NVIDIA Certified Associate: Generative AI LLMs Exam Prep

Covers foundational generative AI and LLM concepts, prompting, retrieval-augmented generation, decoding, transfer learning, fine-tuning, and advanced NLP/LLM concepts. This domain emphasizes practical understanding of how LLMs work, how to interact with them effectively, and when to use them versus simpler approaches.

• Task 1.1: Explain foundational generative AI and LLM concepts
• Generative AI vs traditional ML
• Common LLM use cases
• LLM architecture types
• When to use LLMs
• Task 1.2: Apply prompting techniques for LLM interaction
• Prompting styles

Key references: NCA-GENL official exam guide · ExamPal shared topic tree

Covers core machine learning concepts, evaluation metrics, transformer architecture fundamentals, key transformer components, and experimentation/data-oriented AI workflows. This domain provides the foundational ML and transformer knowledge needed to understand and assess generative AI systems.

• Task 2.1: Explain core machine learning concepts
• Learning paradigms
• Dataset splits
• Loss function purpose
• Overfitting and underfitting
• Training accuracy limitations
• Task 2.2: Interpret model evaluation metrics and validation methods

Key references: NCA-GENL official exam guide · ExamPal shared topic tree

Covers NVIDIA generative AI and inference technologies, model optimization, scaling strategies, and software development/deployment practices for LLM systems. This domain focuses on practical deployment, performance, and infrastructure considerations in the NVIDIA ecosystem.

• Task 3.1: Identify NVIDIA generative AI and inference technologies
• NeMo Framework
• TensorRT
• Triton Inference Server
• ONNX
• NCCL
• Task 3.2: Explain model optimization and efficient deployment techniques

Key references: NCA-GENL official exam guide · ExamPal shared topic tree

Covers evaluation of generative AI systems, experiment design, and data analysis for model improvement. This domain emphasizes both automated and human evaluation, controlled experimentation, and using feedback and monitoring to improve outcomes.

• Task 4.1: Evaluate generative AI system performance
• Task-appropriate evaluation metrics
• BLEU and ROUGE interpretation
• Automated and human evaluation
• Evaluation dimensions
• Task 4.2: Design and interpret experiments for model comparison
• A/B testing purpose

Key references: NCA-GENL official exam guide · ExamPal shared topic tree

Covers principles of trustworthy AI, bias and privacy risks, and techniques for improving safety and reliability in generative systems. This domain emphasizes governance, human oversight, guardrails, and risk reduction for high-stakes use cases.

• Task 5.1: Explain principles of trustworthy AI
• Trustworthy AI principles
• Importance in generative systems
• Governance and oversight
• Utility, openness, and control
• Task 5.2: Recognize bias, privacy, and risk in LLM systems
• Protected attributes and bias

Key references: NCA-GENL official exam guide · ExamPal shared topic tree