Useful Study Guide & Exam Questions to Pass the IAPP AIGP Exam.pdf

Useful Study Guide &
Exam Questions to Pass
the IAPP AIGP Exam
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Here are all the necessary details to pass the AIGP exam on your first attempt.
Get rid of all your worries now and find the details regarding the syllabus,
study guide, practice tests, books, and study materials in one place. Through
the AIGP certification preparation, you can learn more on the IAPP Certified
Artificial Intelligence Governance Professional, and getting the IAPP Certified
Artificial Intelligence Governance Professional (AIGP) certification gets easy.

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AIGP: IAPP Certified Artificial Intelligence Governance Professional 1
How to Earn the IAPP AIGP Certification on Your
First Attempt?
Earning the IAPP AIGP certification is a dream for many candidates. But, the
preparation journey feels difficult to many of them. Here we have gathered all the
necessary details like the syllabus and essential AIGP sample questions to get to the
IAPP Certified Artificial Intelligence Governance Professional (AIGP) certification on the
first attempt.
AIGP Artificial Intelligence Governance Professional
Summary:
● Exam Name: IAPP Certified Artificial Intelligence Governance Professional
(AIGP)
● Exam Code: AIGP
● Exam Price:
○ First Time Member - $649
○ Non-Member - $799
○ Retake Member - $475
○ Non-Member - $625 (USD)
● Duration: 180 mins

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● Number of Questions: 100
● Passing Score: 300 / 500
● Books / Training:
○ AIGP Body of Knowledge and Exam Blueprint
○ AIGP Handbook
● Schedule Exam: Pearson VUE
● Sample Questions: IAPP AIGP Sample Questions
● Recommended Practice: IAPP AIGP Certification Practice Exam
Let’s Explore the IAPP AIGP Exam Syllabus in Detail:
Topic Details
Understanding the Foundations of Artificial Intelligence
Understand the basic
elements of AI and ML
- Understand widely accepted definitions of AI and ML, and
the basic logical-mathematical principles over which AI/ML
models operate.
- Understand common elements of AI/ML definitions under
new and emerging law:
 Technology (engineered or machine-based system;
or logic, knowledge, or learning algorithm).
 Automation (elements of varying levels).
 Role of humans (define objectives or provide data).
 Output (content, predictions, recommendations, or
decisions).
- Understand what it means that an AI system is a socio-
technical system.
- Understand the need for cross-disciplinary collaboration
(ensure UX, anthropology, sociology, linguistics experts
are involved and valued).
- Knowledge of the OECD framework for the classification
of AI systems.
- Understand the use cases and benefits of AI (recognition,
event detection, forecasting, personalization, interaction
support, goal-driven optimization, recommendation).
Understand the differences
among types of AI systems
- Understand the differences between strong/broad and
weak/narrow AI.
- Understand the basics of machine learning and its
training methods (supervised, unsupervised, semi-

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Topic Details
supervised, reinforcement).
- Understand deep learning, generative AI, multi-modal
models, transformer models, and the major providers.
- Understand natural language processing: text as input
and output.
- Understand the difference between robotics and robotic
processing automation (RPA).
Understand the AI
technology stack
- Platforms and applications.
- Model types.
- Compute infrastructure: software and hardware (servers
and chips).
Understand the history of AI
and the evolution of data
science
- 1956 Dartmouth summer research project on AI.
- Summers, winters and key milestones.
- Understand how the current environment is fueled by
exponential growth in computing infrastructure and tech
megatrends (cloud, mobile, social, IOT, PETs, blockchain,
computer vision, AR/VR, metaverse).
Understanding AI Impacts on People and Responsible AI Principles
Understand the core risks
and harms posed by AI
systems
- Understand the potential harms to an individual (civil
rights, economic opportunity, safety).
- Understand the potential harms to a group (discrimination
towards sub-groups).
- Understand the potential harms to society (democratic
process, public trust in governmental institutions,
educational access, jobs redistribution).
- Understand the potential harms to a company or
institution (reputational, cultural, economic, acceleration
risks).
- Understand the potential harms to an ecosystem (natural
resources, environment, supply chain).
Understand the
characteristics of
trustworthy AI systems
- Understand what it means for an AI system to be
″human-centric.″
- Understand the characteristics of an accountable AI
system (safe, secure and resilient, valid and reliable, fair).
transparent.

