NVIDIA NCA-GENL Exam Syllabus Topics:
| Topic | Details |
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| Topic 1 | - Data preprocessing and feature engineering: Covers preparing raw data through cleaning, transformation, and feature selection to make it suitable for model training.
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| Topic 2 | - Data analysis and visualization: Covers interpreting datasets and presenting insights through visual tools to support informed model development decisions.
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| Topic 3 | - Alignment: Addresses methods for ensuring LLM behavior is safe, accurate, and consistent with human intentions and values.
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| Topic 4 | - Fundamentals of machine learning and neural networks: Covers the core concepts of how machine learning models learn from data, including the structure and function of neural networks that underpin large language models.
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| Topic 5 | - LLM integration and deployment: Addresses connecting LLMs into real-world applications and deploying them reliably across production environments.
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| Topic 6 | - Prompt engineering: Focuses on techniques for designing and refining input prompts to effectively guide LLM outputs toward desired results.
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| Topic 7 | - Python libraries for LLMs: Covers key Python frameworks and tools — such as LangChain, Hugging Face, and similar libraries — used to build and interact with LLMs.
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Reference: https://www.nvidia.com/en-us/learn/certification/generative-ai-llm-associate/
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