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| Section | Weight | Objectives |
|---|---|---|
| Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Implement agentic solutions | 20-25% | - Manage agent operations
|
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NEW QUESTION # 95
You build a chatbot that uses the Azure OpenAI GPT-4 model to generate song lyrics.
You need to ensure that responses do NOT contain lyrics from popular songs that might have been ingested during model training.
Which Azure AI Content Safety API should you use?
Answer: D
Explanation:
The Protected material text detection feature in Azure AI Content Safety is specifically designed to identify and block responses that may contain copyrighted or protected content, such as lyrics from popular songs. Since GPT-4 may have been trained on publicly available data, this feature helps ensure that the chatbot does not generate copyrighted lyrics by detecting and filtering out protected material.
NEW QUESTION # 96
You have an Azure subscription that contains an Azure Language in Foundry Tools service resource. You need to identify the URL of the REST interface for the Language service. Which blade should you use in the Azure portal?
Answer: B
Explanation:
The Keys and Endpoint blade displays the resource-specific endpoint URL used to construct REST API requests to Azure Language in Foundry Tools. It also provides the resource access keys when local key-based authentication is enabled. Microsoft's Azure Language documentation directs administrators to open the resource in the Azure portal and select Keys and Endpoint from the left menu to obtain the endpoint and credentials required for API calls.
The endpoint normally forms the base URI for the service. The application appends the operation-specific route and API version required by features such as sentiment analysis, named entity recognition, entity linking, or conversational language understanding. The key may then be supplied in the request's authentication header, although Microsoft Entra ID and managed identities are recommended for supported production workloads because they avoid embedding secrets in application code.
The Networking blade controls public access, firewalls, virtual networks, and private endpoints. Identity manages the resource's managed identity. Properties presents general resource metadata but is not the designated location for retrieving the REST API endpoint and access keys.
Study Guide alignment: plan and manage Azure AI resources, configure service access, and integrate Foundry Tools through their APIs and authentication mechanisms .
NEW QUESTION # 97
Hotspot Question
You have a Microsoft Foundry project that contains an agent.
The agent accepts user-uploaded screenshots and uses a multimodal chat model.
Some screenshots contain potentially malicious embedded text.
You need to prevent a prompt injection attack and ensure that third-party content is treated as lower trust.
How should you configure prompt shields for document attacks? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 98
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 Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content.
You need to improve response completeness.
Solution: You increase the value of the temperature parameter.
Does this meet the goal?
Answer: A
Explanation:
The solution does not meet the goal. Increasing temperature changes the sampling behavior of the generative model, not the completeness-checking logic of the application. Microsoft's Azure OpenAI reference defines temperature as a sampling control where higher values make output more random, while lower values make output more focused and deterministic. Raising the value can increase variation and creativity, but it does not ensure that all required regulatory clauses from the retrieved policy documents are included.
The reported issue is a recall/completeness failure: relevant clauses are already present in retrieved content, but the generated summary omits them. Microsoft Foundry RAG evaluator guidance defines Response Completeness as whether a response covers critical information compared to expected information or ground truth, and distinguishes it from groundedness, which checks that responses do not go beyond grounding context.
A more suitable implementation would add a reflection, verification, or completeness review pass that compares the draft summary against the retrieved clauses and revises the response before returning it.
Increasing temperature could make outputs less predictable and may worsen omission risk. Reference topics:
model parameters, temperature, RAG response completeness, retrieved context, and model reflection.
NEW QUESTION # 99
Hotspot Question
You have a Microsoft Foundry project that contains two agents named PolicyWriter and RskReviewer.
PolicyWriter generates daft updates for customer polices, and RiskReviewer reviews the drafts.
In the visual builder, you need to create a workflow that meets the following requirements:
- Finalizes low-risk updates without manual intervention
- Ensures predictable execution across the agents
- Requires user approval for highs updates
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each comet selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: The human-in-the-loop template that pauses execution of the workflow for input Human-in-the-loop template is correct. This pattern allows the system to run automatically for predictable, node-by-node agent execution while explicitly pausing the workflow when a high-risk condition is met to wait for human intervention.
Box 2: Add a Condition statement
Condition statement is correct. You must evaluate the risk level (low vs. high) before deciding whether to finalize the policy. A condition node checks the risk score output from RiskReviewer. If it is low, it routes to auto-finalization. If it is high, it routes to an approval step.
Reference:
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/building-human-in-the-loop-ai-workflows-with-microsoft-agent-framework/4460342
NEW QUESTION # 100
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