AI Aware Solutions Architects requires a wealth of various skill sets, which are covered on the exam.
The certification exam will test candidates on the following exam objectives.
- AI/ML Fundamentals
- AI Ethics Foundational Concepts.
- AI and Data Governance, Privacy and Lifecycles
- AI Accountability, Transparency and Explainability
- Global Regulatory and Policy Frameworks
Certified Responsible AI Ethics Officer (CRAIEO)
Exam Objectives
Exam Objectives
Domain One - AI Fundamentals
AI/ML Core Concepts
What is AI and ML
What is Deep learning
What are Machine Learning Types
Compare and Contrast AI and ML
Generative AI, Predictive AI, Agentic AI, and others.
History of AI/ML
How AI Works 10,000 foot level (Google Example)
Data Collection, Preparation and Storage
Large Language Models (LLM)
Natural Language Processing (NLP)
Understanding Algorithms
AI ML Project Walkthru
AI Resume Filtering
Use Case AWS MLFlow
Prompt and Context Engineering
What is Prompt Engineering
What is Context Engineering
Safety and Harm Reduction
Preventing Misinformation
Defining Guardrails and Wrappers
Bias Mitigation
Establishing Ethical Boundaries
Traceability and Accountability
Domain Two - AI Ethics Foundational Concepts
Core Foundational Concepts
Defining AI Ethics
Key AI Ethical Principles
Responsible AI Ethics and Governance
Seven C's of AI
Key Pillars of AI Ethics
Understanding Transparency with AI
Key Terminology to Know
Key Ethical Frameworks
Case Study - AI Ethics Driven Strategy
Case Study - Risk Management
Sources of AI Bias
Ethical Development and Deployment
The Moral and Social Implications of AI Deployment
Principles of Ethical Development
Principles of Ethical Deployment
Role of an AI Ethics Officer
Main Roles and Responsibilities
Guiding Development Teams
Creating Ethical Policies and Practices
Communicating with Stakeholders
Conducting Risk Assessments
Establishing Ethical Review Boards and Committees
Domain Three - AI and Data Governance, Privacy and Lifecycles
Core Foundational Concepts
Defining Data Governance
Case Study - Governance Application
Defining Data Privacy
Data Anonymization and De-identification
Defining Data Lifecycles
Regulations and Frameworks
Impact of Major Privacy Regulations
NIST AI Framework
NIST Risk Management Framework
Fedramp AI Guidance
US AI Regulations
International AI Regulations
Important Lifecycles for AI Ethics Officers
What is a Lifecycle?
Key Stages of the AI Lifecycle
NIST AI Lifecycle
Software Development Lifecycle (SDLC)
GenAI Lifecycle
AI Governance & Policy Lifecycle
Continuous Improvement & Learning Lifecycle
Data Governance and Ethical Data Use
Create and Enforce rules
Data Handling and Collection
Domain Four - AI Accountability, Transparency, and Explainability
The Need for Explainable AI (XAI)
Defining Explainable AI
Applying Explainable AI (Transparency)
Legal Frameworks that Establish Requirements
Interpretable vs Explainable vs. Non-Interpretable Models (White Box vs Black Box)
XAI Methods (LIME, SHAP, etc)
Assessing its fairness and reliability
Establishing Legal and Professional Accountability for AI Risks and Decisions
AI Center of Excellence
Risks Associated with AI
Managing Risks with AI
Legal Accountability
Professional Accountability
Code of Conduct
Cloud Shared Responsibility Model
AI Enabled Tools and Platforms for AI Ethics Officers
Choosing the Right Tools and Platforms
Bias Detection and Mitigation Tools
Explainable AI (XAI) Tools
Governance and Risk Management Platforms
Privacy and Security Tools
Domain Five - Global Regulatory and Policy Frameworks
Regulations and Frameworks
NIST AI Framework
ISO/IEC 27001
EU General Data Protection Regulation (GDPR)
EU Artificial Intelligence Act (EU AI Act)
Data Privacy Framework (DPF)
EU General Data Protection Regulation (GDPR)
California Consumer Privacy Act (CCPA)
Virginia Consumer Data Protection Act (CDPA)
Applying International Ethical Guidelines and Standards
Key international ethical frameworks
OECD AI Principles
UNESCO's Recommendation on the Ethics of Artificial Intelligence (2021):
EU Ethics Guidelines for Trustworthy AI (2019)
How to Apply These Principles
Balancing transparency and explainability with privacy and security
Strategies for balancing competing ethical demands
Risk Assessment and Governance
AI Risk Assessments
AI Model Evaluation
Conducting Ethical Risk Assessments
Establishing Ethical Review Boards and Committees
Ethical Charters
Crisis Management Plan
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