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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Topic 2: Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Topic 3: Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Topic 4: Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Topic 5: Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
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NEW QUESTION # 62
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 # 63
You have an Azure subscription that contains an Azure Al Foundry instance named AI1.
You have an app that automatically triages and resolves issues presented in the log files of a system.
You create an incident manager agent and a DevOps agent that collaborate to resolve the issues.
You need to ensure that the incident manager agent can assign work to the DevOps agent. The solution must minimize development effort.
What should you do?
Answer: B
Explanation:
Ensuring that an incident manager agent can assign work to a DevOps agent in Azure AI Foundry is primarily achieved through a Connected Agents configuration. This setup allows a "main" agent to delegate tasks to "specialized" agents via natural language or defined function calls.
Key Implementation Steps
1. Configure Connected Agents
Within the Azure AI Foundry portal, you must register the DevOps agent as a "Connected Agent" of the incident manager.
2. Define Tool-Based Delegation
Use the ConnectedAgentToolDefinition in the Azure Python SDK or C# SDK to programmatically link them. This exposes the DevOps agent to the incident manager as a callable "tool".
3. Implement Handoff Orchestration Patterns
Choose an orchestration pattern that fits the triage-to-resolve workflow.
4. Manage Context and State
5. Enable Permissions and Roles
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/connected-agents
NEW QUESTION # 64
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 Al 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 protected material detection.
Does this meet the goal?
Answer: A
Explanation:
The solution does not meet the goal. Protected material detection is intended to identify large language model output that matches known protected text or code, such as copyrighted text, selected web content, song lyrics, articles, recipes, or code. Microsoft describes protected material detection as a control for preventing AI- generated content from reproducing known protected material, not as a control for image safety or prompt injection.
The stated risk has two parts: users can upload unsafe images, and users can embed hidden instructions in images to manipulate the model. Unsafe image uploads require image moderation, because Azure AI Content Safety provides image APIs that detect harmful content across modalities and can support blocking decisions by harm category and severity. Hidden instructions extracted from images are indirect prompt injection or document attacks; Microsoft Prompt Shields are the capability designed to detect user prompt attacks and document attacks, including harmful instructions embedded in third-party content.
Therefore, protected material detection alone does not mitigate either primary risk. Reference topics: Azure AI Content Safety, image moderation, Prompt Shields, document attacks, indirect prompt injection, and protected material detection.
NEW QUESTION # 65
You need to configure Agent1 to meet the security and compliance requirements.
What should you use?
Answer: B
NEW QUESTION # 66
You have a Microsoft Foundry project that contains an agent.
The knowledge source for the agent is a set of scanned PDF troubleshooting guides stored in Azure Blob Storage. The guide pages contain two-column layouts and tables.
You use Azure Content Understanding in Foundry Tools to process the PDFs.
You plan to ingest the processed content into an index for Retrieval Augmented Generation (RAG) and store extracted fields for downstream automation.
Stakeholders must be able to verify where each extracted field value came from in the original PDF and route low-reliability extractions for manual review.
You need to ensure that the Content Understanding document analyzer output includes a per- field confidence score and source grounding to locations within the source document.
What should you do?
Answer: C
Explanation:
To fulfill all your requirements using Azure Content Understanding in Foundry Tools, you need to configure a custom document analyzer with specific flags, set up an index ingestion pipeline, and build a downstream human-in-the-loop validation rule.
*-> 1. Enable Confidence Scores and Source Grounding
To force the analyzer to provide per-field confidence metrics and precise layout/bounding box coordinates for verification, you must opt-in to the estimate FieldSourceAndConfidence parameter within your configuration.
Option A (Global): Set estimateFieldSourceAndConfidence = true in the main analyzer config to evaluate all fields.
Option B (Field-Level): Set estimateSourceAndConfidence = true under individual field schemas.This ensures the generated JSON response populates the bounding box coordinates, page numbers, and a confidence score 0.0 to 1.0 for every extracted entity.
2. Configure Document Extraction for Two-Column & Table Layouts
3. Build the Ingestion Pipeline (RAG vs. Automation Dual-Path)
4. Implement Threshold Routing and Source Verification
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/document/overview
NEW QUESTION # 67
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