model outputs for domain-specific extraction tasks . Microsoft Learn
NEW QUESTION # 108
......
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