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| Section | Objectives |
|---|---|
| Develop Generative AI Applications and Agents | - AI agents architecture
|
| Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Responsible AI principles and governance - Model selection and lifecycle management |
| Implement Natural Language Processing Solutions | - Text analytics and summarization - Language understanding and intent recognition - Translation and multilingual support |
| Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Knowledge Mining and Information Retrieval | - Indexing and semantic search - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns |
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NEW QUESTION # 148
You plan to configure an evaluation in Microsoft Foundry for a Retrieval Augmented Generation (RAG) chat app.
You need to provide scores for groundedness, relevance, and harmful content categories.
Which two evaluation categories can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
The two evaluation categories that would be of use are AI quality (AI assisted) metrics and risk and safety metrics Risk and safety metrics: Scoring harmful content categories (such as violence, sexual content, hate speech, and self-harm) is handled directly by the specialized safety evaluators contained within this category.
AI quality (AI assisted) metrics: In Microsoft Foundry, evaluating RAG-specific criteria like groundedness and relevance requires large language models (LLMs) acting as a judge to assess semantic context. These are explicitly categorized under AI-assisted quality metrics rather than standard natural language processing (NLP) rules.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/evaluation-evaluators/rag-evaluators
https://learn.microsoft.com/en-us/azure/foundry-classic/how-to/develop/evaluate-sdk
NEW QUESTION # 149
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: A
Explanation:
The correct answer is B. Add a reflection pass before the responses are returned . A reflection pass is an application-orchestration step in which the generated summary is reviewed before final delivery, typically by asking the model or an evaluator step to check whether the answer covers the retrieved policy evidence and to revise the response when important details are missing. This directly addresses response completeness in application logic before the response is returned. The Microsoft Learn study guide explicitly includes Implement model reflection and Apply prompt engineering techniques to improve responses under optimization and operationalization of generative AI solutions.
This is also consistent with Microsoft Foundry agentic-loop guidance, which identifies reflection and planning cycles as patterns for multi-step reasoning in production agent systems. Completeness is a response-quality property: Azure AI evaluation defines completeness as whether a response contains all necessary and relevant information with respect to ground truth.
Option C is not correct because the scenario already says the agent generates summaries from retrieved policy documents, which is already a grounded retrieval pattern. Option A mainly reduces randomness, not missing content. Option D improves delivery experience, not answer completeness. Reference topics: model reflection, prompt engineering, agentic loops, response evaluation, and grounded generative AI solutions.
NEW QUESTION # 150
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: A
Explanation:
The correct answer is B. Modify the system message instructions . The case study states that Agent1 answers general questions about Contoso products and that the business requirement is for Agent1 to answer questions only about the products sold by Contoso . This is a behavioral boundary for the agent, so it should be implemented in the highest-priority instructions that define the agent's role, allowed scope, and refusal behavior.
Microsoft Foundry guidance states that a system message is used to steer model behavior, define the assistant' s role and boundaries, and add safety or quality constraints for the scenario. The system message should instruct Agent1 to answer only when the question concerns Contoso products, use the configured Contoso product documentation as grounding, and politely refuse or redirect questions about non-Contoso products.
Top-p sampling and temperature control randomness, not business-domain scope. Increasing temperature would make responses less deterministic. Few-shot examples can support desired behavior, but examples alone are weaker than explicit system-level instructions for defining operating boundaries. Reference topics:
system message design, prompt engineering, agent instructions, response constraints, and grounded generative AI behavior.
NEW QUESTION # 151
You have a Microsoft Azure AI Foundry project named Project1.
You plan to create an app named App1 that will connect to Project1 and chat by using a generative AI model.
You need to connect App1 to Project1 by using the Azure AI Foundry SDK. The solution must minimize development effort.
What should you configure in App1?
Answer: D
Explanation:
To minimize development effort when using the Azure AI Foundry SDK, you should configure the AIProjectClient object using the project connection string.
This approach is highly efficient because the connection string is a single string that encapsulates multiple required parameters--such as the subscription ID, resource group name, and project name--allowing the client to initialize and authenticate with minimal code.
How to Configure the AIProjectClient
1. Retrieve Connection String: In the Azure AI Foundry portal, go to the Overview page of your project. Under Project details, copy the Project connection string.
2. Initialize the Client: Use the from_connection_string method (or equivalent initialization depending on SDK version) to create the client.
Minimal Code Example (Python)
import os
from azure.ai.projects import AIProjectClient
from azure.identity import DefaultAzureCredential
# Load the connection string from an environment variable for security
conn_str = os.environ["PROJECT_CONNECTION_STRING"]
# Load the connection string from an environment variable for security
conn_str = os.environ["PROJECT_CONNECTION_STRING"]
Reference:
https://workshop.aifoundry.app/1-introduction/3-quick_start
NEW QUESTION # 152
You have a Microsoft Foundry project that contains a model deployment.
You have an application that calls the deployment by using the Azure OpenAl v1 API and DefaultAzureCredential.
The developers at your company receive HTTP 403 errors when they send inference requests, even after running az login.
You need to ensure that the developers can perform model inference. The solution must follow the principle of least privilege.
Which role-based access control (RBAC) role should you assign to the developers?
Answer: A
Explanation:
The correct role is Cognitive Services OpenAl User . The application is using DefaultAzureCredential, so az login only proves the developer's Microsoft Entra identity and enables token acquisition. It does not by itself grant authorization to the model deployment. Azure OpenAI and Microsoft Foundry separate authentication from authorization; Microsoft Entra ID provides token-based authentication, while Azure RBAC controls whether the signed-in principal can perform data-plane actions such as model inference. Microsoft's Foundry guidance states that Microsoft Entra ID supports granular RBAC and that data-plane operations include runtime usage such as chat completions and embedding generation.
For Azure OpenAI resources, the Cognitive Services OpenAI User role specifically allows users to make inference API calls with Microsoft Entra ID against deployed models, while preventing higher-privilege actions such as creating deployments, copying keys, fine-tuning, or managing the resource.
The other roles are not least privilege. Contributor grants broad management-plane permissions. Cognitive Services User is broader and less specific than the Azure OpenAI inference role. Cognitive Services Data Reader is read-oriented and does not provide the required model inference data action. Reference topics:
Microsoft Foundry authentication and authorization, Azure RBAC, Microsoft Entra ID keyless authentication, Azure OpenAI v1 API, and least-privilege model inference.
NEW QUESTION # 153
......
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