IAPP AIGP Cheat Sheet

Cheat sheet IAPP AIGP Cheat Sheet Practice Questions Study Guide Print / Save PDF

Foundations of AI Governance 19-24% of exam AI + ML Types Responsible AI Governance Roles Why Govern AI Lifecycle Policies

Laws, Standards + Frameworks 22-27% of exam EU AI Act Tiers Frameworks + Standards Laws Applying to AI GDPR + AI OECD + NIST

Governing AI Development 25-29% of exam AI Lifecycle Development Controls Data Governance Impact Assessment Testing + Red Team

Governing Deployment + Use 25-29% of exam Deployment Governance Monitoring + Oversight Vendor Review Human Oversight Post-market

Quick Facts Exam AIGP Credential AI Governance Professional Questions 100 (85 scored) Time 2 hr 45 min Pass 300/500 scaled Fee $649 / $799 Format MCQ, Pearson VUE Blueprint Feb 2 2026

Responsible AI Pillars FO Fair Accountable Transparent Safe Explainable Robust Private Fair: no unjust bias Accountable: named owner Transparent: disclose Safe: reliable Explainable: reasons Robust: resilient Private: protect data

Provider vs Deployer FO

Provider Develops system Places on market Most obligations

Deployer Uses the system Under own authority Operational duties Builds vs uses

AI + ML Types FO AI Machines mimic cognition ML Learns from data Supervised Labeled training data Unsupervised Finds hidden patterns Reinforcement Reward-based learning Deep learning Layered neural networks Generative AI Creates new content GPAI Broad general-purpose model

Supervised vs Unsupervised FO

Supervised Labeled data Known outputs Classify, predict

Unsupervised Unlabeled data Find patterns Cluster, group Labeled vs unlabeled

Responsible AI FO Fairness Avoid unjust bias Accountability Named responsible owners Transparency Disclose AI use Explainability Reasons for decisions Safety Reliable, secure operation Human-centric Serves people Robustness Withstands errors, attacks Privacy Protect personal data

Generative vs Discriminative FO

Generative Creates new content Learns distribution LLMs, images

Discriminative Classifies inputs Draws boundaries Spam, fraud Creates vs classifies

Governance Roles FO Provider Develops, places system Deployer Uses under authority Developer Builds the model Distributor Makes available downstream Importer Brings into market User Operates the system Subject Person affected

Transparency vs Explainability FO

Transparency Disclose AI use Open about system Users informed

Explainability Reasons for output Interpretable decisions Why this result Disclose vs explain

Why Govern AI FO Probabilistic Outputs not deterministic Opacity Black-box reasoning Drift Performance degrades over time Bias Skewed, unfair outputs Autonomy Acts without humans Scale Harms multiply fast

NIST AI RMF Functions LA Govern | Map | Measure | Manage Govern: culture + policy Map: context + risks Measure: analyze + track Manage: prioritize + act

NIST RMF vs ISO 42001 LA

NIST AI RMF Voluntary framework Govern Map Measure Manage Flexible guidance

ISO/IEC 42001 Certifiable standard Management system Auditable AIMS Framework vs standard

EU AI Act Tier Picker LA Manipulates, exploits people → Unacceptable ( Banned ) Government social scoring → Unacceptable ( Banned ) Hiring, credit, biometrics → High-risk ( Strict duties ) Chatbot or deepfake → Limited ( Disclose AI ) Spam filter, game AI → Minimal ( No rules ) Foundation model → GPAI ( Model duties )

EU AI Act Tiers LA Unacceptable Banned practices Prohibited High-risk Strict obligations Limited Transparency duties Minimal No extra rules GPAI General-purpose obligations Banned examples Social scoring, manipulation

EU AI Act Tiers LA Unacceptable | High | Limited | Minimal Unacceptable: banned High: strict duties Limited: transparency Minimal: free

Framework Picker LA Build AI management system → ISO/IEC 42001 Voluntary risk process → NIST AI RMF Define AI terms → ISO/IEC 22989 Run AI impact assessment → ISO/IEC 42005 High-level policy principles → OECD Principles AI risk guidance → ISO/IEC 23894

Frameworks + Standards LA NIST AI RMF Voluntary risk framework ISO/IEC 42001 AI management system ISO/IEC 22989 AI terminology ISO/IEC 23894 AI risk guidance ISO/IEC 42005 AI impact assessment OECD AI principles

Laws Applying to AI LA Privacy law GDPR, personal data IP + copyright Training data rights Anti-discrimination No biased outcomes Consumer protection No deceptive claims Product liability Harm from defects Employment law Hiring, screening rules

GDPR + AI LA Lawful basis Required before processing Purpose limitation Stated use only Data minimization Collect least needed DPIA Privacy impact assessment Privacy by design Built-in protection Article 22 Automated decision rights

AI Impact vs DPIA DE

AI Impact Assessment Broad AI harms ISO/IEC 42005 Whole lifecycle

DPIA Privacy risks only GDPR Article 35 Personal data AI harms vs privacy

Lifecycle Control Picker DE Starting a project → Impact assessment Choosing training data → Data governance Before release → Model card Stress-test for harm → Red teaming After deployment → Post-market monitoring Performance decays → Drift detection

AI Lifecycle DE Use-case Define problem, scope Design Architecture, requirements Data Source, prepare, govern Train Fit the model Test Validate, evaluate Release Readiness decision Deploy Put into use Monitor Track live performance

Development Controls DE Impact assessment Identify harms early Data provenance Origin of data Model card Documents model details Red teaming Adversarial testing Threat modeling Map attack paths Bias testing Measure disparate impact

Data Governance DE Provenance Where data originated Lineage How data transformed Quality Accurate, complete, current Fit for purpose Relevant, representative Lawful rights Permission to use Labeling Accurate ground truth

HITL vs HOTL DE

HITL Human approves action In the loop Blocks bad output

HOTL Human monitors On the loop Intervenes if needed Approve vs monitor

Deployment Governance DE Deployment decision Go / no-go Vendor review Third-party due diligence Deployment controls Guardrails, usage limits Post-market monitoring Watch after launch Incident response Handle failures Deactivation Safe shutdown plan

Monitoring + Oversight DE Human oversight People can intervene HITL Human in loop HOTL Human on loop Drift detection Catch model decay Logging Record decisions, events Audit trail Traceable accountability Feedback User, subject reporting

Common Traps

Provider vs Deployer Provider builds, places ≠ Deployer uses it

NIST vs EU AI Act NIST is voluntary ≠ EU AI Act binding

Transparency vs Explainability Transparency = disclosure ≠ Explainability = reasoning

ISO 42001 vs 42005 42001 = management system ≠ 42005 = impact assessment

Bias vs Fairness Bias = the skew ≠ Fairness = the goal

GPAI vs High-risk GPAI = model tier ≠ High-risk = use tier

Last Minute 1 . Pass = 300 of 500 2 . 100 questions, 85 scored 3 . 2 hr 45 min appointment 4 . Provider builds; deployer uses 5 . EU tiers: unacceptable to minimal 6 . RMF = Govern Map Measure Manage 7 . ISO 42001 = AI management system 8 . NIST voluntary; EU AI Act binding 9 . Supervised = labeled; unsupervised = patterns 10 . Transparency = disclose; explainability = why 11 . DPIA = privacy impact only 12 . Human oversight always required

AIGP Practice Questions 200 questions Study Guide 20 topics Cheat Sheet 86 facts Flashcards 50 cards Videos 1 video Blogs 1 blog Free CE Podcast

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