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Topic Details
explainable.
- Understand what it means for an AI system to be privacy-
enhanced.
Understand the similarities
and differences among
existing and emerging
ethical guidance on AI
- Understand how the ethical guidance is rooted in Fair
Information Practices, European Court of Human Rights
and Organization for Economic Cooperation and
Development principles.
- OECD AI Principles; White House Office of Science and
Technology Policy Blueprint for an AI Bill of Rights; High-
level Expert Group AI; UNESCO Principles; Asilomar AI
Principles; The Institute of Electrical and Electronics
Engineers Initiative on Ethics of Autonomous and
Intelligent Systems; CNIL AI Action Plan.
Understanding How Current Laws Apply to AI Systems
Understand the existing
laws that interact with AI
use
- Know the laws that address unfair and deceptive
practices.
- Know relevant non-discrimination laws (credit,
employment, insurance, housing, etc.).
- Know relevant product safety laws.
- Know relevant IP law.
- Understand the basic requirements of the EU Digital
Services Act (transparency of recommender systems).
- Know relevant privacy laws concerning the use of data.
Understanding key GDPR
intersections
- Understand automated decision making, data protection
impact assessments, anonymization, and how they relate
to AI systems.
- Understand the intersection between requirements for AI
conformity assessments and DPIAs.
- Understand the requirements for human supervision of
algorithmic systems.
- Understand an individual’s right to meaningful information
about the logic of AI systems.
Understanding liability
reform
- Awareness of the reform of EU product liability law.
- Understand the basics of the AI Product Liability
Directive.
- Awareness of U.S. federal agency involvement
(EO14091).

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Topic Details
Understanding the Existing and Emerging AI Laws and Standards
Understanding the
requirements of the EU AI
Act
- Understand the classification framework of AI systems
(prohibited, high-risk, limited risk, low risk).
- Understand requirements for high-risk systems and
foundation models.
- Understand notification requirements (customers and
national authorities).
- Understand the enforcement framework and penalties for
noncompliance.
- Understand procedures for testing innovative AI and
exemptions for research.
- Understand transparency requirements, i.e., registration
database.
Understand other emerging
global laws
- Understand the key components of Canada’s Artificial
Intelligence and Data Act (C-27).
- Understand the key components of U.S. state laws that
govern the use of AI.
- Understand the Cyberspace Administration of China’s
draft regulations on generative AI.
Understand the similarities
and differences among the
major risk management
frameworks and standards
- ISO 31000:2018 Risk Management – Guidelines.
- United States National Institute of Standards and
Technology, AI Risk Management Framework (NIST AI
RMF).
- European Union proposal for a regulation laying down
harmonized rules on AI (EU AIA).
- Council of Europe Human Rights, Democracy, and the
Rule of Law Assurance Framework for AI Systems
(HUDERIA).
- IEEE 7000-21 Standard Model Process for Addressing
Ethical Concerns during System Design
- ISO/IEC Guide 51 Safety aspects – guidelines for their
inclusion in standards.
Singapore Model AI Governance Framework.
Understanding the AI Development Life Cycle
Understand the key steps in
the AI system planning
- Determine the business objectives and requirements.
- Determine the scope of the project.

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Topic Details
phase - Determine the governance structure and responsibilities.
the AI system design phase
- Implement a data strategy that includes:
 Data gathering, wrangling, cleansing, labeling.
 Applying PETs like anonymization, minimization,
differential privacy, federated learning.
- Determine AI system architecture and model selection
(choose the algorithm according to the desired level of
accuracy and interpretability).
the AI system development
phase
- Build the model.
- Perform feature engineering.
- Perform model training.
- Perform model testing and validation.
the AI system
implementation phase
- Perform readiness assessments.
- Deploy the model into production.
- Monitor and validate the model.
- Maintain the model.
Implementing Responsible AI Governance and Risk Management
Ensure interoperability of AI
risk management with other
operational risk strategies
- Ex. security risk, privacy risk, business risk.
Integrate AI governance
principles into the company
- Adopt a pro-innovation mindset.
- Ensure governance is risk-centric.
- Ensure planning and design is consensus-driven.
- Ensure team is outcome-focused.
- Adopt a non-prescriptive approach to allow for intelligent
self-management.
- Ensure framework is law-, industry-, and technology-
agnostic.
Establish an AI governance
infrastructure
- Determine if you are a developer, deployer (those that
make an AI system available to third parties) or user;
understand how responsibilities among companies that
develop AI systems and those that use or deploy them
differ; establish governance processes for all parties;
establish framework for procuring and assessing AI
software solutions.

