Section 1
Gen AI Foundations
Preview
Preview: Fundamentals of Gen AI
- 4 of 60 lesson topics
- 1 overview segment
- 3 core concepts
- 2 exam tips
Lesson Topics
- Generative Output Overview
- Multimodal Models Overview
- Model Selection Factors Overview
- Data Quality Risk Overview
Overview
This section, 'Gen AI Foundations,' establishes the fundamental concepts underpinning generative AI. It's crucial because it provides the baseline understanding needed to evaluate models, manage data, and implement Gen AI solutions responsibly. Without this foundation, you'll struggle with later sections focused on governance, security, and advanced applications.
Core Concepts
- **Generative Output:** Generative AI models produce *new* content, unlike classification models that assign labels. This includes text, images, and other data types, based on patterns learned from training data. Question 001 highlights this difference.
- **Multimodal Models:** These models process and integrate multiple data types, such as text and images. Question 002 emphasizes the need for models capable of handling diverse input formats.
- **Model Selection Factors:** Considerations include context window size (maximum input length), output quality, security requirements, and cost. Question 003 demonstrates the prioritization of these factors in a practical scenario.
Exam Tips
- Carefully read the scenario to identify the *specific* requirements and constraints.
- Don't assume that the 'most advanced' solution is always the best. Consider factors like cost and complexity.