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.
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Exam Preparation
Certification Learning Path
Exam Blueprint
The table below provides an overview of the topic areas covered in the certification exam and how much of the exam is focused on that subject.
| Topic Areas | % of Exam | Topics Covered |
|---|---|---|
| LLM Architecture | 6% | Understanding and applying foundational LLM structures and mechanisms. |
| Prompt Engineering | 13% | Adapting LLMs to new domains, tasks, or data distributions via prompt engineering, chain-of-thought (CoT), domain adaptation, zero/one/few-shot learning, and output control. |
| Data Preparation | 9% | Preparing data for pretraining, fine-tuning, or inference by cleaning, curating, analyzing, and organizing datasets, tokenization, and vocabulary management. |
| Model Optimization | 17% | Deploying LLMs in production environments. Includes building containerized inference pipelines, configuring model serving and orchestration (e.g., Kubernetes, NVIDIA Triton™), implementing real-time monitoring, optimizing deployment for latency and throughput, and managing model updates. |
| Fine-Tuning | 13% | Creating conceptual data mapping documents, custom importers, exports, and scripts for interchange of data with OpenUSD. |
| Evaluation | 7% | Assessing LLMs via quantitative and qualitative metrics, framework design, benchmarking, error analysis, and scalable evaluation. |
| GPU Acceleration and Optimization | 14% | Scaling and optimizing LLM training/inference on GPU hardware. Involves multi-GPU/distributed setups, parallelism techniques, troubleshooting, memory and batch optimization, and performance profiling. |
| Model Deployment | 9% | Deploying LLMs in production via containerized pipelines, scalable orchestration, efficient batch/model serving, and real-time monitoring. |
| Production Monitoring and Reliability | 7% | Establishing monitoring dashboards and reliability metrics while tracking logs and anomalies for root cause analysis and benchmarking agents against previous versions. Implementing automated tuning, retraining, and versioning to ensure continuous uptime, transparency, and trust in production deployments. |
| Safety, Ethics, and Compliance | 5% | Responsible for AI practices throughout the LLM lifecycle. Includes auditing for bias and fairness, implementing guardrails, configuring monitoring for ethical compliance, and applying bias detection and mitigation strategies to ensure responsible deployment and use of LLMs. |
Exam Study Guide
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