Resources
Find out what your next role asks for, and how far you are from it.
Learning paths with every topic mapped, and practice exams in the format of the real test. All open to read. When you want to know where you stand, a diagnostic measures your level.
Where to start
Learning pathsThe full topic map of each technology and area, from basics to advanced.Browse learning paths →Practice examsTimed practice exams for certifications, with per-domain scoring.Browse practice exams →System Design guidesReal-world architecture cases, papers and core concepts, explained step by step.Read the guides →ProfessionsWhat each level of a profession must prove, and why.Browse professions →
Most studied paths
- AI Accountability for DevelopersAssessment covering developer accountability when using AI coding tools. Explores who is responsible when AI-generated code ships and fails, mandatory oversight, bias and silent failure patterns, audit trails, and organizational AI governance.
- AI Agent BuilderComprehensive assessment of AI agent engineering proficiency covering LLM interaction and provider APIs, agent architecture and design patterns, RAG and knowledge systems, multi-agent orchestration, agent safety and evaluation, production agent systems, and advanced agent engineering.
- AI Application DevelopmentComprehensive assessment of AI application development skills covering LLM fundamentals, RAG systems, agent frameworks, evaluation and safety, production deployment, and advanced AI engineering including fine-tuning and RLHF.
- AI Liability for DevelopersAssessment covering the legal liability landscape for developers who use AI tools. Explores EU AI Act, US AI policy, product liability for AI-generated code, GDPR/CCPA implications, high-risk AI system requirements, and incident response legal obligations.
- AI Tools Power UserComprehensive assessment of AI tool proficiency covering fundamentals and model literacy, conversational AI mastery, AI-assisted content creation, AI-powered coding and development, workflow automation and integration, professional AI strategy and ethics, and advanced power techniques.
- AI for Customer SuccessAssesses the ability to leverage AI tools and techniques across the customer success lifecycle — from AI-powered onboarding automation and customer health scoring to churn prediction, engagement intelligence, workflow automation, and strategic CS leadership with AI.
- AI for EngineeringAssesses the ability to leverage AI tools and techniques to boost software engineering productivity — from AI-powered code generation and review to automated testing, intelligent debugging, architecture optimization, and strategic AI adoption across engineering teams.
- AI for FP&AAssesses the ability to leverage AI tools and techniques to transform Financial Planning & Analysis — from AI-powered revenue forecasting and anomaly detection to scenario modeling, NLP-driven insights, automated reporting, and AI governance in finance.
Popular practice exams
Reading shows the way. A diagnostic shows where you are on it.
Pick a target, answer the assessment, and get your gap by topic and a plan to close it.