How is the CAIP exam structured?
The CAIP exam is domain-based, covering AI fundamentals, data analysis, ML, deep learning and NLP, computer vision and robotics, plus AI risk, privacy, compliance, ethics, governance, and strategy.
The exam is organized around competence domains that reflect end-to-end AI capability. It starts with fundamental AI concepts and principles, then evaluates data analysis and visualization knowledge. It continues into building machine learning models and understanding deep learning and NLP concepts.
It also covers the knowledge and application of computer vision, robotics, and expert systems, and finishes with domains focused on AI risk, privacy and compliance, and AI ethics, governance, and strategy. The stated exam delivery is online with a three-hour duration.
Related Information
- Domains include AI fundamentals and applications.
- Data analysis and visualization are explicitly assessed.
- ML, deep learning, and NLP domains test modeling understanding.
- Computer vision, robotics, and expert systems are included.
- Risk, privacy, compliance, ethics, governance, and strategy are assessed.
Expert Insight
To prepare effectively, map each domain to artifacts you'd produce in a real project: data profile and visuals, model evaluation notes, deployment considerations, and a risk/ethics checklist. That makes domain questions easier and more consistent.
“CAIP tests both technical and responsible AI competence.”
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What does a Certified Artificial Intelligence Professional do in practice?
A CAIP professional designs and deploys AI solutions, validates models with data, and manages risk, ethics, privacy, and governance so AI delivers value responsibly.
byAlexis HIRSCHHORN
What topics are covered across the four course days?
Day 1 covers AI fundamentals and data analysis; Day 2 focuses on machine learning; Day 3 covers deep learning and NLP; Day 4 covers computer vision, robotics, and responsible AI strategy, governance, and risk.
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How do machine learning, deep learning, and NLP differ in practice?
Machine learning learns patterns from data. Deep learning uses neural networks for complex representations. NLP applies these techniques to language understanding and generation.
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