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How to Build Better Technical Interview Questions

Published by Karat

The Karat guide “How to Build Better Technical Interview Questions” provides a scientific approach to designing interviews that are predictive, fair, and enjoyable. It emphasizes using Job Task Analysis (JTA) to define success in a role, focusing on objective, skill-based questions aligned with Bloom’s Taxonomy for deeper insight. The guide advocates for structured, consistent interviews with clear rubrics to minimize bias and avoid creating monocultures. It also highlights the importance of candidate experience, ensuring interviews reflect company values and promote inclusivity. Metrics like passthrough rates and job performance help validate and refine the process.

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Related Categories Machine Learning, Artificial Intelligence, Deep Learning, Machine Learning Models, AI in Business, AI-powered Analytics, AI in Healthcare, Metaverse, Unsupervised Learning, Reinforcement Learning, Sentiment Analysis, Text Mining, Speech Recognition

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