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NVIDIA NCA-GENL Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 2	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.
Topic 3	Data analysis and visualization: Covers interpreting datasets and presenting insights through visual tools to support informed model development decisions.
Topic 4	Experiment design: Focuses on structuring controlled tests and workflows to systematically evaluate LLM performance and outcomes.
Topic 5	Alignment: Addresses methods for ensuring LLM behavior is safe, accurate, and consistent with human intentions and values.
Topic 6	Prompt engineering: Focuses on techniques for designing and refining input prompts to effectively guide LLM outputs toward desired results.
Topic 7	Data preprocessing and feature engineering: Covers preparing raw data through cleaning, transformation, and feature selection to make it suitable for model training.
Topic 8	Experimentation: Explores running and evaluating trials to test model behavior, compare approaches, and validate generative AI solutions.
Topic 9	LLM integration and deployment: Addresses connecting LLMs into real-world applications and deploying them reliably across production environments.