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| Section | Weight | Objectives |
|---|---|---|
| Describe fundamental principles of machine learning on Azure | 30-35% | - Describe core machine learning concepts - Identify common machine learning tasks - Describe Azure Machine Learning capabilities - Describe features of no-code automated ML |
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify common NLP tasks - Identify Azure AI services for NLP - Describe Azure capabilities for NLP |
| Describe features of Generative AI workloads on Azure | 15-20% | - Identify responsible AI considerations for generative AI - Describe generative AI concepts - Describe Azure OpenAI Service capabilities |
| Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
| Describe features of computer vision workloads on Azure | 15-20% | - Identify Azure AI services for computer vision - Identify common computer vision tasks - Describe Azure capabilities for computer vision |
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NEW QUESTION # 124
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
NEW QUESTION # 125
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The question describes a process where an AI system generates text that describes an image - for example,
"A dog playing with a ball in the park." This process is an example of image classification, a core workload in computer vision that allows a system to recognize and categorize the content of an image.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify Azure services for computer vision," image classification involves analyzing the pixels of an image and assigning one or more predefined categories or labels to it. In more advanced implementations, image classification models are combined with caption generation algorithms to produce descriptive text. For example, Azure AI Vision can generate captions and tags that describe an image's content, such as "outdoor scene," "a person riding a bicycle," or "a group of people smiling." Let's review the other options to clarify why they are incorrect:
* Facial detection: Identifies the presence and location of human faces in an image, but does not generate descriptive text.
* Object detection: Identifies and locates multiple objects within an image by drawing bounding boxes, not by describing the overall scene.
* Optical character recognition (OCR): Extracts text from images or scanned documents (for example, reading a street sign), but it doesn't create descriptive language about what's depicted.
Therefore, the correct answer is Image classification, as it aligns with the AI-900 learning objective that describes this task as recognizing and categorizing the main content of an image, often leading to caption generation in modern vision models such as those in Azure AI Vision.
NEW QUESTION # 126
Match the principles of responsible AI to appropriate requirements.
To answer, drag the appropriate principles from the column on the left to its requirement on the right. Each principle 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:
Graphical user interface, text, application, email Description automatically generated
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
NEW QUESTION # 127
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
NEW QUESTION # 128
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/linear-regression
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize-model-clustering Regression is a form of machine learning that is used to predict a numeric label based on an item's features.
https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/introduction
NEW QUESTION # 129
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