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Study Notes

A Level - Exam Preparation

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› Course

› GenAI Ops

› 1. Optimizing and Governing AI Systems

· 1. Dashboard Design for AI Cohort Monitoring

· 2. Statistical Drift Detection

· 3. Statistical Drift Detection v2

· 4. Prompt Monitoring — Cohort Metrics

› 2. MTTR Analysis and Operational Resilience

· 1. Technical Architecture Framework

· 2. Cost-Benefit Analysis Methods

· 3. Building Decision Matrices

· 4. Implementation Strategies

· 5. Dialog Review — Fine-Tuning vs RAG

· 6. Assignment

› 3. Create Governance Frameworks

· 1. Why AI Governance Determines Enterprise Success

· 2. Comprehensive AI Governance Framework Components

· 3. Designing Technical Guardrails

· 4. Policy Development and Implementation

· 5. Dialog Review — Stakeholder Dynamics

› 4. Ethical AI Decision-Making and Bias Mitigation

· 1. When AI Bias Becomes Business Risk

· 2. Quantifying Bias and Fairness

· 3. Enterprise AI Risk Management

· 4. Fairness Assessment Tools

› 5. Strategic AI Roadmap Alignment

· 1. Strategic Alignment and Business Value

· 2. Mapping AI Initiatives to Business Objectives

· 3. Systematic Approaches to Assessing Strategic AI Roadmaps

· 4. Using Strategic Alignment Tools to Assess AI Initiatives

· 5. ai-roadmap-analysis-assignment

· 6. AI Roadmap Assignment - Response

· 7. Strategic AI Roadmap Assessment - Summary

› 6. Building AI Centers of Excellence

· 1. From Scattered AI Experiments to Strategic Excellence

· 2. Governance Frameworks for AI Operations at Scale

· 3. Essential Elements of Effective AI Governance Charters

› 7. Analyze Model Complexiity vs Interpretability Trade off

· 1. Why Model Interpretability Can Make or Break Your ML Career

· 2. The Strategic Framework for Complexity-Interpretability Decisions

· 3. Production Trade-off Analysis Framework and Methods

· 4. Hands-on Trade-off Analysis with Production Constraints

› 8. Evaluating Algorithm Performance with Statistical Goal

· 1. Statistical Significance Testing Prevents Million-Dollar Mistakes

· 2. Statistical Testing Foundations for Production ML

· 3. Implementing Statistical Tests for Algorithm Comparison

· 4. Hands-on Statistical Testing Implementation in Python

› 9. Creating Ensemble Models by Combining Mutipe Algorithm

· 1. Netflix Combines 107+ Algorithms Into Billion-Dollar Ensembles

· 2. Ensemble Architecture Fundamentals for Production Systems

· 3. Building Production Ensemble Systems from Scratch

› 10. Feature Importance & Bias Analysis

· 1. Model Interpretability Determines Trust and Fairness

· 2. Understanding SHAP and LIME for Feature Importance

· 3. Detecting and Measuring Bias in ML Models

· 4. Generating SHAP Plots and Interpreting Feature Contributions

› AIGP

› Study Set A

› Domain 1 (Foundation of AI Governance)

· 1. What AI Is and Why It Needs Governance

· 2. Types of AI Systems for Governance

· 3. Foundation Models and Multimodal AI

· 4. Training Paradigms and Governance

· 5. Inference Architecture and Agentic AI

· 6. AI Risk Categories -- Risk Officer's Map

· 7. Likelihood, Impact, and Residual Scoring

· 8. Appetite, Tolerance, Treatment Authority

· 9. Risk Treatment -- The Right Option

· 10. Risk Aggregation, KRIs, Cross-Domain

· 11. Seven Principles -- Risk Officer's Map

· 12. Fairness and Transparency -- Risk Events

· 13. Accountability, Oversight, Harm -- Risks

· 14. Privacy, Autonomy, Ethics Risk -- Tracking

· 15. AI Governance Roles: Risk Officer View

· 16. 3LoD for AI: Design and Independence

· 17. Policy Hierarchy: Enterprise to Operations

· 18. Governance Design: Three Program Models

· 19. Governance KRIs and Maturity Assessment

· 20. AI Lifecycle: A Risk Officer's Map

· 21. Stages 1-2: Problem Framing and Data Risk

· 22. Stages 3-4: Development and Testing Risk

· 23. Stages 5-6: Deployment and Monitoring Risk

· 24. Stage 7: Lifecycle-Level Risk Aggregation

· 25. Policy Hierarchy as Risk Treatment

· 26. Policy Components as Risk Treatment

· 27. AI Policy Types: Risk Domain Coverage

· 28. Training and Review as Risk Maintenance

· 29. The AI AUP as a Risk Control

· 30. AUP Content Design: Tiers, Prohibitions

· 31. AUP Control Effectiveness: Training

· 32. AUP Incident Classification and Metrics

· 33. D1 Capstone: Ethics Committee Lab

· 34. D1 Capstone: Public Sector AI Policy

› Domain 2 (Law and Standard)

