NVIDIA Generative AI LLM Certification Exam

Knowledge of algorithms, conventions, and techniques that allow computers to learn from and make predictions or decisions based on data.

• Assist in deployment and evaluation of model scalability, performance, and reliability under the supervision of senior team members.
• Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
• Build LLM use cases such as retrieval-augmented generation (RAG), chatbots, and summarizers.
• Curate and embed content datasets for RAGs.
• Familiarity with the fundamentals of machine learning (e.g., feature engineering, model comparison, cross validation).
• Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).
• Read research papers (articles, conference papers, etc.) to identify emerging LLM trends and technologies.
• Select and use models to create text embeddings.
• Use prompt engineering principles to create prompts to achieve desired results.
• Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.

30%
Exam Weight

Inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.

• Awareness of the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.
• Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
• Conduct data analysis under the supervision of a senior team member.
• Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
• Identify relationships and trends or any factors that could affect the results of research.

14%
Exam Weight

Perform, evaluate, and interpret experiments, including AI model evaluation and the use of human subjects in labeling or reinforcement learning from human feedback (RLHF).

• Assist in model training and training optimization under the supervision of a senior team member.
• Assist in preparing (e.g., scraping, tokenization) large datasets for pretraining, fine-tuning, and RLHF.
• Assist in the design and conduct of hardware or software tests for LLM applications.
• Assist in the evaluation of current or emerging technologies to consider factors such as cost, portability, compatibility, or usability.
• Awareness of, and/or participation in, data collection from human subjects (e.g., RLHF).
• Evaluate and refine existing models / benchmarking.
• Executes experimentation to evaluate models and pipelines.

22%
Exam Weight

Create, maintain, and test software.

• Assist in the deployment and evaluations of model scalability, performance, and reliability under the supervision of a senior team member.
• Build LLM use cases such as RAGs, chatbots, and summarizers.
• Familiarity with the capabilities of Python natural language packages (spaCy, NumPy, vector databases, etc.).
• Identify system data, hardware, or software components required to meet user needs.
• Monitor functioning of data collection, experiments, and other software processes.
• Use Python packages (spaCy, NumPy, Keras, etc.) to implement specific traditional machine learning analyses.
• Write software components or scripts under the supervision of a senior team member.

24%
Exam Weight

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.

• Describe the ethical principles of trustworthy AI.
• Describe the balance between data privacy and the importance of data consent.
• Describe how to use NVIDIA and other technologies to improve AI trustworthiness.
• Describe how to minimize bias in AI systems.

10%
Exam Weight