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| Section | Objectives |
|---|---|
| Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Responsible AI principles and governance - Model selection and lifecycle management |
| Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Implement Natural Language Processing Solutions | - Translation and multilingual support - Language understanding and intent recognition - Text analytics and summarization |
| Knowledge Mining and Information Retrieval | - Indexing and semantic search - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns |
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NEW QUESTION # 143
You are developing an application that will use Azure AI Search for internal documents.
You need to implement document-level filtering for Azure AI Search.
Which three actions should you include in the solution? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,D,F
Explanation:
[A]
Add allowed groups to each index entry: Each document in your search index must include a field (e.g., group_ids) listing the security groups or principals that have permission to view that document.
[E]
Retrieve the group memberships of the user: When a user submits a query, your application must query Microsoft Entra ID to fetch the list of all security groups the user belongs to.
[F]
Supply the groups as a filter for the search requests: Your application will then translate the user's group memberships into an OData filter (using the search.in() function) and append it to the search API query, restricting the results only to documents the user has access to.
Reference:
https://learn.microsoft.com/en-us/azure/search/search-security-trimming-for-azure-search
NEW QUESTION # 144
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
You need to improve response completeness. The solution must be implemented in the logic of the application code before responses are returned.
What should you do?
Answer: D
Explanation:
To enhance response completeness in your Microsoft Foundry agent, you must intercept the retrieved documents and the generated summary within your backend application logic before returning the payload to the user.
1. Implement Completeness Verification Logic
Add a verification step in your orchestration code (e.g., in your Python/Semantic Kernel or LangChain pipeline) that compares the generated summary against the retrieved chunks.' Map Key Assertions: Extract main policy rules from retrieved text.Cross-Reference Entities: Verify all key entities are in the summary.
Check Scope Coverage: Ensure every retrieved document is represented.
Scan for Gaps: Identify critical missing constraints or exceptions.
2. Apply Application-Level Mitigation Strategies
If the verification step detects that the summary is incomplete, use your code to correct it before the final response leaves your system.
Reference:
https://dev.to/moonrunnerkc/how-i-built-a-verification-layer-for-copilot-clis-multi-agent-output-4b7h
NEW QUESTION # 145
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?
Answer: C
Explanation:
The correct configuration is advanced data parsing because the issue is not merely OCR; the ingestion job must preserve document structure for reliable RAG retrieval. Microsoft guidance for advanced parsing states that it automatically detects tables across all pages, including tables in scanned documents, merges tables that span multiple pages, restores column headers, and creates table chunks with metadata such as table index, shape, page numbers, section headings, and table previews. This directly satisfies the requirement to retrieve specific rows from multipage tables while retaining source-page context.
Basic parsing with fixed-size chunking would flatten the document into arbitrary text fragments, which is the current failure mode. OCR with page-level chunking improves text extraction from scanned PDFs, but it does not provide structure-aware chunks that preserve headings and table relationships across pages. Storing each page as a single chunk is too coarse for row-level retrieval and can bury relevant table rows in excessive context. Advanced data parsing is purpose-built for RAG ingestion because it produces semantically meaningful, retrievable chunks and enriches them with metadata needed for citations and grounding.
Reference topics: RAG ingestion, advanced parsing, OCR, table extraction, structure-aware chunking, page metadata, and Azure Blob Storage document ingestion.
NEW QUESTION # 146
You are designing a solution that will answer questions about human resources (HR) policies stored in the PDF format.
You need to ensure that the identical answer to a specific question is returned every time. The solution must minimize development effort.
Which service should you include in the solution?
Answer: B
Explanation:
You can create a Custom FAQs PDF Solution Powered by Azure OpenAI with Citations from Grounded Data.
Business Problem
Imagine an organization that frequently deals with FAQs, legal documents, or reports. They want an automated system that can:
Generate answers from questions using the information found in existing PDF documents.
Ensure citations from those documents are included in the responses.
Output a structured PDF with the Q&A and citations formatted in a professional and readable way.
Solution Overview
By leveraging Azure OpenAI on your data and Azure Cognitive Search, we can ground the OpenAI GPT model with custom documents like PDFs. When queried, the model retrieves the most relevant responses from the documents and includes citations such as document titles or page numbers and create a question-and-answer format JSON file. The system then generates a structured, professional-looking PDF with bold questions, clear answers, and citations.
Reference:
https://www.linkedin.com/pulse/creating-custom-faqs-pdf-solution-powered-azure-openai-saqlain-tahir-x9gse
NEW QUESTION # 147
You have a Microsoft Foundry project that contains an agent named PaymentAgent.
PaymentAgent includes a function tool that issues customer refunds by using an external API.
You are creating a workflow in YAML.
You need to ensure that the workflow pauses for human approval and continues with the refund step only after approval is granted.
How should you complete the workflow definition? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
type: ask_question
condition: approval == " approved "
The approval step must use type: ask_question because the workflow must pause and wait for a human response before the refund execution proceeds. Microsoft Foundry workflows support human-in-the-loop patterns where the workflow asks the user a question and awaits input before continuing; this pattern is explicitly intended for approval requests and clarifying questions. The workflow guidance also identifies workflows as declarative sequences that orchestrate agents and business logic, including branching logic and human-in-the-loop steps.
The refund execution step must use condition: approval == " approved " so that the second invocation of PaymentAgent runs only when the approval response matches the required approval value. Using true would always execute the refund, bypassing the approval control. Using propose_refund.output != null would only confirm that the first agent step produced output; it would not prove that a human approved the refund.
data_transformation is also incorrect for the approval node because it sets or parses values rather than pausing for user input. Reference topics: Microsoft Foundry workflows, human-in-the-loop workflow pattern, YAML workflow editing, agent orchestration, conditional execution, and workflow approval gates.
NEW QUESTION # 148
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