NVIDIA-Certified Associate: Generative AI LLMs Preparation Details
The NVIDIA-Certified Associate: Generative AI LLMs (NCA-GENL) exam validates foundational skills in building, integrating, and maintaining generative AI and large language model applications with NVIDIA solutions. This guide maps every exam objective across all five domains to official NVIDIA documentation and training resources. You can also explore more NVIDIA certification study guides on the NVIDIA category to keep building your skills.
NVIDIA-Certified Associate: Generative AI LLMs Materials
| Coursera | NCA-GENL: NVIDIA-Certified Generative AI LLMs |
| Udemy | NCA-GENL: SoAI-Certified Generative AI LLMs |
| Whizlabs | NVIDIA Certified Associate Gen AI and LLMs |
Core Machine Learning and AI Knowledge: Exam Weight 30%
Knowledge of algorithms, conventions, and techniques that allow computers to learn from and make predictions or decisions based on data.
1.1 Assist in deployment and evaluation of model scalability, performance, and reliability under the supervision of senior team members.
NVIDIA Triton Inference Server
NVIDIA Collective Communications Library (NCCL)
1.2 Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
RAPIDS | GPU Accelerated Data Science
RAPIDS cuDF Accelerates pandas Nearly 150x with Zero Code Changes
1.3 Build LLM use cases such as retrieval-augmented generation (RAG), chatbots, and summarizers.
What Is Retrieval-Augmented Generation (RAG)?
Explainer: What Is Retrieval-Augmented Generation?
1.4 Curate and embed content datasets for RAGs.
Overview of NVIDIA NeMo Retriever Embedding NIM
1.5 Familiarity with the fundamentals of machine learning (e.g., feature engineering, model comparison, cross validation).
RAPIDS | GPU Accelerated Data Science
RAPIDS cuDF Accelerates pandas Nearly 150x with Zero Code Changes
1.6 Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).
RAPIDS | GPU Accelerated Data Science
Overview of NVIDIA NeMo Retriever Embedding NIM
1.7 Read research papers (articles, conference papers, etc.) to identify emerging LLM trends and technologies.
World Leading Research | NVIDIA Research
An Introduction to Large Language Models: Prompt Engineering and P-Tuning
1.8 Select and use models to create text embeddings.
Overview of NVIDIA NeMo Retriever Embedding NIM
What Is Retrieval-Augmented Generation (RAG)?
1.9 Use prompt engineering principles to create prompts to achieve desired results.
An Introduction to Large Language Models: Prompt Engineering and P-Tuning
How to Get Better Outputs from Your Large Language Model
Mastering LLM Techniques: Customization
1.10 Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.
RAPIDS | GPU Accelerated Data Science
RAPIDS cuDF Accelerates pandas Nearly 150x with Zero Code Changes
Data Analysis: Exam Weight 14%
Inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.
2.1 Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
RAPIDS | GPU Accelerated Data Science
RAPIDS cuDF Accelerates pandas Nearly 150x with Zero Code Changes
2.2 Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
Mastering LLM Techniques: Customization
How to Get Better Outputs from Your Large Language Model
RAPIDS | GPU Accelerated Data Science
2.3 Conduct data analysis under the supervision of a senior team member.
RAPIDS | GPU Accelerated Data Science
2.4 Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
RAPIDS | GPU Accelerated Data Science
2.5 Identify relationships and trends or any factors that could affect the results of research.
World Leading Research | NVIDIA Research
RAPIDS | GPU Accelerated Data Science
Experimentation: Exam Weight 22%
The study of how to perform, evaluate, and interpret experiments, including AI model evaluation and the use of human subjects in labeling or reinforcement learning from human feedback (RLHF).
3.1 Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
RAPIDS | GPU Accelerated Data Science
3.2 Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
Mastering LLM Techniques: Customization
How to Get Better Outputs from Your Large Language Model
3.3 Conduct data analysis under the supervision of a senior team member.
RAPIDS | GPU Accelerated Data Science
3.4 Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
RAPIDS | GPU Accelerated Data Science
3.5 Identify relationships and trends or any factors that could affect the results of research.
World Leading Research | NVIDIA Research
Software Development: Exam Weight 24%
Create, maintain, and test software.
4.1 Assist in the deployment and evaluations of model scalability, performance, and reliability under the supervision of senior team member.
NVIDIA Triton Inference Server
NVIDIA Collective Communications Library (NCCL)
4.2 Build LLM use cases such as RAGs, chatbots, and summarizers.
What Is Retrieval-Augmented Generation (RAG)?
Explainer: What Is Retrieval-Augmented Generation?
4.3 Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).
RAPIDS | GPU Accelerated Data Science
Overview of NVIDIA NeMo Retriever Embedding NIM
4.4 Identify system data, hardware, or software components required to meet user needs.
NVIDIA Triton Inference Server
4.5 Monitor functioning of data collection, experiments, and other software processes.
NVIDIA Triton Inference Server
RAPIDS | GPU Accelerated Data Science
4.6 Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.
RAPIDS | GPU Accelerated Data Science
RAPIDS cuDF Accelerates pandas Nearly 150x with Zero Code Changes
4.7 Write software components or scripts under the supervision of a senior team member.
End-to-End AI for NVIDIA-Based PCs: Transitioning AI Models with ONNX
Trustworthy AI: Exam Weight 10%
Creation and assessment of ethical, energy-conscious, and reliable artificial intelligence systems capable of interpreting and integrating various forms of data, ensuring that they’re designed and applied in a manner that’s transparent, fair, and verifiable.
5.1 Describe the ethical principles of trustworthy AI.
Trustworthy AI For A Better World
5.2 Describe the balance between data privacy and the importance of data consent.
Trustworthy AI For A Better World
5.3 Describe how to use NVIDIA and other technologies to improve AI trustworthiness.
Trustworthy AI For A Better World
5.4 Describe how to minimize bias in AI systems.
Trustworthy AI For A Better World
Wrapping Up NVIDIA-Certified Associate: Generative AI LLMs
This guide has walked through every domain of the NVIDIA-Certified Associate: Generative AI LLMs (NCA-GENL) exam, from core machine learning knowledge through trustworthy AI. Working through the linked NVIDIA documentation and training resources for each objective will help you build a solid, practical understanding of generative AI and LLM development on NVIDIA’s platform. Good luck on your exam. You can also explore more NVIDIA certification study guides on the NVIDIA category to keep building your skills. Have a question or tip? Leave a comment below.
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