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1890 learning items

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Quiz

Scope, Extraterritorial Reach & Actor Classifications

Unofficial AIGP practice on the material and territorial scope of the EU AI Act and the roles of providers, deployers and supply-chain actors. Questions distinguish EU market or output connections from the location of servers, ownership of weights or a person’s nationality. Study the boundaries of scientific and premarket research exclusions, exclusive military or national-security purposes, personal nonprofessional use and free/open-source system treatment. Role questions connect importer checks, authorised-representative mandates and downstream changes with the relevant statutory conditions. The material uses the Commission’s text based on the 27 July 2026 consolidated Act and distinguishes scope from the deferred application of high-risk obligations. Basic familiarity with AI systems and the EU risk framework is helpful. Practise identifying the actor, use context and relevant provision before deciding which duties apply. This focused set supports IAPP AIGP BoK v2.1 Domain II.C, particularly differences in duties by organizational role. It is unofficial study practice and does not cover every sector-specific exception or implementation issue.

ai-governance

Oct 22, 2026

14 questions

Quiz

System Boundary Scoping, Context of Use & Threat Modeling

Unofficial AIGP practice on defining AI system scope, intended use and the environments in which model outputs will be relied upon. Questions connect architecture and data flows with threat assessment, foreseeable misuse, population changes and documentation. Practise distinguishing training-data attacks from inference-time attacks and choosing proportionate controls around retrieval, orchestration and tool interfaces. EU AI Act high-risk system provisions are considered alongside voluntary NIST guidance; applicability depends on the system and legal context. Basic familiarity with model training, inference and risk assessment is useful. Use the explanations to test assumptions and limits rather than memorising a single security architecture. This focused set supports selected development-governance competencies in the IAPP syllabus; it is not an official exam or complete syllabus simulation.

ai-governance

Oct 22, 2026

15 questions

Quiz

Sectoral AI Mandates: Healthcare, Financial Services & Critical Infrastructure

Unofficial AIGP practice on how healthcare, credit and operational-technology contexts shape AI governance. Healthcare questions examine FDA device classification and premarket pathways, authorized change-control plans, GMLP principles and HIPAA responsibilities for data used by service providers. Financial-services scenarios focus on meaningful adverse-action reasons under ECOA and Regulation B, checking explanation methods, and risk-based model validation. The set distinguishes binding requirements from supervisory guidance: SR 26-2 replaced SR 11-7 in April 2026, and the withdrawn CFPB circular is not used as the operative source of notice duties. Infrastructure questions address risk-based controls and the limits of NERC CIP applicability. Familiarity with AI data flows and lifecycle controls is helpful. Practise determining the relevant legal scope, evidence and responsible parties before approving a use or modification. The questions support relevant IAPP AIGP BoK v2.1 Domain II.A–B concepts and lifecycle governance. This is focused unofficial practice, not complete coverage of every regulated sector.

ai-governance

Oct 22, 2026

15 questions

Quiz

Product Liability, Strict Liability & Tort Law in AI Systems

Unofficial AIGP practice comparing negligence, strict product liability and evidence problems in AI-related civil claims. Questions examine software as a product, safety expectations, post-deployment learning, development-risk defenses and the roles of manufacturers and operators. The EU material distinguishes adopted Directive (EU) 2024/2853 from the AI Liability Directive proposal withdrawn in October 2025. Historical proposal questions are labelled accordingly. The revised PLD requires national implementation and has product-date limits; it should not be treated as a universal rule for every existing AI injury. A U.S. electronic-contracting example separates automated transaction attribution from tort liability. Basic familiarity with civil claims and the AI lifecycle is helpful. Practise identifying the legal regime, required evidence and limits of a proposed defense before assigning responsibility. This focused set supports IAPP AIGP BoK v2.1 Domain II.B; it is not official IAPP material or jurisdiction-specific legal advice.

