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The NVIDIA - Certified Associate: Generative AI and LLMs Certification (NCA-GENL) is a professional certification designed to validate the foundational knowledge and skills needed for working with generative artificial intelligence (AI) models and large language models (LLMs). The certification includes the basics of Generative AI, LLMs working, deployment techniques, and fine-tuning models for real-world applications. It is suitable for individuals interested in understanding and applying Generative AI, in domains like NLP (natural language processing) and AI-driven content generation.
Why is NVIDIA - Certified Associate: Generative AI and LLMs Certification (NCA-GENL) important?
Who should take the NVIDIA - Certified Associate: Generative AI and LLMs Certification (NCA-GENL) Exam?
NVIDIA - Certified Associate: Generative AI and LLMs Certification (NCA-GENL) Certification Course Outline
The NVIDIA - Certified Associate: Generative AI and LLMs Certification (NCA-GENL) Certification covers the following topics -
Module 1. Core Machine Learning and AI Knowledge
Module 2. Data Analysis
Module 3. Experimentation
Module 4. Software Development
Module 5. Trustworthy AI
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The certification exam consists of 50 questions with a 60-minute time limit. It is conducted online and can be remotely proctored.
The NVIDIA Certified Associate: Generative AI and LLMs (NCA-GENL) certification is designed to validate foundational knowledge and skills in generative AI and large language models (LLMs), specifically within the NVIDIA ecosystem. This entry-level certification is ideal for individuals aiming to understand the development, integration, and maintenance of AI-driven applications using generative AI and LLMs.
While there are no formal prerequisites, having a basic understanding of generative AI, LLMs, Python, and Linux is beneficial.
This certification is suitable for AI DevOps engineers, AI strategists, data scientists, machine learning engineers, software engineers, and other professionals involved in AI and LLMs.
Candidates will gain expertise in areas such as alignment, experimentation, experiment design, and using Python libraries for LLMs.
Topics include fundamentals of machine learning and neural networks, prompt engineering, data analysis, data preprocessing, feature engineering, software development, and LLM integration and deployment.
The certification is valid for two years.