NVIDIA-Certified Associate: Generative AI LLMs Preparation Details
The NVIDIA-Certified Associate: Generative AI LLMs (NCA-GENL) exam validates foundational skills in building and deploying generative AI and large language model applications. This guide maps every NCA-GENL domain to official NVIDIA and open-source documentation for fast, focused exam prep. You can also explore more artificial intelligence certification study guides on the Artificial Intelligence category to keep building your skills.
NVIDIA-Certified Associate: Generative AI LLMs Materials
| Coursera | Exam Prep (NCA-GENL): Generative AI LLMs |
| Udemy | NCA-GENL – Prep Course – GenAI & LLMs Associate |
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
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
Welcome to the cuDF documentation!
Welcome to cuML’s documentation!
1.3 Build LLM use cases such as retrieval-augmented generation (RAG), chatbots, and summarizers.
Overview of NVIDIA NIM for Large Language Models
Overview of NVIDIA NeMo Retriever Embedding NIM
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).
Cross-validation: evaluating estimator performance
Welcome to cuML’s documentation!
1.6 Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).
spaCy · Industrial-strength Natural Language Processing in Python
Overview of NVIDIA NeMo Retriever Embedding NIM
1.7 Read research papers (articles, conference papers, etc.) to identify emerging LLM trends and technologies.
1.8 Select and use models to create text embeddings.
Overview of NVIDIA NeMo Retriever Embedding NIM
SentenceTransformers Documentation
Using Sentence Transformers at Hugging Face
1.9 Use prompt engineering principles to create prompts to achieve desired results.
An Introduction to Large Language Models: Prompt Engineering and P-Tuning
Overview of NVIDIA NIM for Large Language Models
1.10 Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.
spaCy · Industrial-strength Natural Language Processing in Python
Keras documentation: Keras 3 API documentation
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
Welcome to the cuDF documentation!
2.2 Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
Metrics and scoring: quantifying the quality of predictions
Welcome to cuML’s documentation!
Cross-validation: evaluating estimator performance
2.3 Conduct data analysis under the supervision of a senior team member.
Welcome to the cuDF documentation!
10 Minutes to cuDF and Dask cuDF
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.
Metrics and scoring: quantifying the quality of predictions
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
Welcome to the cuDF documentation!
3.2 Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
Metrics and scoring: quantifying the quality of predictions
Welcome to cuML’s documentation!
Cross-validation: evaluating estimator performance
3.3 Conduct data analysis under the supervision of a senior team member.
Welcome to the cuDF documentation!
10 Minutes to cuDF and Dask cuDF
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.
Metrics and scoring: quantifying the quality of predictions
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
4.2 Build LLM use cases such as RAGs, chatbots, and summarizers.
Overview of NVIDIA NIM for Large Language Models
Overview of NVIDIA NeMo Retriever Embedding NIM
4.3 Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).
spaCy · Industrial-strength Natural Language Processing in Python
Overview of NVIDIA NeMo Retriever Embedding NIM
4.4 Identify system data, hardware, or software components required to meet user needs.
Get Started With NVIDIA AI Enterprise
4.5 Monitor functioning of data collection, experiments, and other software processes.
Overview: NVIDIA NIM for Large Language Models 2.0
RAPIDS | GPU Accelerated Data Science
4.6 Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.
spaCy · Industrial-strength Natural Language Processing in Python
Keras documentation: Keras 3 API documentation
4.7 Write software components or scripts under the supervision of a senior team member.
Keras documentation: Keras 3 API documentation
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 NCA-GENL study guide walked through all five domains of the NVIDIA-Certified Associate: Generative AI LLMs blueprint, from core machine learning knowledge to trustworthy AI. Every objective links to real NVIDIA documentation or the open-source tools the exam expects you to know, so you can study from primary sources instead of guesswork. You can also explore more artificial intelligence certification study guides on the Artificial Intelligence category to keep building your skills. Have a question or tip? Leave a comment below.
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