The Generative AI LLMs professional certification is an intermediate-level credential that validates a candidate’s ability to design, train, and fine-tune cutting-edge LLMs, applying advanced distributed training techniques and optimization strategies to deliver high-performance AI solutions.
Candidate Audiences:
- Software developers
- Software engineers
- Solutions architects
- Machine learning engineers
- Data scientists
- AI strategists
- Generative AI specialists
Prerequisites
2–3 years of practical experience in AI or ML roles working with large language models, with a solid grasp of transformer-based architectures, prompt engineering, distributed parallelism, and parameter-efficient fine-tuning. Familiarity with advanced sampling, hallucination mitigation, retrieval-augmented generation, model evaluation metrics, and performance profiling is expected. Proficiency in efficient coding (Python, plus C++ for optimization), experience with containerization and orchestration tools, and acquaintance with NVIDIA’s AI platforms is beneficial but not strictly required.
Recommended training for this certification
- Building RAG Agents with LLMs (self-paced, 8 hours)
- Adding New Knowledge to LLMs (instructor-led workshop, 8 hours)
- Model Parallelism: Building and Deploying Large Neural Networks (MPBDLNN)
- Deploying RAG Pipelines for Production at Scale (self-paced, 8 hours)
- Optimizing CUDA ML Codes with NVIDIA Nsight's Profiling Tools (self-paced, 4 hours)
Exams
Exam Details:
- Duration: 120 minutes
- Price: $200
- Certification level: Professional
- Subject: Generative AI LLMs
- Number of questions: 60-70
- Language: English
Topics covered in the exam:
- LLM Foundations and Prompting: Covers model architecture, prompt engineering techniques (CoT, zero/one/few-shot), and adaptation strategies.
- Data Preparation and Fine-Tuning: Involves dataset curation, tokenization, domain adaptation, and customizing LLMs for specific use cases.
- Optimization and Acceleration: Focuses on GPU/distributed training, performance tuning, batch/memory optimization, and efficiency improvements.
- Deployment and Monitoring: Includes building scalable inference pipelines, containerized orchestration, real-time monitoring, reliability, and lifecycle management.
- Evaluation and Responsible AI: Covers benchmarking, error analysis, bias detection, guardrails, compliance, and ethical AI practices.
Recertification
This certification is valid for two years from issuance. Recertification may be achieved by retaking the exam.