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.
Prerequisites
Students should have a basic understanding of generative AI
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 (instructor-led workshop, 8 hours)
- Fundamentals of Accelerated Data Science (FADS) (instructor-led workshop, 8 hours)
- Get Started With Highly Accurate Custom ASR for Speech AI (self-paced course, 3 hours)
- Building Conversational AI Applications (BCAA) (instructor-led workshop, 8 hours)
- Introduction to Transformer-Based Natural Language Processing (self-paced course, 6 hours)
- Generative AI With Diffusion Models (self-paced course, 8 hours) or Generative AI with Diffusion Models (GAIDM) (instructor-led workshop, 8 hours)
- Deploying a Model for Inference at Production Scale (self-paced course, 4 hours)
- Efficient Large Language Model (LLM) Customization (ELLMC) (instructor-led workshop, 8 hours)
- Prompt Engineering With LLaMA-2 (self-paced course, 3 hours)
- Rapid Application Development Using Large Language Models (RADLLM) (instructor-led workshop, 8 hours)
- Computer Vision for Industrial Inspection (CVII) (instructor-led workshop, 8 hours)
- Applications of AI for Anomaly Detection (AAAD) (instructor-led workshop, 8 hours)
- Applications of AI for Predictive Maintenance (AAPM) (instructor-led workshop, 8 hours)
Exams
Certification Exam Details
- Duration: One hour
- Price: $135
- Certification level: Associate
- Subject: Multimodal generative AI
- Number of questions: 50
- Language: English
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
Topics covered in the exam include:
- Core machine learning/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.