Complete AI Training

Certification

Certification: GenAI Foundations – Practical Skills for Beginners

Show the world you have AI skills. Build practical experience with Generative AI through hands-on projects and concepts—perfect for beginners ready to enhance their CV and take the next step in their technology journey.

Exam of 10 to 20 questionsCertificate for LinkedInBeginner
Take the exam Share

The exam

Take the certification exam

Multiple-choice questions about the course. Pass with 70% or more and your certificate is issued at once, with a public page and an "Add to LinkedIn" button.

What the exam covers

2 questions from each of the 15 chapters of the course

20 multiple-choice questions, drawn fresh for every attempt. Pass with 70% or more.

  1. 01Introduction, roadmap and fundamentals103:59
  2. 02Local deployment, prompting and agents72:06
  3. 03Hardware, architecture and data science46:42
  4. 04Benchmarks, cloud platforms and APIs68:28
  5. 05Assistant workflows and real apps58:00
  6. 06Data mining, evaluation and search82:12
  7. 07Agentic prototyping and platform comparison76:57
  8. 08Azure AI Foundry, serving and compression59:57
  9. 09Real-time apps and orchestration96:51
  10. 10Structured JSON, Replicate, RAG prompting, CoT83:37
  11. 11Assistant design, FastHTML and ReAct95:16
  12. 12Cloud dev platforms and IaC83:50
  13. 13Data modelling and AI careers52:57
  14. 14Assistant tools, agentic workflows and RAG111:33
  15. 15Conclusion and next steps281:30
About the course

The Certification: GenAI Foundations – Practical Skills for Beginners provides a comprehensive starting point for those looking to build real-world skills in Generative AI. By mastering essential concepts like large language models, optimization techniques, and practical development tools, you can gain a competitive advantage and future-proof your career in AI-driven industries. Enroll now to unlock new opportunities and maximize your potential with foundational GenAI expertise.

This certification covers the following topics:

  • Understanding Large Language Models (LLMs)
  • Model Optimization and Distillation
  • Understanding Machine Learning Fundamentals
  • Exploring Development Environments
  • Utilizing External Libraries and APIs
  • Local vs. Cloud Development
  • Quantization for Model Efficiency
  • Serving Large Language Models
  • Vector Stores and Semantic Search
  • Library Management and Troubleshooting