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Product Description Exam Number/Code: NCA-GENL
Exam Name: NVIDIA Generative AI LLMs
Certification Provider: NVIDIA
Corresponding Certification: NVIDIA-Certified Associate
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NCA-GENL exam is an important NVIDIA Certification which can test your professional skills. Candidates want to pass the exam successfully to prove their competence. Fast2test NVIDIA technical experts have collected and certified 97 questions and answers of NVIDIA-Certified Associate - NVIDIA Generative AI LLMs which are designed to cover the knowledge points of the Planning and Designing NVIDIA Superdome Server Solutions and enhance candidates' abilities. With Fast2test NCA-GENL preparation tests you can pass the NVIDIA-Certified Associate - NVIDIA Generative AI LLMs easily, get the NVIDIA certification and go further on NVIDIA career path.

NVIDIA NCA-GENL Exam Syllabus Topics:

TopicDetails
Topic 1
  • This section of the exam measures skills of AI Product Developers and covers how to strategically plan experiments that validate hypotheses, compare model variations, or test model responses. It focuses on structure, controls, and variables in experimentation.
Topic 2
  • Data Preprocessing and Feature Engineering: This section of the exam measures the skills of Data Engineers and covers preparing raw data into usable formats for model training or fine-tuning. It includes cleaning, normalizing, tokenizing, and feature extraction methods essential to building robust LLM pipelines.
Topic 3
  • LLM Integration and Deployment: This section of the exam measures skills of AI Platform Engineers and covers connecting LLMs with applications or services through APIs, and deploying them securely and efficiently at scale. It also includes considerations for latency, cost, monitoring, and updates in production environments.
Topic 4
  • Experimentation: This section of the exam measures the skills of ML Engineers and covers how to conduct structured experiments with LLMs. It involves setting up test cases, tracking performance metrics, and making informed decisions based on experimental outcomes.:
Topic 5
  • Data Analysis and Visualization: This section of the exam measures the skills of Data Scientists and covers interpreting, cleaning, and presenting data through visual storytelling. It emphasizes how to use visualization to extract insights and evaluate model behavior, performance, or training data patterns.
Topic 6
  • Fundamentals of Machine Learning and Neural Networks: This section of the exam measures the skills of AI Researchers and covers the foundational principles behind machine learning and neural networks, focusing on how these concepts underpin the development of large language models (LLMs). It ensures the learner understands the basic structure and learning mechanisms involved in training generative AI systems.
Topic 7
  • Alignment: This section of the exam measures the skills of AI Policy Engineers and covers techniques to align LLM outputs with human intentions and values. It includes safety mechanisms, ethical safeguards, and tuning strategies to reduce harmful, biased, or inaccurate results from models.
Topic 8
  • Python Libraries for LLMs: This section of the exam measures skills of LLM Developers and covers using Python tools and frameworks like Hugging Face Transformers, LangChain, and PyTorch to build, fine-tune, and deploy large language models. It focuses on practical implementation and ecosystem familiarity.
Topic 9
  • Software Development: This section of the exam measures the skills of Machine Learning Developers and covers writing efficient, modular, and scalable code for AI applications. It includes software engineering principles, version control, testing, and documentation practices relevant to LLM-based development.

Reference: https://www.nvidia.com/en-us/learn/certification/generative-ai-llm-associate/

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