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| Section | Objectives |
|---|---|
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Knowledge Mining and Information Retrieval | - Indexing and semantic search - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns |
| Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Implement Natural Language Processing Solutions | - Translation and multilingual support - Language understanding and intent recognition - Text analytics and summarization |
| Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Responsible AI principles and governance - Azure AI resource provisioning and configuration |
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NEW QUESTION # 138
You are building an app by using the Semantic Kernel.
You need to include complex objects in the prompt templates of the app. The solution must support objects that contain subproperties.
Which two prompt templates can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
Semantic Kernel provides support for the following template formats:
semantic-kernel - Built-in Semantic Kernel format.
handlebars - Handlebars template format.
liquid - Liquid template format
The Semantic Kernel prompt template language is a simple way to define and compose AI functions using plain text. You can use it to create natural language prompts, generate responses, extract information, invoke other prompts or perform any other task that can be expressed with text.
Reference:
https://learn.microsoft.com/en-us/semantic-kernel/concepts/prompts/prompt-template-syntax
NEW QUESTION # 139
You have a Microsoft Foundry project that processes procurement documents submitted by suppliers.
You need to implement two pipelines by using Azure Content Understanding in Foundry Tools. The solution must meet the following requirements:
* Include a pipeline named Pipeline1 that supports cost-effective, high-volume processing of standalone PDF invoices.
* Include a pipeline named Pipeline2 that supports cross-document validation by using multi-step reasoning and reference data.
How should you configure each pipeline? To answer, drag the appropriate configurations to the correct pipelines. Each configuration may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Pipeline1: Single-file task in standard mode
Pipeline2: Multi-file task in pro mode
Pipeline1 should use a single-file task in standard mode because the workload is high-volume processing of standalone PDF invoices. Azure Content Understanding standard mode is intended for individual files that require straightforward structured extraction, and Microsoft describes it as minimizing cost and latency for broad, data-centric processing scenarios. This makes it the best fit for cost-effective invoice extraction where each PDF can be processed independently.
Pipeline2 should use a multi-file task in pro mode because the requirement includes cross-document validation, multi-step reasoning, and reference data. Microsoft guidance states that pro mode is designed for advanced scenarios requiring multi-step reasoning and cross-file analysis, including processing multiple input files in a single request, validating or enriching data across documents, and using reference data to guide extraction and validation.
Single-file pro mode would add unnecessary capability for Pipeline1 and would not optimize for cost- effective high-volume standalone processing. Multi-file standard mode does not meet the pro-mode requirement for reference-data-based reasoning. Reference topics: Azure Content Understanding standard mode, pro mode, single-file tasks, multi-file tasks, field extraction, and procurement document validation.
NEW QUESTION # 140
You have a customer support agent that uses the Microsoft Foundry Agent Service.
Sometimes, customers return to a session days later to continue the same support case, and the agent must resume with the full historical context. The agent must provide the following:
- Multi-turn continuity within the session
- Cross-session continuity for the same case
- Access to the full interaction history, including user messages,
agent messages, tool calls, and tool outputs
You need to ensure that the agent automatically reloads the complete history on each new turn.
What should you do?
Answer: A
Explanation:
To achieve full, multi-turn, and cross-session continuity with complete historical context (including user messages, agent messages, tool calls, and tool outputs), you must leverage the durable Conversation lifecycle provided by the Microsoft Foundry Agent Service runtime.
Because the underlying Agent Service can be stateless at the inference tier, managing continuity across days requires linking your agent interactions to a persistent, server-side conversation ID.
Here is exactly what must be done to meet your requirements:
*-> 1. Maintain a Durable Conversation ID
Instead of creating a brand-new conversation session every time a user returns, your application must persist and map the unique support case to a Microsoft Foundry Conversation ID (typically prefixed with resp_* or conv_*) in your external database.
New Case: Create a new session and capture the generated ConversationId.
Returning Case: Retrieve the previously saved ConversationId associated with that customer's support case from your database.
2. Resume Sessions via the SDK
When the customer returns days later, initialize the agent's session by passing the existing Conversation ID into the session creation method. This forces the Microsoft Foundry Agent Service to automatically reload the complete, un-summarized thread history on the backend before processing the next turn.
Reference:
https://learn.microsoft.com/en-us/agent-framework/agents/conversations/session
NEW QUESTION # 141
You have an Azure subscription that contains an Azure OpenAI resource named AI1.
You build a chatbot that uses AI1 to provide generative answers to specific questions.
You need to ensure that questions intended to circumvent built-in safety features are blocked.
Which Azure AI Content Safety feature should you implement?
Answer: D
Explanation:
Prompt Shields in Azure AI Content Safety are specifically designed to detect and block prompts or questions that attempt to bypass safety features in generative AI models, such as those used in chatbots. This feature ensures that the chatbot adheres to safety and ethical guidelines by preventing inappropriate or harmful content generation.
NEW QUESTION # 142
You have an Azure subscription that contains an Azure AI Foundry hub named Hub1, an Azure OpenAI resource named resource1, and a user named User1.
You need to ensure that User1 can create a new Azure AI Content Understanding project in Hub1. The solution must follow the principle of least privilege.
Which role should you assign to User1?
Answer: D
Explanation:
Cognitive Services Contributor
This role is typically granted access at the resource group level for a user in conjunction with additional roles. By itself this role would allow a user to perform the following tasks.
* Create new Azure OpenAI resources within the assigned resource group.
* Etc.
To create an Azure AI Content Understanding project on an Azure AI Foundry hub, you need either the Azure AI Account Owner, Contributor, or Cognitive Services Contributor role at the subscription level to create the necessary Azure AI Foundry resource, and then the user must have the Azure AI User role at the project level to create the agent within that project.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/role-based-access-control
NEW QUESTION # 143
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