The NCA Generative AI LLMs certification is an entry-level credential that validates the foundational concepts for developing, integrating, and maintaining AI-driven applications using generative AI and large language models (LLMs) with NVIDIA solutions.
Candidate audience includes:
- AI DevOps engineers
- AI strategists
- Applied data scientists
- Applied data research engineers
- Applied deep learning research scientists
- Cloud solution architects
- Data scientists
- Deep learning performance engineers
- Generative AI specialists
- LLM specialists/researchers
- Machine learning engineers
- Senior researchers
- Software engineers
- Solutions architects
Prerequisites
Learners should have a basic understanding of generative AI and large language models.
Recommended training for this certification
- Generative AI Explained (self-paced course, 2 hours, free)
- Getting Started With Deep Learning (self-paced course, 8 hours) or Fundamentals of Deep Learning (FDL) (instructor-led workshop, 8 hours)
- Fundamentals of Accelerated Data Science (FADS) (instructor-led workshop, 8 hours)
- Introduction to Transformer-Based Natural Language Processing (self-paced course, 6 hours)
- Building Transformer-Based Natural Language Processing Applications (BNLPA) (instructor-led workshop, 8 hours)
- Rapid Application Development Using Large Language Models (RADLLM) (instructor-led workshop, 8 hours)
- Efficient Large Language Model (LLM) Customization (ELLMC) (instructor-led workshop, 8 hours)
- Prompt Engineering With LLaMA-2 (self-paced course, 3 hours)
- Augmenting Your LLM Using Retrieval-Augmented Generation (self-paced course, 1 hour, free)
- Building RAG Agents With LLMs (self-paced course, 8 hours, free) or Building RAG Agents with LLMs (BRAL) (instructor-led workshop, 8 hours)
Exams
Certification Exam Details
- Duration: One hour
- Price: $125
- Certification level: Associate
- Subject: Generative AI and large language models
- Number of questions: 50-60 multiple-choice
- Language: English
Topics covered in the exam include:
- Fundamentals of machine learning and neural networks
- Prompt engineering
- Alignment
- Data analysis and visualization
- Experimentation
- Data Preprocessing and feature engineering
- Experiment design
- Software development
- Python libraries for LLMs
- LLM integration and deployment
Recertification
This certification is valid for two years from issuance. Recertification may be achieved by retaking the exam.