NVIDIA-Certified Professional: Generative AI LLMs (NCP-GENL)

About This Certification

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. The exam is online and proctored remotely, includes 60–70 questions, and has a 120-minute time limit.

Please carefully review our certification FAQs and exam policies before scheduling your exam.

Certification Exam Details

Duration: 120 minutes  

Price: $200

Certification level: Professional  

Subject: Generative AI LLMs  

Number of questions: 60–70 

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.

Language: English 

Validity: This certification is valid for two years from issuance. Recertification may be achieved by retaking the exam.

Credentials: Upon passing the exam, participants will receive a digital badge and optional certificate indicating the certification level and topic.

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.

Candidate Audiences

  • Software developers
  • Software engineers
  • Solutions architects
  • Machine learning engineers
  • Data scientists
  • AI strategists
  • Generative AI specialists

Exam Study Guide

Review study guide