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| Section | Weight | Objectives |
|---|---|---|
| Implement agentic solutions | 20-25% | - Manage agent operations
|
| Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
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46. Frage
You are deploying a support agent that enables users to upload photos.
You need to automatically classify uploaded images for harmful content. The solution must block content based on severity levels.
What should you do?
Antwort: D
Begründung:
The correct answer is A. Implement image moderation . Azure AI Content Safety provides image analysis that classifies uploaded images for harmful content, including harm categories such as hate, sexual content, violence, and self-harm. Microsoft's Content Safety overview states that the Analyze Image API scans images for harmful content with multi-severity levels, which directly matches the requirement to automatically classify uploaded photos and block content based on configured severity thresholds.
Prompt Shields are intended to detect prompt injection and jailbreak-style attacks against generative models, not to classify image harm categories. Keyword scanning OCR output would only detect visible text extracted from the image and would miss visual harm in the image itself. Blocklists can help match known words or custom patterns, but they are not a complete image safety classifier and do not provide the built-in severity- based image harm classification required here. Image moderation is therefore the correct control for user- uploaded photos. Reference topics: Azure AI Content Safety, image moderation, harm categories, severity levels, Foundry guardrails, and responsible AI controls.
47. Frage
You have a Microsoft Foundry project that contains two agents named PolicyWriter and RiskReviewer.
PolicyWriter generates draft updates for customer policies, 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
Antwort:
Begründung:
Explanation:
* Orchestration pattern: The sequential template that passes outputs node-by-node
* Approval checkpoints: Add a condition statement
Use the sequential orchestration template because the agents must execute in a fixed, predictable order.
PolicyWriter first generates the proposed policy update, and its output is then passed directly to RiskReviewer. Microsoft defines the sequential pattern as passing the result from one agent to the next in a defined order, making it appropriate for deterministic, multistage processing. The group-chat pattern would allow control to move dynamically between agents, which would reduce execution predictability.
After RiskReviewer returns a structured risk classification, add a condition statement that evaluates the result. The low-risk branch can proceed directly to the finalization action without requesting human input. A separate higher-risk branch can route to an approval step, such as an Ask a question node. Adding an Ask a question node without conditional branching would pause every workflow execution, including low-risk updates, and therefore would not satisfy the automation requirement. Microsoft Foundry workflows support if
/else branching and condition expressions for selecting the next action.
Study Guide alignment: implement orchestrated multi-agent solutions and build autonomous or semiautonomous workflows with safeguards and approval-flow controls .
48. Frage
You have a Microsoft Foundry project that contains an agent.
You need to process mixed-format documents that contain scanned text, tables, and multicolumn layouts. The extracted content must preserve the document structure and be converted into the Markdown format for downstream reasoning.
What should you configure first?
Antwort: A
Begründung:
The first step should be an Azure Content Understanding analyzer within Microsoft Foundry Tools.
This specific tool is designed to handle the complexities in this scenario--scanned text, complex tables, and multi-column layouts--by converting unstructured documents into a structured Markdown format. This preserves the document's original hierarchy and structural relationships, which is critical for accurate downstream reasoning by an agent.
Incorrect:
[Not A]
Azure Language is designed for text-based natural language processing (NLP) like sentiment analysis or PII redaction on raw text strings. It cannot parse scanned images, optical character recognition (OCR), tables, or complex multi-column visual layouts.
[Not C]
The first step in this specific Microsoft Foundry agent configuration should not be an Azure OpenAI Responses API call.While multimodal models can read images, they frequently hallucinate data, fail to capture complex multi-column reading orders, and degrade table structures when processing dense, mixed-format documents.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/document/elements
49. Frage
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?
Antwort: A
Begründung:
The correct approach is to create and reuse a conversation by storing the conversation's ID and supplying that ID on subsequent requests . In Microsoft Foundry Agent Service, conversations are durable objects with unique identifiers that can be reused across sessions. The official runtime guidance states that conversations store items, including messages, tool calls, tool outputs, and other data, and are intended for multi-turn continuity, cross-session continuity, and inspection of what happened over time. This directly satisfies the requirement to resume the same support case days later with the full historical context.
Persisting only the final model response is insufficient because it loses the full interaction chain, especially tool calls and tool outputs that may be essential to case state. Memory summarization is also not the best fit because the requirement asks for the complete history, not a compressed representation that may omit details.
Reusing the conversation ID allows Foundry to maintain the conversation server-side so the next turn can reuse prior context without the client manually rebuilding prompts. Reference topics: Foundry Agent Service runtime components, conversations, conversation items, multi-turn continuity, cross-session continuity, and tool output history.
50. Frage
You have a Microsoft Foundry project that contains an agent.
The agent uses a knowledge source built from documents stored in Azure Blob Storage. The documents include digitally scanned PDFs that contain multipage tables.
You have an ingestion job that extracts only plain text, causing loss of table structure, headings, and page-number metadata.
Users frequently ask questions that require the retrieval of specific table rows across the pages.
You need to configure an ingestion job for a Retrieval Augmented Generation (RAG) pipeline that performs optical character recognition (OCR) on scanned PDFs, preserves tables and headings as structure-aware chunks, and stores page-number metadata with each chunk.
How should you configure the ingestion job?
Antwort: C
Begründung:
Use OCR and page-level chunking.
Structure Preservation: OCR combined with document layout analysis (such as layout-aware or page-level chunking) detects visual structures like tables, headers, and reading order.
Row-Level Granularity: Page-level chunking retains the boundaries and context of multipage tables, enabling the RAG system to isolate and retrieve specific rows effectively.
Metadata Enrichment: Processing documents page-by-page allows the ingestion pipeline to automatically tag each extracted text chunk with its source page-number metadata.
Incorrect:
[not C]
Use page-level OCR extraction and store each page as a single chunk:
While this captures page numbers, storing an entire page as a single chunk introduces too much noise. It prevents the model from pinpointing specific table rows and often exceeds the optimal context window size for precise RAG retrieval.
[Not D]
Use basic parsing and fixed-size chunking:
Basic parsing cannot read digitally scanned PDFs, resulting in empty or completely garbled text.
Furthermore, fixed-size chunking splits text at arbitrary character limits, which destroys table structures and splits individual rows across different chunks.
Reference:
https://pub.towardsai.net/unlocking-key-technologies-in-document-parsing-81bfe20d741b
51. Frage
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