· 1. GDPR Principles: Privacy Risk Foundations

· 2. Article 22 and DSRs: Governance and Risk

· 3. Privacy by Design and DPIAs: Program

· 4. Cross-Border Transfers and US Sector Laws

· 5. US Federal AI Obligations

· 6. Sector Regulations as Compliance Risk

· 7. Consumer Protection and Employment AI

· 8. International Frameworks: Program Design

· 9. EU AI Act: The Risk Classification Ladder

· 10. Arts. 9 Through 15: Risk Control Map

· 11. Roles, GPAI, Timeline, and Penalty Tiers

· 12. NIST AI RMF Overview for Risk Officers

· 13. GOVERN -- Building the Risk Foundation

· 14. MAP -- Context and Categorizing AI Risks

· 15. MEASURE -- Analyzing and Benchmarking AI

· 16. MANAGE -- Sustaining the Risk Program

· 17. ISO 42001 as a Governance Framework

· 18. Risk Planning, Objectives, and Leadership

· 19. Operational Controls and Annex A

· 20. Performance Evaluation and Crosswalks

· 21. D2 Capstone: Cross-Border AI Governance

· 22. D2 Capstone: Multi-Framework Alignment

› Domain 3 (Govern+Development)

· 1. Transparency, XAI, and Governance

· 2. XAI Methods as Governance Design Choices

· 3. Regulatory Drivers for XAI Obligations

· 4. Governance Program Design for XAI

· 5. AI Bias Taxonomy: Risk and Governance Map

· 6. Fairness Metrics and Impossibility

· 7. Bias Detection and Mitigation Program

· 8. Legal Exposure by Sector: Compliance

· 9. Data Risk as a Risk Class

· 10. Data Quality KRIs and Classification Risk

· 11. Lineage as Risk Evidence

· 12. Provenance and Third-Party Data Risk

· 13. Catalog, Retention, and Access Risk

· 14. The AI Testing Pyramid

· 15. Data Splitting and Leakage Risks

· 16. Performance Metrics by Task Type

· 17. Bias Testing, Red-Teaming, and Monitoring

· 18. SR 11-7 and the MRM Framework

· 19. Model Inventory Design

· 20. Model Validation: SR 11-7 Dimensions

· 21. AI-Specific MRM Challenges

· 22. MRM Governance: Three Lines and MRC

· 23. The Human-AI Control Spectrum

· 24. Escalation Trigger Design

· 25. Oversight Mechanisms

· 26. Cognitive Failure Modes in HITL

· 27. Regulatory Anchors for HITL

· 28. AI IR Through the Risk Lens

· 29. Detection to Declaration: KRI Breach

· 30. Containment as Risk Treatment

· 31. Regulatory Notification: Governance

· 32. BCP as Risk Continuity

· 33. D3 Capstone: AI Privacy Impact Assessment

· 34. D3 Capstone: AI Compliance Program

› Domain 4 (Govern+Deployment)

· 1. AI Vendor Risk Lifecycle: Risk Officer Map

· 2. Pre-Contract Due Diligence: AI Vendor Risk

· 3. AI Contract Requirements: Risk Coverage

· 4. Assurance, Monitoring, and EU AI Act

· 5. Procurement as a Risk Gate

· 6. Vendor Risk Register and Needs Assessment

· 7. RFP, Evaluation Matrix, and POC

· 8. Governance Tiers and Concentration Risk

· 9. Contract Clauses, Exit Risk, Re-assessment

· 10. Change as Risk Vector: ADKAR for Risk

· 11. CAB: The Risk Function's Formal Seat

· 12. Pre-Deployment Risk Gates and KRIs

· 13. Rollback as Risk Treatment for AI Systems

· 14. Stakeholder Mapping for AI Change Risk

· 15. KRI vs. KPI: Designing AI Risk Indicators

· 16. AI-Specific KRI Catalog: What to Measure

· 17. Reporting Tiers and Cadence

· 18. Escalation, Visualization, and Frameworks

· 19. AI Security Threats as Risk Events

· 20. Prompt Injection: Risk Treatment Decisions

· 21. Data Poisoning and Adversarial Risk Events

· 22. Extraction, Inversion, Supply Chain Risk

· 23. Defensive Controls as Risk Treatment

· 24. Trust, Safety, Security: Governance Lens

· 25. Hardcoded vs Softcoded: Governance Design

· 26. Safety-Critical Domains: Obligations

· 27. Failure Modes and Residual Risk

· 28. D4 Capstone: AI Deployment Governance

· 29. D4 Capstone: Misuse and Deactivation

· 30. D1/D2 Cross-Domain Patterns

· 31. D3/D4 Cross-Domain Patterns

› Study Set B

· AI Study Guide

· GDPR Article Reference

› Private Study

› Statistics & ML

· Proportions, SE, CIs, Hypothesis Tests, Cohen's d & Classification Metrics

· Deep-Study Stats Tutor Prompt

· Model Evaluation — Overview

· Logistic Regression — Classification & Decision Boundaries

· Decision Tree – Deep Study Note

· Random Forest – Deep Study Note

· Logistic Regression — Classification & Decision Boundaries

· Logistic Regression & Gradient Descent

· Regularization Techniques

· Decision Boundary

· Stacking — Ensemble Learning

· SVM: Margin, Soft Margin & Kernels

· Boosting Algorithms — Step by Step

· Ensemble Methods — Voting & Stacking

› Study

· Chemistry

· Math

· Physics

› OpenClaw

· CLI Commands

· Docker CLI Commands

· Slash Commands

· Event Processing

· Conversation Q&A v2

· Conversation Q&A v3

· Conversation Summary

› AI Next Era

· Modern AI Agents — From Concepts to Working Systems

A Level - Exam Preparation

Study notes organised by subject.