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AI-900 Microsoft Azure AI Fundamentals Exam Questions and Answers – Page 1

The latest Microsoft AI-900 Azure AI Fundamentals certification actual real practice exam question and answer (Q&A) dumps are available free, which are helpful for you to pass the Microsoft AI-900 Azure AI Fundamentals exam and earn Microsoft AI-900 Azure AI Fundamentals certification.

AI-900 Microsoft Azure Fundamentals Exam Questions and Answers

Exam Question 81

Anomaly Detection

Correct Answer:
ML based technique that analyzes data over time and identifies unusual changes. Azure has an Anomaly Detector service

Exam Question 82

Computer Vision

Correct Answer:
enables software engineers to create intelligent solutions that extract information from images; a common task in many artificial intelligence (AI) scenarios

Exam Question 83

Computer Vision models

Correct Answer:
Image classification, Object detection, Semantic segmentation, Image analysis, Face detection, analysis and recognition, Optical character recognition

Exam Question 84

Image classification

Correct Answer:
trains ML model to classify images based on their contents. Ex. classification can be used in a traffic monitoring solution to classify images based on the type of vehicle they contain.

Exam Question 85

What service do you use for image classification?

Correct Answer:
Custom Vision

Exam Question 86

What resources can be used for custom vision?

Correct Answer:
You can use either custom vision or cognitive services

Exam Question 87

How are custom vision models evaluated?

Correct Answer:
With precision, recall and AP

Exam Question 88


Correct Answer:
Percentage of the class prediction made by the model that were correct. For example if the model predicted that 10 images are oranges, of which 8 were actually oranges, then recall is 80%

Exam Question 89


Correct Answer:
percentage of class prediction that the model correctly identified. Ex. if there are 10 images of apples and the model found 7, then precision is 70%

Exam Question 90


Correct Answer:
Average precision, an overall metric that takes into account both precision and recall

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