AI Leadership Fundamentals Certification

Most organisations now have access to the same AI tools, so the tools are no longer the differentiator. What separates organisations that get value from AI is the quality of the decisions around it — which use cases to fund, how to govern them, whether a model's reasoning can be explained to a regulator or a customer, and where the risks sit. With the EU AI Act and comparable regulation now in force, those decisions carry legal weight as well as commercial weight.

DataCamp Certification is included with your premium subscription, so there are no additional costs. When you subscribe you will have access to learning materials to prepare for your certification, practice assessments and exams and the certification itself.

Yes. Your subscription includes the full course library, practice assessments and curated tracks. The Artificial Intelligence (AI) Leadership track is the direct preparation route for this certification.

For DataCamp Certifications you can take the exams whenever you want. Just like the rest of the DataCamp platform, we have designed the certification process so that you can fit it into your schedule. Once you start the exam, you will have 45 minutes to complete it, and you will have 30 days overall to complete all of the requirements. Otherwise, the process is designed so that everyone has the opportunity to get certified.

Our Fundamentals Certifications do not expire. As they are designed to show proficiency of fundamental knowledge that is unlikely to change over time.

The exam covers four areas:

• AI strategy and value — monetising data assets, building a product-first AI strategy, assessing maturity with established models, and justifying investment through ROI
• Responsible AI and governance — Responsible AI principles, risks across the AI lifecycle, the regulatory landscape including the EU AI Act, and putting a governance framework into practice
• Explainability and transparency — why interpretability matters, the limits of black box models, techniques including SHAP and LIME, explainability for large language models, and designing for explainability from the start
• AI risk and security — AI-specific risks, the AI security ecosystem, model-level risks, external attacks, secure development practices, and structured risk assessment

The Artificial Intelligence (AI) Leadership track covers all four in four courses, six hours total.