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| Section | Objectives |
|---|---|
| Topic 1: Develop Generative AI Applications and Agents | - AI agents architecture
|
| Topic 2: Knowledge Mining and Information Retrieval | - Indexing and semantic search - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns |
| Topic 3: Implement Natural Language Processing Solutions | - Text analytics and summarization - Translation and multilingual support - Language understanding and intent recognition |
| Topic 4: Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Model selection and lifecycle management - Azure AI resource provisioning and configuration |
| Topic 5: Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
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NEW QUESTION # 80
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 # 81
You have a Microsoft Foundry project that generates product marketing images from text prompts.
After publishing several images, the legal team at your company identifies a competitor's logo on a sign in the background of an image.
You need to remove only the logo, while preserving the rest of the image.
What should you do?
Answer: A
Explanation:
To remove a logo from a generated image while preserving the rest of the content, you should use AI-powered inpainting tools or standard object removal features in professional image editing software.
Microsoft Designer or Azure AI (In-Engine Fixes)
If your Foundry project is built on top of Azure OpenAI Service or Dall-E, you can often handle this programmatically or through related first-party tools.
Generative Erase / Inpainting: Use Microsoft Designer's "Erase" tool. Brush over the logo, and the AI will replace it by seamlessly blending the background.
API Inpainting: If you have access to the underlying image generation API (like DALL-E 3 or Stable Diffusion), use the Inpainting API. Pass the original image along with a black-and-white mask highlighting the logo, and prompt it to "fill the background naturally." Reference:
https://starryai.com/en/blog/mai-image-1
NEW QUESTION # 82
You are developing prompts for a Micosoft Foundry project that classifies incoming support tickets by category.
You need to improve accuracy by showing the model how correct classifications look, without retaining the model or storing knowledge permanently.
Which prompt engineering approach should you use?
Answer: B
Explanation:
Few-shot prompting is the best approach for this project. This technique improves classification accuracy by including a few high-quality examples directly inside the prompt, giving the model a clear pattern to follow without modifying its weights or storing data permanently.
Zero retraining: Works entirely through in-context learning during the API call.
No permanent storage: The knowledge disappears as soon as the inference request is completed.
Immediate accuracy boost: Demonstrates formatting, nuances, and edge cases directly to the model.
Reference:
https://www.linkedin.com/pulse/top-interview-questions-answers-prompt-engineering-nitin-sharma-ka4fc
NEW QUESTION # 83
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: B
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 # 84
You have a Microsoft Foundry project.
You need to deploy a model from the model catalog to support a search solution for internal policy documents. The model must generate vector representations of the text in the documents and of user queries.
Which type of model should you use?
Answer: D
Explanation:
To fulfill this requirement, you need an embeddings model from the Microsoft Foundry AI Model Catalog that captures semantic meaning and outputs numerical vectors for text chunks.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/openai/concepts/understand-embeddings
NEW QUESTION # 85
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