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| Section | Weight | Objectives |
|---|---|---|
| Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
| Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
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NEW QUESTION # 68
You need to recommend an invoice review solution that resolves the issue reported by the finance department.
What should you include in the recommendation?
Answer: C
Explanation:
The correct recommendation is Azure Content Understanding in Foundry Tools . The case study states that Contoso's finance department must manually review vendor invoices to verify that invoice details match vendor contract terms, and that the invoices contain tables, logos, and varied layouts that make consistent processing difficult. It also states that the planned solution must evaluate both the visual layout and textual content of the invoices.
Azure Content Understanding is designed for this type of multimodal document-processing workload.
Microsoft describes Content Understanding as a Foundry Tool that processes unstructured and multimodal content, including documents and images, and transforms it into structured output for AI applications. It can use document analyzers to extract text, layout, tables, fields, and relationships from diverse document types.
Chat completions alone would not reliably extract structured invoice fields from complex layouts. Azure Document Intelligence can extract OCR, layout, and tables, but Content Understanding is the better end-to- end Foundry capability for combining visual and textual understanding with structured extraction for downstream verification. Image Analysis focuses on image-level visual features and is insufficient for invoice field and table review. Reference topics: Content Understanding, document analyzers, multimodal extraction, invoice processing, tables, layout, and structured JSON output.
NEW QUESTION # 69
You are building a chatbot.
You need to ensure that the chatbot can classify user input into separate categories. The categories must be dynamic and defined at the time of inference.
Which service should you use to classify the input?
Answer: B
Explanation:
To classify user input into separate categories that are dynamic and defined at the time of inference, Azure OpenAI text classification is the best option. OpenAI models such as GPT can dynamically classify text into categories based on context and user-defined instructions. Since the categories need to be flexible and determined at inference time, OpenAI's natural language understanding capabilities are well-suited for this task.
NEW QUESTION # 70
You are creating an image-editing workflow in a Microsoft Foundry project.
The workflow must meet the following requirements:
* Ensure that background objects can be removed by applying a mask-based inpainting edit.
* Preserve the original lighting and style of the edited images.
* Use the built-in image editing controls, NOT a custom model.
You need to ensure that image edits apply exclusively inside the masked area.
How should you configure the workflow?
Answer: D
Explanation:
The correct configuration is D. Enable mask_inpainting and supply both the input image and a mask indicating which part of the image to modify . The requirement is not to generate a new image, but to edit a specific region of an existing image while preserving the surrounding lighting, composition, and style. Azure OpenAI image editing in Microsoft Foundry supports modifying existing images by submitting an input image plus a prompt. For masked edits, the mask explicitly defines the part of the image the model is allowed to change; Microsoft states that the mask parameter defines the area to edit and must match the input image dimensions.
text_to_image would create a new image from a prompt and cannot guarantee preservation of the original image. image_variation generates related variants rather than targeted removals. image_to_image with high strength can regenerate broader areas and may alter unrelated visual details. Mask-based inpainting is the built-in editing control that limits modification to the selected region. Reference topics: Azure OpenAI image editing, mask inpainting, image edit API, input image, mask parameter, and computer vision image generation workflows.
NEW QUESTION # 71
You have a Microsoft Foundry project that uses Azure Content Understanding in Foundry Tools to analyze marketing videos.
Video segmentation is enabled.
You need to configure an analyzer to output a generated JSON field that describes the color scheme of each video segment.
How should you configure the analyzer? To answer, drag the appropriate values to the correct targets. Each value 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:
* Field method: generate
* Field value type: string
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
The field should use the generate method because "describe the color scheme" asks Content Understanding to infer visual characteristics from each segment and generate a new semantic description. The desired value is not simply literal text that can be extracted from the media.
The field value type is string because a color-scheme description is textual-for example, " warm red and orange palette " or " predominantly cool blue and gray tones " .
Microsoft ' s Content Understanding video documentation explains that after segmentation, the generative- model layer can interpret scenes and populate developer-defined fields for every segment. Field schemas define a field ' s name, type, and description, and generated fields can capture semantic information derived from the source content. Microsoft Learn Therefore, the two values shown in the supplied answer image are reversed. The technically correct configuration is:
method = generate
type = string
This directly aligns with AI-103 ' s computer-vision objectives to implement visual understanding by configuring Azure Content Understanding to extract visual characteristics and process and interpret video segments . Microsoft Learn
NEW QUESTION # 72
You process vendor onboarding PDFs containing tables and hyperlinks. The extracted content is indexed and supplied to an agent in Markdown.
You need Markdown with layout and semantic structure optimized for RAG.
Answer: D
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Use prebuilt-documentSearch . Microsoft ' s current Content Understanding documentation explicitly classifies prebuilt-documentSearch as a Retrieval Augmented Generation analyzer .
RAG analyzers extract content with layout represented as Markdown and perform semantic analysis to enhance retrieval quality for downstream applications. prebuilt- documentSearch extracts document elements including paragraphs, tables, and figures and produces layout-preserving structured Markdown optimized for indexing and retrieval. Microsoft Learn prebuilt-layout is useful when the principal requirement is document-layout extraction, but the question explicitly requires both layout and semantic structure optimized for RAG . That wording maps directly to the RAG analyzer rather than the base layout analyzer.
prebuilt-read primarily addresses basic textual extraction, while prebuilt-documentFieldSchema serves field
/schema-oriented scenarios rather than producing RAG-optimized document representations.
Therefore, although the supplied file displays C (prebuilt-layout) , the verified current Microsoft Learn answer is B (prebuilt-documentSearch) . Microsoft ' s SDK documentation also describes prebuilt- documentSearch as preserving layout, detecting tables and figures, and generating structured Markdown specifically for RAG. Microsoft Learn
NEW QUESTION # 73
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