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| Section | Objectives |
|---|---|
| Topic 1: Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
| Topic 2: Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Azure AI resource provisioning and configuration - Responsible AI principles and governance |
| Topic 3: Develop Generative AI Applications and Agents | - AI agents architecture
|
| Topic 4: Knowledge Mining and Information Retrieval | - Indexing and semantic search - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns |
| Topic 5: Implement Natural Language Processing Solutions | - Text analytics and summarization - Translation and multilingual support - Language understanding and intent recognition |
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NEW QUESTION # 124
You are designing a content management system.
You need to ensure that the reading experience is optimized for users who have reduced comprehension and learning differences, such as dyslexia. The solution must minimize development effort.
Which Azure service should you include in the solution?
Answer: A
Explanation:
Include Azure AI Immersive Reader in your solution. It is an applied AI service that provides built- in accessibility features like read-aloud (text-to-speech), real-time translation, line focusing, and customizable typography. It requires no machine learning expertise and integrates easily to minimize development effort.
Reference:
https://azure.microsoft.com/en-us/products/ai-services/ai-immersive-reader
NEW QUESTION # 125
You have an Azure AI Search indexer that ingests PDF policy manuals.
Client applications must display page-level citations that have bounding polygons for both text and images.
You need to add a single built-in multimodal content extraction skill to the Azure AI Search skillset. The solution must meet the following requirements:
- Provide text and image location metadata.
- Extract tables that span multiple pages.
What should you add?
Answer: C
Explanation:
To meet all requirements for this project, you need the Azure Content Understanding skill (integrated via Azure AI Foundry / Microsoft Foundry Tools).
1. Extracting Cross-Page Tables: A critical constraint is the ability to recognize and extract tables that span multiple pages as a single unit. The Azure Content Understanding skill natively supports this capability, whereas the older Document Layout skill outputs layout content as flattened text/markdown boundaries that break across pages, leading to information loss.
2. Page-Level Bounding Polygons: It functions as a layout-aware, built-in multimodal content extraction skill that maps out text and image location metadata (location_metadata), satisfying your client application requirement for strict page-level citation boundaries.
Reference:
https://learn.microsoft.com/en-us/azure/search/cognitive-search-skill-content-understanding
NEW QUESTION # 126
You need to create a new resource That will be used to perform sentiment analysis and optical character recognition (OCR}. The solution must meet the following requirements:
* Use a single key and endpoint to access multiple services.
* Consolidate billing for future services that you might use.
* Support the use of Azure Vision in Foundry Tools in the future.
How should you complete the HTTP request to create the new resource? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Azure Resource Manager uses an HTTP PUT request to create or update a resource at a specified resource ID. Therefore, the first dropdown must be PUT.
The resource must use the CognitiveServices kind because the requirements describe a multi-service Azure AI resource rather than an individual service-specific resource.
A TextAnalytics-specific kind would provide Language functionality but would not satisfy the requirement for a single resource covering OCR and future Vision use. ComputerVision would similarly focus on Vision rather than consolidating multiple Azure AI service capabilities.
The multi-service resource allows applications to access supported Azure AI services while centralizing resource administration and billing. A single Azure AI services resource can therefore serve as the account boundary for capabilities such as Language sentiment analysis and Vision OCR.
The request should target the Microsoft.CognitiveServices/accounts resource type and supply the selected kind and SKU in the body. Identity configuration may additionally be applied depending on the security architecture.
The AI-103 Study Guide requires candidates to design Azure infrastructure for AI apps and choose appropriate Foundry services as part of planning and managing an Azure AI solution. Microsoft Learn
NEW QUESTION # 127
You have a Microsoft Foundry project that contains three agents as shown in the following table.
Name
Description
TriageAgent
Classifies incoming customer requests
PolicyAgent
Answers policy questions by searching internal content
ActionAgent
Creates or updates tickets by calling an HTTP API
You need to orchestrate the agents to ensure that the customer requests meet the following requirements:
* Support a deterministic, step-based process that uses conditional branching and shared state across the agents.
* Optionally trigger a ticket action based on the triage result.
The solution must minimize development effort.
What should you include in the solution?
Answer: D
Explanation:
The correct answer is a workflow . Microsoft Foundry workflows are designed to orchestrate agents and business logic as declarative, predefined sequences of actions. The official workflow guidance states that workflows are ideal when you need to orchestrate multiple agents in a repeatable process, add branching logic such as if/else, and handle variables without writing application orchestration code. This directly matches the requirement for a deterministic, step-based process with conditional branching and shared state.
In this scenario, TriageAgent can classify the request first, the workflow can store the triage result, and conditional logic can determine whether to invoke PolicyAgent, ActionAgent, or both. The ticket action is optional, so it should be triggered through a workflow condition based on the triage output. This minimizes development effort because the branching, sequencing, and variable handling are managed in the Foundry workflow rather than being manually implemented across separate runs in application code.
A group chat session is better for dynamic agent handoff, not a strict deterministic process. Threads and runs or separate app-coordinated calls require more custom orchestration. Reference topics: Microsoft Foundry workflows, multi-agent orchestration, conditional branching, variable handling, and agent-driven workflows.
NEW QUESTION # 128
You are building an Azure AI Search indexing pipeline named Pipeline1 that ingests invoices stored in Azure Blob Storage. The invoices are stored as scanned images.
You need to enable users to search invoice data across the invoice fields.
Which built-in skill should you add to the skillset of Pipeline1?
Answer: A
Explanation:
You should add the Azure Machine Learning OCR skill (historically known as the Microsoft.Skills.Vision.OcrSkill) to your skillset pipeline.
Text Extraction: Scanned images contain unsearchable pixels rather than text.
Data Ingestion: The OCR skill extracts the printed text and layout information from the images.
Downstream Processing: It feeds raw text into subsequent skills (like Entity Recognition) or directly into your search index fields.
Reference:
https://techcommunity.microsoft.com/blog/azurearchitectureblog/from-large-semi-structured-docs-to-actionable-data-reusable-pipelines-with-adi-a/4474054
NEW QUESTION # 129
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