ai-governance

Oct 22, 2026

14 questions

Quiz

Consumer Protection, Deceptive Practices & FTC Enforcement

Unofficial AIGP practice on U.S. consumer protection and AI governance. Review the distinction between unfairness and deception, evidence for objective AI marketing claims, consumer-facing chatbot design, privacy commitments and FTC enforcement remedies. The questions use facial-recognition and children’s-data settlements to connect legal duties with testing, oversight and data-governance decisions. Remedies and consent-order safeguards depend on their statutory basis and case-specific terms; they are not universal mandates for every AI product. Useful preparation includes basic familiarity with the FTC Act and AI data flows. Practise identifying unsupported representations, broken privacy promises and governance failures. This set supports the consumer-protection elements of IAPP AIGP BoK v2.1 Domain II.B and related privacy considerations in II.A. It is a focused study aid, not an official IAPP exam or a complete legal compliance guide.

ai-governance

Oct 22, 2026

13 questions

Quiz

Anti-Discrimination, Equal Opportunity & Algorithmic Employment Laws

Employment decisions, equality and automated tools This unofficial AIGP practice set examines legal risks in algorithmic recruitment and worker management. Its US questions distinguish intentional disparate treatment from disparate impact, apply a simplified four-fifths calculation and consider employer responsibility, job-relatedness, business necessity and disability accommodation. The NYC items address covered automated employment decision tools, independent audits, advance notices and the metrics used for selection and scoring systems. They distinguish an audit’s procedural requirements from a determination that a tool is free of discrimination. The EU classification question examines an employment use under the AI Act and separates high-risk status from actor-specific duties. A warehouse scenario asks learners to identify potentially applicable data-protection and employment issues without assuming every legal regime applies everywhere. Study approach Review basic selection rates, protected grounds, qualified applicants and controller responsibilities before attempting the set. Practise identifying the applicable jurisdiction and legal test, then separate statistical warning signs from a final legal conclusion. Vendor assurances, technical accuracy and the existence of an audit do not replace assessment of the actual employment practice. The set supports selected Domain II.B and related II.C competencies in the IAPP AIGP Body of Knowledge version 2.1. It is independent practice material. Check current rules, contractual roles and the facts of each deployment when applying these concepts outside a study setting.

ai-governance

Oct 22, 2026

13 questions

Quiz

Copyrightability, Ownership & Patentability of AI Outputs

Rights in AI-assisted outputs This unofficial AIGP practice set examines US copyright authorship, patent inventorship and contractual allocation of rights in AI-assisted work. It distinguishes qualifying human expression from machine-generated elements and considers the scope of protection for original editing, selection and arrangement. Questions use the Zarya of the Dawn registration decision, the human-authorship principle and DABUS proceedings in the US, UK and EPO to frame jurisdiction-specific issues. The inventorship item uses the USPTO’s revised November 2025 guidance, which replaced its February 2024 approach. Other questions address the limits of output assignments, potential downstream infringement, a named vendor’s conditional defence commitment, identity-related claims and code-provenance controls. Preparing and applying Basic copyright concepts, patent terminology and familiarity with generative-AI workflows are helpful prerequisites. Practise separating authorship from ownership, inventorship from patentability, and contractual protection from third-party clearance. A generated output’s lack of new copyright does not establish that it is safe to publish or exclusively available. The set supports selected intellectual-property competencies in Domain II.B of the IAPP AIGP Body of Knowledge version 2.1. It is independent study material. Legal results depend on jurisdiction and facts, and contractual commitments must be checked against their actual product scope, conditions and exclusions.

ai-governance

Oct 22, 2026

12 questions

Quiz

Intellectual Property & Copyright in AI Training Inputs

Intellectual property in AI training inputs This unofficial AIGP practice set explores how copyright, licensing and trade-secret rules affect the acquisition and use of AI training material. It compares the EU’s scientific-research and general text-and-data-mining exceptions, rights reservations and GPAI copyright-policy and training-content-summary obligations. The US questions examine the statutory fair-use factors, the limited relevance of the Google Books decision to other technologies, and the distinction between direct and secondary infringement theories. Additional questions address confidential technical information, code from copyleft repositories, third-party dataset provenance and the limits of synthetic data as a risk-reduction technique. Study approach Familiarity with copyright ownership, licences, controllers and the AI training lifecycle is helpful. Practise identifying the jurisdiction, the protected material, the act being performed and the precise permission or exception relied on. Keep a dataset’s public availability separate from authorization to train, and distinguish a legal requirement from a practical diligence control. The set supports selected intellectual-property competencies in Domain II.B of the IAPP AIGP Body of Knowledge version 2.1, with related data-governance context. It is independent practice material. It does not treat every training use as lawful or unlawful, or imply that a transparency summary, synthetic-data label or open-source source automatically resolves copyright questions.

