The NCA Generative AI Multimodal certification is an entry-level credential that validates the foundational skills needed to design, implement, and manage AI systems that synthesize and interpret data across text, image, and audio modalities.
Candidate audiences:
- AI DevOps engineers
- AI strategists
- Applied data research engineers
- Applied data scientists
- Applied deep learning research scientists
- Cloud solution architects
- Data scientists
- Deep learning performance engineers
- Generative AI specialists
- Large language model (LLM) specialists/researchers
- Machine learning engineers
- Senior researchers
- Software engineers
- Solutions architects
Prerequisites
Students should have a basic understanding of generative AI
Recommended training for this certification
- Getting Started With Deep Learning (self-paced course, 8 hours) or Fundamentals of Deep Learning (FDL) (instructor-led workshop, 8 hours)
- Introduction to Transformer-Based Natural Language Processing (self-paced course, 6 hours) or Building Transformer-Based Natural Language Processing Applications (BNLPA) (instructor-led workshop, 8 hours)
- Building Conversational AI Applications (BCAA) (instructor-led workshop, 8 hours)
- Generative AI With Diffusion Models (self-paced course, 8 hours) or Generative AI with Diffusion Models (GAIDM) (instructor-led workshop, 8 hours)
Students are also recommended to review these additional materials from NVIDIA:
- High-Resolution Image Synthesis via Two-Stage Generative Models (on-demand video, 35 minutes)
- Accelerated Creative AI - Using NVIDIA-Optimized Image Generation for Media and Entertainment (on-demand webinar, 1 hour)
- Building Lifelike Digital Avatars with NVIDIA ACE Microservices (blog, 15 minutes)
- The Future of Generative AI for Content Creation (on-demand video, 35 minutes)
Exams
Certification Exam Details
- Duration: One hour
- Price: $125
- Certification level: Associate
- Subject: Multimodal generative AI
- Number of questions: 50-60 multiple-choice
- Language: English
Topics covered in the exam include:
- Core machine learning and AI knowledge
- Data analysis and visualization
- Experimentation
- Multimodal data
- Performance optimization
- Software development and engineering
- Trustworthy AI
Recertification
This certification is valid for two years from issuance.
Recertification may be achieved by retaking the exam.