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| Section | Objectives |
|---|---|
| Topic 1: Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Responsible AI principles and governance - Model selection and lifecycle management |
| Topic 2: Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Translation and multilingual support - Text analytics and summarization |
| Topic 3: Knowledge Mining and Information Retrieval | - Indexing and semantic search - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns |
| Topic 4: Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Topic 5: Develop Generative AI Applications and Agents | - AI agents architecture
|
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NEW QUESTION # 146
You have a custom agent named Agent1.
You need to control access to and monitor activity for Agent1 by using Microsoft Foundry.
What should you do first?
Answer: B
NEW QUESTION # 147
You have a Microsoft Foundry project that contains an agent and an image generation model deployment.
The agent generates original images from user-supplied product photos.
You need to ensure that the generated images maintain the product identity and visual characteristics of the provided photo.
What should you do?
Answer: C
Explanation:
The correct answer is A. Set the input_fidelity parameter to high . The scenario requires the generated image to preserve the identity and visual characteristics of the user-supplied product photo. In Azure OpenAI image editing and generation workflows, input_fidelity controls how strongly the model attempts to match the style and features of the input image. Microsoft's documentation states that this parameter lets you make subtle edits without changing unrelated areas, and that high input fidelity preserves input-image features more accurately than standard mode.
Including a prompt and input image is necessary for image-guided generation, but it does not by itself maximize preservation of the product's appearance. The explicit preservation control is input_fidelity, and the requirement specifically asks to maintain product identity and visual characteristics. A groundedness detection filter applies to validating generated text against source data, not preserving visual features in image generation. Lowering temperature may reduce randomness in text generation, but it is not the image-control parameter used to retain product-specific visual details. Reference topics: Azure OpenAI image generation, image edit API, input images, input_fidelity, image-to-image generation, and visual identity preservation.
NEW QUESTION # 148
Hotspot Question
You plan to create a Microsoft Foundry project named Project1 that will contain an agent and use an Azure key vault named KV1.
You need to configure a connection from Project1 to KV1.
How should you complete the Bicep code? To answer, select the appropriate options in the answer area?
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: AzureKeyVault
To configure a connection from a Microsoft Foundry project to an Azure Key Vault, you must use the AzureKeyVault connection category.
Box 2: AccountManagedIdentity
To connect a Microsoft Foundry project to an Azure Key Vault, you must use AccountManagedIdentity as the authType.
Microsoft Foundry secures its outbound connections to core Azure storage and security infrastructure using Microsoft Entra ID role-based access control (RBAC). Using AccountManagedIdentity completely eliminates the need to handle or rotate explicit credentials or api keys within your configuration.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/how-to/set-up-key-vault-connection
NEW QUESTION # 149
You configure Azure AI Content Safety to moderate user-uploaded images in a generative application. During testing, a colleague claims an image returned a Violence severity score of 3.
Why is this result impossible?
Answer: D
Explanation:
The Azure AI Content Safety image model uses a trimmed severity scale that returns only 0, 2, 4, and 6, so an odd value such as 3 cannot occur for image content. The text model uses the full 0 to 7 scale, which is where odd-numbered severities appear.
NEW QUESTION # 150
You have a Microsoft Foundry project that contains an agent.
The agent uses Azure AI Search for Retrieval Augmented Generation (RAG).
You plan to ingest and index PDF product manuals.
You need to build a solution that supports semantic similarity matching. The solution must ensure that the agent retrieves relevant data when user questions use different wording than the product manuals.
Which indexing approach should you use?
Answer: D
Explanation:
Vector search is the correct indexing and retrieval type to use for this solution.
Vector search versus semantic ranking
While both features help bridge the gap between different wordings, vector search is specifically designed at the indexing and retrieval layer to handle semantic similarity matching by converting text into mathematical vectors (embeddings) based on conceptual meaning. Semantic ranking is a secondary re-ranking layer applied after initial retrieval to improve precision, but it cannot function as the primary indexing method on its own.
Reference:
https://www.scribd.com/document/866453317/Agentforce-Specialist
NEW QUESTION # 151
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