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| Section | Objectives |
|---|---|
| Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Knowledge Mining and Information Retrieval | - Indexing and semantic search - Azure AI Search configuration - RAG (Retrieval Augmented Generation) patterns |
| Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Translation and multilingual support - Text analytics and summarization |
| Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Plan and Manage Azure AI Solutions | - Model selection and lifecycle management - Azure AI resource provisioning and configuration - Responsible AI principles and governance |
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NEW QUESTION # 132
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 # 133
Hotspot Question
You develop a test method to verify the results retrieved from a call to the Azure Vision in Foundry Tools API. The call is used to analyze the existence of company logos in images. The call returns a collection of brands named brands.
You have the following code segment:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Yes
The code segment correctly filters for and displays the name (and coordinates) of each detected brand only if the model's confidence score is 75 percent or higher.The expression if brand.confidence >= 0.75 guarantees that only brands meeting or exceeding this threshold are printed.
Box 2: Yes
The code segment will display the coordinates. Specifically, it prints the x and y values of the rectangle's top-left corner alongside its width (w) and height (h) for any detected brand with a confidence score equal to or greater than 0.75 (75%).
The provided code uses the properties directly to extract the bounding box:
brand.rectangle.x and brand.rectangle.y: The coordinates of the top-left corner of the bounding box.
brand.rectangle.w and brand.rectangle.h: The width and height of the bounding box.
Box 3: No
See Box 2 above.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-brand-detection
NEW QUESTION # 134
You plan to build an app that will use the Azure AI DALL-E model.
You need to deploy the model.
Answer: B
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Use Microsoft Foundry and the Azure CLI . Microsoft Foundry provides the model discovery and deployment experience for supported Azure-hosted generative models, including image-generation models.
From Foundry, developers can select an available image-generation model, configure a deployment name and supported model version, and expose the model through an Azure endpoint. The Azure CLI can be used to automate the underlying resource and deployment configuration, making it appropriate for repeatable infrastructure and CI/CD workflows.
Azure Machine Learning Studio is not the primary deployment surface for Azure OpenAI DALL-E deployments. Microsoft Graph provides APIs for Microsoft 365 and identity-related workloads rather than deploying generative AI image models.
DALL-E is a text-to-image model, which means the eventual application submits a natural-language image- generation prompt and receives generated image content rather than performing classification or analysis of existing images.
The AI-103 Study Guide explicitly includes design and implementation of image-generation solutions , including generating images from prompts and reference media and selecting appropriate generation controls.
Microsoft Learn
NEW QUESTION # 135
You need to configure Agent1 to answer customer questions about only the Contoso products. The solution must meet the business requirements.
What should you do?
Answer: B
Explanation:
The correct answer is B. Modify the system message instructions . The case study states that Agent1 answers general questions about Contoso products and that a business requirement is for Agent1 to answer questions only about products sold by Contoso. This requirement defines the agent's allowed domain and refusal boundary, so it must be expressed in the agent's system-level instructions. Microsoft Foundry guidance states that system messages steer Azure OpenAI chat model behavior and are used to define the assistant's role, boundaries, output format, and safety or quality constraints.
The system message should instruct Agent1 to answer only Contoso-product questions, use Contoso product documentation when available, and decline questions about non-Contoso products. This directly enforces the intended business scope at the highest instruction level. Few-shot examples can reinforce desired behavior but are not the primary control for defining mandatory operating boundaries. Top-p sampling and temperature are decoding controls; they influence randomness and diversity, not whether the agent restricts answers to a specific product domain. Increasing temperature would likely reduce consistency. Reference topics: Microsoft Foundry agent instructions, system message design, prompt engineering, response boundaries, and grounded generative AI behavior.
NEW QUESTION # 136
You have a Microsoft Foundry project that ingests scanned PDF invoices stored in Azure Blob Storage. Each invoice contains printed fine items and has a table-based layout.
Extracted results are stored as structured JSON and used as grounding data for an agent in a Retrieval Augmented Generation (RAG) solution.
You need to create a single analyzer that meets the following requirements:
- Extracts the invoice number, invoice date, vendor name, and total
amount across varying templates
- Returns confidence scores so that results with confidence below 0.80
can be routed for supervisor review
What should you use?
Answer: D
Explanation:
The best option in this scenario is a custom Azure Content Understanding in Foundry Tools analyzer that defines the required fields as the extracted fields and the returned confidence scores for routing.
Custom Field Targeting with Confidence Scores: Azure Content Understanding allows you to build a document analyzer with a user-defined schema. By defining your required fields (InvoiceNumber, InvoiceDate, VendorName, and TotalAmount), the service will handle extraction across varying layouts and return a dedicated field-level confidence score. You can natively evaluate these confidence metrics to implement your supervisor routing workflow.
RAG-Ready Output Structure: The service naturally returns highly structured JSON payloads.
This makes it perfectly optimized to be ingested directly as grounding data for a Retrieval- Augmented Generation (RAG) agent.
Incorrect:
[Not C]
The prebuilt-layout analyzer extracts raw structural elements such as blocks of text, hierarchy, selection marks, and complete tables. It does not automatically classify or cleanly isolate specific target entities (like vendor name or total amount) into dedicated schema properties, leaving you with heavy post-processing work to isolate the text.
[Not D]
The prebuilt-documentSearch analyzer is optimized for broad, layout-aware text extraction for indexing. Relying on Azure AI Search's search.score for supervisor routing is conceptually flawed; search.score represents a relevance score for a search query ranking rather than an accuracy metric for data extraction confidence.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/document/analyzer-improvement
NEW QUESTION # 137
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