Generative AI Llm
NCA-GENL
Money Back Guarantee
98% Success Rate
Real Questions
1
Core Machine Learning and AI Knowledge 30%
1.1
Machine Learning Fundamentals
2 subtopics1.1.1Neural Network Basics
1.1.2Deep Learning Concepts
Learning Objectives
- Understand fundamental machine learning and neural network concepts
- Explain the mathematical foundations of deep learning
- Identify appropriate architectures for different tasks
1.2
Transformer Architecture and LLMs
2 subtopics1.2.1Transformer Fundamentals
1.2.2Large Language Model Architectures
Learning Objectives
- Explain transformer architecture components and their functions
- Compare different LLM architectures and their use cases
- Understand how self-supervision improves transformer models
1.3
Generative AI Concepts
2 subtopics1.3.1Generative Model Types
1.3.2Retrieval-Augmented Generation (RAG)
Learning Objectives
- Understand different generative AI approaches
- Implement RAG systems for enhanced accuracy
- Evaluate generative model performance
Domain Hands-on Skills
Building basic neural networksImplementing transformer modelsCreating RAG pipelines
Common Mistakes to Avoid
- Confusing encoder-only vs decoder-only architectures
- Misunderstanding attention mechanism computation
- Incorrect loss function selection
2
Software Development 24%
3
Experimentation 22%
4
Data Analysis and Visualization 14%
5