ai-governance

Oct 22, 2026

13 questions

Quiz

Cross-Border Data Transfers, Cloud Compute & Model Weights

International personal-data flows in AI systems This unofficial AIGP practice set examines how the EU GDPR’s international-transfer rules apply to training datasets, prompts, model parameters and outputs. Learners identify the relevant personal data, exporter, separate recipient and destination before selecting or assessing a transfer mechanism. The questions distinguish adequacy decisions, transfer Standard Contractual Clauses, Binding Corporate Rules and the exceptional routes under Article 49. They explore destination-country assessments, conditional supplementary measures and the limits of encryption when a cloud provider requires cleartext access. Model-weight exports are assessed through the possibility of identifying individuals, rather than through the file’s numerical format. Using the set Basic familiarity with controllers, processors, personal data and GDPR lawful bases is useful. Practise separating a transfer mechanism from the remaining privacy and security duties, and check actual flows rather than relying on cloud-region or sovereignty labels. The localization scenario stipulates a particular restriction; it does not present an EEA-only architecture as a universal sector requirement. This set supports selected Domain II.A competencies in the IAPP AIGP Body of Knowledge version 2.1. It is independent study material. Verify current adequacy status, recipient coverage and the facts of a deployment before applying these concepts to an operational decision.

ai-governance

Oct 22, 2026

13 questions

Quiz

Automated Individual Decision-Making & Profiling (GDPR Article 22)

Automated decisions and profiling under the EU GDPR This unofficial AIGP practice set examines when automated processing becomes a decision with legal or similarly significant effects. It distinguishes profiling from the narrower Article 22 prohibition and explores how meaningful human involvement differs from routine approval of machine recommendations. Questions cover the statutory exceptions, the safeguards associated with contractual necessity and explicit consent, and the additional restrictions affecting special-category data. Employment, credit and statutory-benefit examples help learners apply the significance threshold. A third-party credit-scoring question introduces the relevance of the CJEU’s SCHUFA ruling. Study approach Review basic GDPR concepts such as personal data, controllers, lawful basis and data-subject rights before attempting the set. Practise separating the conditions permitting a decision from the safeguards needed when it is permitted. The transparency items address information about automated processing and its consequences, while the oversight comparison distinguishes GDPR intervention rights from the EU AI Act’s high-risk-system requirements. The set supports selected Domain II privacy-law competencies in the IAPP AIGP Body of Knowledge version 2.1. It is independent practice material, rather than official IAPP content or a complete legal compliance assessment. Apply the relevant jurisdiction, facts and current rules when studying a real deployment.

ai-governance

Oct 22, 2026

13 questions

Quiz

Data Subject Rights in AI Pipelines & Machine Unlearning

Unofficial AIGP practice on EU GDPR rights in AI pipelines: access, rectification, erasure, objection and restriction, with model identifiability and technical response limits. Compare exact and approximate unlearning, SISA, retrieval and modular approaches, and privacy-attack evidence. Requires basic GDPR and machine-learning knowledge. No particular architecture guarantees legal compliance or erasure in every case.

ai-governance

Oct 22, 2026

15 questions

Quiz

Core Data Protection Principles Applied to Machine Learning

Unofficial AIGP practice on EU GDPR principles in machine learning: purpose compatibility, minimization, retention, personal-data accuracy and protection by design and default. Distinguish legal principles from model performance and from the limited guarantees of privacy technologies. Basic GDPR and machine-learning familiarity is helpful.

ai-governance

Oct 22, 2026

15 questions