Exam Study Guide: AI-Native Foundations Exam - AI-Native Foundations Professional

Agentic AI Understanding; understanding the meaning of an Agentic AI approach

Define the different common AI jargons.

Describe 5 levels of AI: Rule-Based Automation, Intelligent Automation, Agentic Workflows, Semi-Automatic Agents, Full-Autonomous Agents

Describe Retrieval-Augmented Generation (RAG): Articulate the concept of RAG and its role in enhancing AI model responses with external knowledge.

Navigate common AI terminology: Be able to use, define, and discuss frequently used AI terms.

Understand Composite AI: Understand how combining multiple AI techniques (e.g., machine learning, rules-based systems, and LLMs) can create more accurate and robust solutions than using a single method.

Understand how AI Agents work and their capabilities: Explain the function, architecture, and potential applications of AI agents.

Understand how to use generative AI: Understand what an LLM and what Generative AI is

Understand the concept of creating custom GPTs and copilots.

Understand the difference between an LLM and an AI Agent

Apply the principles of Responsible AI: Integrate ethical considerations and best practices into AI use to ensure fairness, transparency, and accountability.

Ethical Decision-Making Frameworks for AI: Dive deeper into specific frameworks or methodologies for navigating ethical dilemmas in AI development and deployment.

Explain AI general security best practices like Dos and Don’ts

Understanding Data Privacy and Security in AI: Learn about data governance, anonymization techniques, and compliance with regulations like GDPR or CCPA when using AI.

Ability to create effective prompts for Image and video generation

Ability to create RAG-based prompts

Apply the Success Factors to your specific role by creating a concrete micro-plan for immediate implementation: Develop a personalized plan to integrate AI-Native Success Factors into daily work.

Craft a concise summary of an AI opportunity, linking it to a relevant challenge or goal in your work: Clearly communicate the potential of AI solutions in addressing specific business needs.

Craft effective prompts for text generation: Develop and refine prompts to elicit desired and accurate responses from AI models.

Design AI-enhanced workflows combining human judgment with appropriate AI enhancements: Create efficient processes that leverage AI tools while maintaining human oversight and decision-making.

Generate powerful questions with AI to uncover hidden project risks: Utilize AI to formulate questions that reveal unforeseen risks in projects.

Use AI to turn difficult feedback into productive questions: Leverage AI to reframe challenging feedback into constructive inquiries.

Articulate the four EDGE (exponential growth, disruptive, generative, emergent) forces and the AI-Native case in plain business terms: Explain key AI concepts and their business relevance in an accessible manner.

Classify AI use cases as Stable, Evolving, or Frontier: Categorize AI applications based on their maturity and potential for future development.

Distinguish between core AI solution patterns to help teams select the most practical approach: Guide teams in choosing appropriate AI solutions for specific problems.

Translate AI concepts and their trade-offs into clear business language: Bridge the gap between technical AI details and business implications.

Translate business needs into a realistic AI solution : Convert organizational requirements into practical AI and data implementation plans.

Vendor Selection and Management for AI Solutions: Understand how to evaluate, select, and manage third-party AI tools and services.