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| Section | Weight | Objectives |
|---|---|---|
| Implement agentic solutions | 20-25% | - Build AI agents
|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
| Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
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NEW QUESTION # 58
You have a Microsoft Foundry project that uses Azure AI 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: D
Explanation:
The most interesting observability signal in this scenario is Retrieval Score vs. Response Quality (Groundedness Evaluation), specifically analyzing the Context Relevance metric alongside Retrieval Confidence Scores.
Because your LLM agent uses a Retrieval-Augmented Generation (RAG) pattern via Azure AI Search, a sudden drop in accuracy after a content update usually implies that the search index is either surface-matching bad keywords, or the ranking algorithm is pulling technically relevant but contextually wrong documents.
Reference:
https://blog.gopenai.com/how-to-prevent-context-loss-in-azure-ai-search-rag-pipelines-497c346c7077
NEW QUESTION # 59
You have a Microsoft Foundry project that contains an internal Q & A agent.
Users report the following issues when they ask the agent questions:
* An increase in the following response: "No relevant information found"
* Periodic HTTP 429 rate limit exceeded errors during peak hours
You need to identify whether each issue is caused by model unavailability, resource limits, or inference failures.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Metrics to enable: Model Availability Rate and Provisioned Utilization
Diagnostic log to collect: trace
The correct metrics are Model Availability Rate and Provisioned Utilization . Model Availability Rate identifies whether failures are caused by service-side model unavailability because it is calculated from total calls minus server errors. Provisioned Utilization identifies resource-limit conditions because Microsoft states that when utilization reaches or exceeds 100%, calls are throttled and HTTP 429 errors are returned. This directly maps to the reported peak-hour rate-limit errors.
The correct diagnostic log is trace . For an internal Q & A scenario, trace logging is required to analyze question-answering behavior, including unanswered questions. Microsoft's custom question answering analytics guidance states that diagnostic logging stores telemetry and chat logs and instructs enabling Trace in addition to Audit, RequestResponse, and AllMetrics. The sample Kusto queries for unanswered questions inspect the answer, question, score, and knowledge base ID, and filter unanswered results by a score of zero.
RequestResponse is useful for request status and latency, and Audit is for administrative operations, but neither is the best diagnostic category for analyzing Q & A inference behavior such as "No relevant information found." Reference topics: Foundry monitoring, model availability, provisioned utilization, diagnostic logging, custom question answering analytics, and trace logs.
NEW QUESTION # 60
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 personalized user interactions for Agent1. The solution must meet the business requirements.
What should you include in the solution?
Answer: A
Explanation:
Scenario, 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.
To configure personalized user interactions that retain context and recall previous information, you should use the Memory in Foundry Agent Service.
Managed Long-Term Memory: This allows the agent to extract meaningful information from user conversations and store it as durable knowledge (e.g., user preferences or historical details) to be recalled across future interactions.
Scoped Memory Stores: By using the scope parameter when creating memory stores, you can segment memories to ensure a secure, personalized experience for distinct users.
Automatic Injection: The agent will automatically inject these learned facts and user profile details into the prompt before new interactions, eliminating the need to rehydrate context manually.
References:
https://learn.microsoft.com/en-us/azure/foundry/agents/concepts/what-is-memory
NEW QUESTION # 61
Hotspot Question
You have a Microsoft Founcy project that contains a Retrieval Augmented Generation (RAG) solution.
You need to run a pre-production evaluation by using labeled CSV dataset that contains the query, context, response and ground truth. The evaluation must measure the following:
- Whether responses address the user query
- Whether responses are supported by the provided context
- Whether responses contain sensitive or proprietary information
Which AI quality evaluation metrics should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Groundedness and Relevance
The two best AI quality evaluation metrics for your pre-production evaluation in Microsoft Foundry are Relevance and Groundedness.
Microsoft Foundry provides built-in, AI-assisted quality evaluators specifically designed to measure these two critical dimensions of a Retrieval-Augmented Generation (RAG) system:
Relevance: This metric directly addresses your first requirement: whether responses address the user query. It evaluates how well the gens.nerated response aligns with and answers the initial user query.
Groundedness: This metric directly addresses your second requirement: whether responses are supported by the provided context. It evaluates the degree to which the generated response is factually supported by and derived strictly from the retrieved context, which helps capture model hallucinatio Box 2: Protected material The best AI quality evaluation metric for this scenario is Protected material.
Direct alignment: The Protected material metric specifically checks whether an AI model outputs copyrighted text, proprietary data, or sensitive information that matches its training data or internal context.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/concepts/observability
https://learn.microsoft.com/en-us/azure/foundry/how-to/develop/langchain-middleware
NEW QUESTION # 62
A copilot must answer multi-part questions that depend on earlier turns in the conversation. You want the system to break each complex question into focused subqueries, run them in parallel, and semantically rerank the results before composing an answer. Which Azure AI Search capability provides this?
Answer: D
Explanation:
Agentic retrieval in Azure AI Search uses a large language model to decompose a complex query into focused subqueries, runs them in parallel, semantically reranks each set of results, and merges them into a unified response, while taking conversation history into account. That is precisely the behaviour the scenario describes.
NEW QUESTION # 63
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