AWS Certified AI Practitioner AIF-C01 Practice Question
A manufacturing company plans to build a computer vision model that automatically detects defects on photos of finished circuit boards. Which type of data should the data science team collect for training this model?
Labeled photographs of the circuit boards (image data)
A CSV file listing component IDs and measurements (tabular data)
Sequential temperature readings from production sensors (time-series data)
Maintenance logs describing failures in plain sentences (text data)
Computer vision models learn patterns directly from visual content, so they require image data such as photographs or video frames. Text data is used for natural language tasks, time-series data suits temporal sensor readings, and tabular data represents rows and columns of structured attributes. None of these alternatives provide the pixel information needed for visual defect detection, making image data the only suitable choice.
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Why is labeled image data necessary for training computer vision models?
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What advantages do labeled photographs offer over video frames for defect detection?
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Why are labeled photographs necessary for training a computer vision model?
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What does 'labeled data' mean in the context of machine learning?
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Why can't text, time-series, or tabular data be used for computer vision tasks?
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AWS Certified AI Practitioner AIF-C01
Fundamentals of AI and ML
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