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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe fundamental principles of machine learning on Azure | 25-30% | - Machine learning concepts
|
| Topic 2: Describe features of computer vision workloads on Azure | 15-20% | - Azure AI Vision services
|
| Topic 3: Describe features of generative AI workloads on Azure | 10-15% | - Azure OpenAI and generative services
|
| Topic 4: Describe Artificial Intelligence workloads and considerations | 20-25% | - Identify features of common AI workloads
|
| Topic 5: Describe features of natural language processing (NLP) workloads on Azure | 15-20% | - Azure NLP services
|
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NEW QUESTION # 41
Your company provides users with clear information about an AI system's purpose, functionality, and limitations. Which Microsoft responsible AI principle is this an example of?
Answer: A
Explanation:
Transparency is the Microsoft responsible AI principle that provides clear information about an AI system's purpose, how it works, and its limitations.
System Purpose: Explains what the AI is designed to do and how it helps users.
Functionality: Shows how the model makes decisions or generates content.
Limitations: Warns users about potential errors, blind spots, or times when the AI might be wrong.
User Awareness: Ensures people know they are interacting with an AI system.
Reference:
https://www.microsoft.com/en-us/ai/principles-and-approach
NEW QUESTION # 42
You are developing a web app that processes invoices to calculate expenses.
You need to extract structured fields, including nested values, from the invoices by using a defined schema.
What should you use?
Answer: C
Explanation:
The requirement is to extract structured fields, including nested values, from invoices by using a defined schema. In Azure Content Understanding, an analyzer is the processing unit that defines how content is analyzed, what information is extracted, and how the output is structured, including JSON fields.
Microsoft's Content Understanding document solutions documentation states that Content Understanding uses customizable analyzers to extract essential information, fields, and relationships from documents and forms. Microsoft's quickstart also shows invoice processing with the prebuilt- invoice analyzer to extract structured data from an invoice document.
NEW QUESTION # 43
For each of the following statements, select Yes if the statement is true. Otherwise, select NOTE Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Statement 1: Azure Content Understanding in Foundry Tools can analyze only PDF documents. = No Azure Content Understanding is not limited to PDF documents. It can analyze multiple content types, including documents, forms, images, audio, and video.
Statement 2: Azure Content Understanding in Foundry Tools results are returned in the JSON format.
= Yes
Azure Content Understanding returns structured analysis results in JSON format. This is how extracted fields, values, confidence scores, and other analysis results are represented.
Statement 3: Azure Content Understanding in Foundry Tools can extract structured fields from documents and forms. = Yes Azure Content Understanding can use analyzers and schemas to extract structured fields from documents and forms, such as invoices, receipts, contracts, and other business documents.
NEW QUESTION # 44
For each of the following statements, select if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Statement
Answer
Image analysis capabilities in Microsoft Foundry Tools can perform optical character recognition (OCR).
Yes
Image analysis capabilities in Microsoft Foundry Tools can generate captions and descriptive tags for images.
Yes
Image analysis capabilities in Microsoft Foundry Tools are designed primarily to create new images from text prompts.
No
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Fundamentals/Course Guide
/topics:
The correct sequence is Yes, Yes, No .
The first statement is Yes . Azure Vision Image Analysis in Microsoft Foundry Tools supports optical character recognition (OCR) . Microsoft documents Image Analysis 4.0 as supporting synchronous OCR through its Read capability, which extracts printed or handwritten text contained within images.
The second statement is also Yes . Image Analysis provides both captioning and image tagging . Captions generate a human-readable description of image content, while tags identify recognizable objects, scenes, actions, and other visual concepts. Microsoft explicitly lists capabilities such as Captions, Dense Captions, Tags, Object Detection, People detection, and Read/OCR as Image Analysis features.
The third statement is No . Image Analysis is primarily concerned with extracting information from existing images , not generating new images. Creating an image from a textual prompt is a generative AI/image- generation workload, typically performed by image-generation models. Image Analysis instead interprets visual input by extracting text, captions, tags, objects, and related information.
NEW QUESTION # 45
You have a Microsoft Foundry project that has a generative AI model deployment.
You need to ensure that responses generated by the model minimize costs and remain within a defined length.
Which parameter should you configure?
Answer: C
NEW QUESTION # 46
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