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Generative AI with LLMs Exam Guide

A comprehensive 6-week study plan to help you prepare for the NVIDIA Generative AI with LLMs Associate certification exam.

Exam Information

60 minutes

Duration

50 questions

Questions

70%

Passing Score

$135 USD

Exam Cost

Recommended Study Plan

Week 1

Generative AI Fundamentals

Generative AI Concepts

10-12 hours

Introduction to generative AI
Types of generative models (GANs, VAEs, Transformers)
Use cases and applications
Ethical considerations in AI

Recommended Resources:

NVIDIA DLI

Coursera

Week 2

Large Language Models

LLM Architecture and Training

12-14 hours

Transformer architecture deep dive
Attention mechanisms
Pre-training and fine-tuning
Tokenization and embeddings

Recommended Resources:

Hugging Face Course

Papers

Week 3

Prompt Engineering

Prompt Engineering and Optimization

10-12 hours

Prompt design principles
Zero-shot and few-shot learning
Chain-of-thought prompting
Prompt optimization techniques

Recommended Resources:

OpenAI Guides

LangChain

Week 4

RAG and Vector Databases

Retrieval-Augmented Generation

12-14 hours

RAG architecture and components
Vector embeddings and databases
Document processing and chunking
Semantic search implementation

Recommended Resources:

LangChain

Pinecone Docs

Week 5

Fine-Tuning and Deployment

Model Customization and Production

12-14 hours

Fine-tuning techniques (LoRA, PEFT)
Model evaluation and metrics
Inference optimization
Deployment considerations

Recommended Resources:

NVIDIA NeMo

Hugging Face

Week 6

NVIDIA Tools & Review

NVIDIA Ecosystem and Final Prep

15-18 hours

NVIDIA NeMo and TensorRT-LLM
NVIDIA AI Enterprise platform
Practice exams and review
Weak area reinforcement

Recommended Resources:

NVIDIA Docs

Practice Exams

Exam Tips & Strategies

Focus on NVIDIA Tools

Understand NVIDIA-specific tools like NeMo, TensorRT-LLM, and NVIDIA AI Enterprise platform thoroughly.

Time is Tight

With 60 minutes for 50 questions, you have just over 1 minute per question. Move quickly through easier questions.

Hands-On Practice

Use Google Colab or NVIDIA DLI labs to practice building and fine-tuning LLM applications.

Know the Architecture

Understand transformer architecture, attention mechanisms, and how different components work together.

Key Topics to Master

Transformer architecture and attention

Pre-training vs fine-tuning LLMs

Tokenization strategies

Prompt engineering techniques

RAG (Retrieval-Augmented Generation)

Vector databases and embeddings

LoRA and parameter-efficient fine-tuning

Model evaluation metrics (perplexity, BLEU)

NVIDIA NeMo framework

TensorRT-LLM optimization

Inference optimization techniques

Responsible AI practices

Hallucination mitigation

Context window management

Multi-modal generative AI

For the most up-to-date exam information, visit the official exam guide.

View Official Exam Guide

Back to NVIDIA Gen AI

Generative AI with LLMs Exam Guide

A comprehensive 6-week study plan to help you prepare for the NVIDIA Generative AI with LLMs Associate certification exam.

Exam Information

60 minutes

Duration

50 questions

Questions

70%

Passing Score

$135 USD

Exam Cost

Recommended Study Plan

Week 1

Generative AI Fundamentals

Generative AI Concepts

10-12 hours

Introduction to generative AI
Types of generative models (GANs, VAEs, Transformers)
Use cases and applications
Ethical considerations in AI

Recommended Resources:

NVIDIA DLI

Coursera

Week 2

Large Language Models

LLM Architecture and Training

12-14 hours

Transformer architecture deep dive
Attention mechanisms
Pre-training and fine-tuning
Tokenization and embeddings

Recommended Resources:

Hugging Face Course

Papers

Week 3

Prompt Engineering

Prompt Engineering and Optimization

10-12 hours

Prompt design principles
Zero-shot and few-shot learning
Chain-of-thought prompting
Prompt optimization techniques

Recommended Resources:

OpenAI Guides

LangChain

Week 4

RAG and Vector Databases

Retrieval-Augmented Generation

12-14 hours

RAG architecture and components
Vector embeddings and databases
Document processing and chunking
Semantic search implementation

Recommended Resources:

LangChain

Pinecone Docs

Week 5

Fine-Tuning and Deployment

Model Customization and Production

12-14 hours

Fine-tuning techniques (LoRA, PEFT)
Model evaluation and metrics
Inference optimization
Deployment considerations

Recommended Resources:

NVIDIA NeMo

Hugging Face

Week 6

NVIDIA Tools & Review

NVIDIA Ecosystem and Final Prep

15-18 hours

NVIDIA NeMo and TensorRT-LLM
NVIDIA AI Enterprise platform
Practice exams and review
Weak area reinforcement

Recommended Resources:

NVIDIA Docs

Practice Exams

Exam Tips & Strategies

Focus on NVIDIA Tools

Understand NVIDIA-specific tools like NeMo, TensorRT-LLM, and NVIDIA AI Enterprise platform thoroughly.

Time is Tight

With 60 minutes for 50 questions, you have just over 1 minute per question. Move quickly through easier questions.

Hands-On Practice

Use Google Colab or NVIDIA DLI labs to practice building and fine-tuning LLM applications.

Know the Architecture

Understand transformer architecture, attention mechanisms, and how different components work together.

Key Topics to Master

Transformer architecture and attention

Pre-training vs fine-tuning LLMs

Tokenization strategies

Prompt engineering techniques

RAG (Retrieval-Augmented Generation)

Vector databases and embeddings

LoRA and parameter-efficient fine-tuning

Model evaluation metrics (perplexity, BLEU)

NVIDIA NeMo framework

TensorRT-LLM optimization

Inference optimization techniques

Responsible AI practices

Hallucination mitigation

Context window management

Multi-modal generative AI

For the most up-to-date exam information, visit the official exam guide.

View Official Exam Guide