Computer Vision Flashcards
6 cards from real Microsoft Azure AI Fundamentals practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 Computer Vision flashcards as text
Which iteration-based workflow does Azure Custom Vision use to improve model accuracy?
Answer: Active learning loop: label → train → evaluate → retrain with corrected images
Azure Custom Vision follows an iterative cycle where you label images, train, evaluate performance, correct errors, and retrain to progressively improve accuracy.
What does Azure Face service provide that goes beyond basic face detection?
Answer: Verifying whether two faces belong to the same person and detecting facial attributes
Azure Face service can verify face similarity, detect facial attributes like age and emotion (with responsible AI constraints), and find similar faces.
Which metric is commonly used to evaluate object detection model performance in Azure Custom Vision?
Answer: Mean Average Precision (mAP)
Mean Average Precision (mAP) measures how accurately an object detection model identifies and localizes objects across all classes.
What is the 'Read API' in Azure AI Vision used for?
Answer: Extracting printed and handwritten text from images and PDFs at scale
The Read API in Azure AI Vision is an OCR engine optimized for extracting large amounts of printed and handwritten text from images and multi-page PDFs.
In Azure Custom Vision, what does the 'Precision' metric indicate?
Answer: The percentage of predictions the model made that were actually correct
Precision measures how many of the model's positive predictions were actually correct, indicating how trustworthy the model's positive results are.
Which Azure AI Vision feature can generate a dense description of all regions within an image?
Answer: Dense captioning
Dense captioning generates individual natural language captions for multiple regions across an image, providing a richer description than a single overall caption.