Complete Guide to Passing NVIDIA’s NCA-GENL Exam: Generative AI, LLMs, Prompting, and Model Deployment
Sub Category
- Hardware
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Objectives
- Understand foundational concepts in machine learning and neural networks critical to generative AI.
- Explain the architecture of transformers and large language models (LLMs), including attention mechanisms and training strategies.
- Design and evaluate effective prompts using zero-shot, few-shot, and chain-of-thought techniques.
- Compare fine-tuning, instruction tuning, LoRA, and PEFT approaches for adapting pretrained models.
- Use key NVIDIA tools such as NeMo, Triton, RAPIDS, and TensorRT for LLM training, optimization, and deployment.
- Apply best practices in LLM evaluation, experimentation, and reproducibility to prepare for real-world use and the certification exam.
Pre Requisites
- Basic understanding of Python programming (e.g., variables, functions, loops)
- Familiarity with general AI/ML terminology such as “model,” “training,” “inference,” and “dataset”
- Curiosity about generative AI technologies, including chatbots, LLMs, and prompt-based tools
- Access to a computer with a modern browser for hands-on labs and NVIDIA-recommended tools
- Optional but beneficial: Experience with Jupyter notebooks or platforms like Google Colab
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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