Test and Secure AI Code

Learners will demonstrate mastery by completing a Secure AI Testing Toolkit, where they will evaluate a dependency update, run integration tests, and document their findings, while developing a comprehensive testing suite with pytest that achieves at least 88% coverage. As part of this process, learners will evaluate a sample PR upgrading LangChain from version 0.1.5 to 0.1.8. Working in an off-platform Python environment, they will review changelogs for deprecated features, run security scans to identify vulnerabilities, and perform integration tests to validate compatibility. They will submit a structured report that includes an evaluation of a LangChain upgrade, a testing strategy documentation, and a reflection on the CI/CD pipeline improvements. Throughout the course, learners will engage in hands-on labs, guided coding exercises, in-video questions, interactive dialogues, and scenario-based video quizzes to apply their skills to real-world challenges. The final submission works as a personalized security and testing resource that enables learners to safeguard AI code, improve long-term reliability, and prove readiness to apply critical testing practices in professional AI development environments

• Evaluate dependency updates for security and compatibility
• Run integration tests to validate functionality
• Develop comprehensive testing strategies to enhance coverage
• Perform security scans to detect vulnerabilities
• Document findings in structured reports

• Basic familiarity with AI concepts and terminology
• Readiness to practice through applied exercises