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| Section | Weight | Objectives |
|---|---|---|
| Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
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NEW QUESTION # 73
You have a Microsoft Foundry project that contains a Retrieval Augmented Generation (RAG) chat solution used by customer support agents.
You are adding an automated pre-production evaluation step to a CI/CD pipeline named Pipeline1. The evaluation will run against a labeled test dataset that contains support questions and the expected grounding context.
You need to ensure that Pipeline1 fails if unsupported content or a retrieval mismatch exceeds a defined threshold:
- responses include claims not supported by the retrieved source
content
- retrieved source content does not align with the labeled expected
context
Which two built-in evaluators should you use in Pipeline1? Each correct answer presents pat of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
The Groundedness Evaluator evaluator validates that the model's responses include only claims supported by the retrieved source content It flags ungrounded content or hallucinations. If the average score drops below your defined threshold, it triggers a pipeline failure.
The correct additional built-in evaluator appropriate for the pipeline is Retrieval (specifically, the RetrievalEvaluator or DocumentRetrievalEvaluator).
A standard RAG evaluation pipeline assesses both the generator (the LLM producing the answer) and the retriever (the search system pulling documentation). The CI/CD requirements specify two distinct failure thresholds:
Responses including claims not supported by the retrieved source content: This checks for model hallucinations and is handled by the Groundedness Evaluator.
Retrieved source content not aligning with the labeled expected context: This explicitly measures the performance of your search step against your ground-truth data. The built-in Retrieval evaluator maps to this requirement. It computes metrics like context recall to ensure your system successfully retrieves the exact reference documents specified in your labeled test dataset Reference:
https://learn.microsoft.com/en-us/microsoft-365/copilot/extensibility/evaluations-cli-evaluators
NEW QUESTION # 74
You have a Microsoft Foundry project that contains a customer support agent. The agent calls an internal knowledge API tool before generating responses.
Users report the following issues:
* Some requests take more than 15 seconds to complete.
* Some responses are incorrect, even when the knowledge API returns the expected data.
You need to inspect individual agent runs to view the ordered sequence of large language model (LLM) calls, tool invocations, and timing information.
Which observability capability should you use?
Answer: D
Explanation:
The correct capability is tracing because the requirement is to inspect the execution path of an individual agent run. Microsoft Foundry tracing captures detailed telemetry for agent behavior, including LLM calls, tool invocations, agent decision flows, inputs, outputs, tool results, token consumption, duration, and latency.
This is the appropriate observability mechanism when you need to determine which step introduced a delay, whether the agent called the internal knowledge API, what data the tool returned, and how the model used that data before producing the final response. Microsoft's Foundry observability guidance describes distributed tracing as the mechanism that provides visibility into LLM calls, tool invocations, agent decisions, and inter-service dependencies.
Token usage is useful for cost analysis and prompt optimization, but it does not show ordered run steps or tool-call sequencing. Safety metrics evaluate risk-related output behavior, not latency or tool execution.
General monitoring provides aggregate health, latency, success-rate, and dashboard views, but the question asks for per-run sequence inspection and timing breakdowns. Foundry agent tracing specifically supports debugging unexpected behavior and monitoring latency across requests. Reference topics: Microsoft Foundry observability, agent tracing, OpenTelemetry-based traces, tool invocations, LLM call inspection, and latency diagnostics.
NEW QUESTION # 75
You need to ensure that Agent1Dev Team can access Agent1. The solution must meet the security and compliance requirements.
How should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
credential = DefaultAzureCredential()
agent = project_client.agents.get(agent_name=myAgent)
The correct authentication option is DefaultAzureCredential() because the case study states that API keys must not be used to access Foundry-deployed models and that Contoso developers must authenticate to Microsoft Foundry resources by using Microsoft Entra authentication. It also states that access to Project1 must be assigned to Agent1Dev Team by using the security group SC_Agent1_Dev . Microsoft Foundry authentication guidance recommends Microsoft Entra ID for production workloads because it supports least- privilege RBAC, per-principal auditing, and keyless authentication. AzureKeyCredential() would violate the no-API-key requirement, and None would not provi de a valid credential.
The correct agent operation is get because the task is to access an existing agent named Agent1, not create a new version or retrieve a specific published version. Microsoft Foundry SDK examples show AIProjectClient created with DefaultAzureCredential() and then using project agent operations to create, retrieve, or interact with agents by name. To meet the compliance requirement, the group SC_Agent1_Dev must also be granted the appropriate project-scoped Foundry role, such as Foundry User, for Project1. Reference topics: Microsoft Entra authentication, Foundry RBAC, AIProjectClient, and project agent access.
NEW QUESTION # 76
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 AI 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 user prompts.
Does this meet the goal?
Answer: A
Explanation:
Correct:
* You configure a prompt shield for documents.
Prompt Shield for Documents: Highly Effective (Critical Defense)
How it helps: This shield specifically scans untrusted, third-party data inputs (like external documents or text extracted from uploaded images).
Mechanism: It evaluates the extracted image text before it is sent to the LLM to identify hidden jail
* You configure a prompt shield for user prompts.
Prompt Shield for User Prompts: Partially Effective (Defense in Depth)
How it helps: This shield targets direct jailbreak attempts written manually by the user in the text prompt field accompanying the upload.
Mechanism: It prevents the user from typing supporting instructions that prime the model to execute the hidden instructions found within the image.
* You configure image moderation to block unsafe content before processing the images.
Implementing rigorous image moderation is one of the most effective ways to secure multimodal AI systems against these threats. Moderation acts as a necessary gatekeeper, preventing malicious inputs from ever reaching the generative model.
Incorrect:
* You configure protected material detection.
Protected Material Detection: Ineffective for this Threat
Why it does not help: This feature is designed to scan model outputs to prevent the generation of copyrighted text, proprietary source code, or licensed imagery.
Limitation: It does not scan inputs for adversarial instructions and will not prevent a user from manipulating the model's logic.
Reference:
https://www.upgrad.com/blog/what-is-multimodal-ai/
https://learn.microsoft.com/en-us/azure/ai-services/content-safety/concepts/jailbreak-detection
NEW QUESTION # 77
You need to recommend a solution to support the planned changes and technical requirements for Agent1 to use the product information stored in storage1.
What should you include in the recommendation?
Answer: B
NEW QUESTION # 78
......
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