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NVIDIA-Certified Associate: Generative AI LLMs (NCA-GENL) Syllabus

Core Machine Learning and AI Knowledge

Applying ML fundamentals, embeddings, prompt engineering and RAG to LLM use cases.

30%
Weight
18
Questions
30
Marks

Machine learning fundamentals

  • Feature engineering
  • Model comparison and cross-validation
  • Traditional ML analysis with spaCy, NumPy and Keras
  • Evaluating model scalability, performance and reliability

LLM techniques

  • Selecting models to generate text embeddings
  • Prompt engineering principles
  • Curating and embedding datasets for RAG
  • Building RAG, chatbot and summarization use cases
  • Tracking emerging LLM research

Data Analysis

Extracting insights from data and comparing models with statistical metrics.

14%
Weight
9
Questions
14
Marks

Data mining and visualization

  • Extracting insights from large datasets
  • Building charts and graphs
  • Identifying relationships and trends
  • Factors that can affect results

Model comparison

  • Loss functions
  • Explained variance
  • Statistical comparison of models
  • Supervised data analysis tasks

Experimentation

Preparing data and running training, benchmarking and evaluation experiments for LLMs.

22%
Weight
13
Questions
22
Marks

Data preparation for training

  • Dataset preparation for pretraining and fine-tuning
  • Scraping and tokenization
  • Human feedback data for RLHF
  • Fine-tuning workflows

Testing and evaluation

  • Designing hardware and software tests for LLM applications
  • Benchmarking and refining models
  • Evaluating technologies on cost, portability and usability
  • Running experiments on models and pipelines

Software Development

Building, scripting and monitoring LLM applications and identifying their requirements.

24%
Weight
14
Questions
24
Marks

Building LLM applications

  • RAG systems, chatbots and summarizers
  • Python NLP tools and vector databases
  • Writing software components and scripts
  • Serving models with NVIDIA tools such as Triton Inference Server

Requirements and monitoring

  • Identifying data, hardware and software requirements
  • Monitoring data collection and experiments
  • Evaluating scalability and reliability
  • GPU-accelerated data processing with RAPIDS

Trustworthy AI

Applying ethical principles, privacy and bias mitigation to AI systems.

10%
Weight
6
Questions
10
Marks

Ethics and privacy

  • Ethical principles of trustworthy AI
  • Balancing data privacy and consent
  • Transparency and accountability
  • Responsible AI guidelines

Bias and safety tooling

  • Methods to reduce bias
  • NVIDIA tools for AI trustworthiness such as NeMo Guardrails
  • Evaluating model outputs for harm
  • Data provenance

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Hi! I'm your AI Tutor. Let's create a personalized study plan for your NVIDIA-Certified Associate: Generative AI LLMs (NCA-GENL) certification.

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Learning Progress

Core Machine Learning and AI Knowledge 85%
Data Analysis 92%

Practice Test Scores

95%
Latest Score
Above passing threshold

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