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Topic Details
- Establish and understand the roles and responsibilities of
AI governance people and groups including, but not limited
to, the chief privacy officer, the chief ethics officer, the
office for responsible AI, the AI governance committee, the
ethics board, architecture steering groups, AI project
managers, etc.
- Advocate for AI governance support from senior
leadership and tech teams by:
 Understanding pressures on tech teams to build AI
solutions quickly and efficiently.
 Understanding how data science and model
operations teams work.
 Being able to influence behavioral and cultural
change.
- Establish organizational risk strategy and tolerance.
- Develop central inventory of AI and ML applications and
repository of algorithms.
- Develop responsible AI accountability policies and
incentive structures.
- Understand AI regulatory requirements.
- Set common AI terms and taxonomy for the organization.
- Provide knowledge resources and training to the
enterprise to foster a culture that continuously promotes
ethical behavior.
- Determine AI maturity levels of business functions and
address insufficiencies.
- Use and adapt existing privacy and data governance
practices for AI management.
- Create policies to manage third party risk, to ensure end-
to-end accountability.
- Understand differences in norms/expectations across
countries.
Map, plan and scope the AI
project
- Define the business case and perform cost/benefit
analysis where trade-offs are considered in the design of
AI systems. Why AI/ML?
- Identify and classify internal/external risks and
contributing factors (prohibitive, major, moderate).
- Construct a probability/severity harms matrix and a risk
mitigation hierarchy.

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Topic Details
- Perform an algorithmic impact assessment leveraging
PIAs as a starting point and tailoring to AI process. Know
when to perform and who to involve.
- Establish level of human involvement/oversight in AI
decision making.
- Conduct a stakeholder engagement process that includes
the following steps:
 Evaluate stakeholder salience.
 Include diversity of demographics, disciplines,
experience, expertise and backgrounds.
 Perform positionality exercise.
 Determine level of engagement.
 Establish engagement methods.
 Identify AI actors during design, development, and
deployment phases.
 Create communication plans for regulators and
consumers that reflect compliance/disclosure
obligations for transparency and explainability (UI
copy, FAQs, online documentation, model or system
cards).
- Determine feasibility of optionality and redress.
- Chart data lineage and provenance, ensuring data is
representative, accurate and unbiased. Use statistical
sampling to identify data gaps.
- Solicit early and continuous feedback from those who
may be most impacted by AI systems.
- Use test, evaluation, verification, validation (TEVV)
process.
- Create preliminary analysis report on risk factor and
proportionate management.
Test and validate the AI
system during development
- Evaluate the trustworthiness, validity, safety, security,
privacy and fairness of the AI system using the following
methods:
 Use edge cases, unseen data, or potential malicious
input to test the AI models.
 Conduct repeatability assessments.
 Complete model cards/fact sheets.

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Topic Details
 Create counterfactual explanations (CFEs).
 Conduct adversarial testing and threat modeling to
identify security threats.
 Refer to OECD catalogue of tools and metrics for
trustworthy AI.
 Establish multiple layers of mitigation to stop system
errors or failures at different levels or modules of the
AI system.
 Understand trade-offs among mitigation strategies.
- Apply key concepts of privacy-preserving machine
learning and use privacy-enhancing technologies and
privacy-preserving machine learning techniques to help
with privacy protection in AI/ML systems.
- Understand why AI systems fail. Examples include:
brittleness;hallucinations; embedded bias; catastrophic
forgetting; uncertainty; false positives.
- Determine degree of remediability of adverse impacts.
- Conduct risk tracking to document how risks may change
over time.
- Consider, and select among different deployment
strategies.
Manage and monitor AI
systems after deployment
- Perform post-hoc testing to determine if AI system goals
were achieved, while being aware of ″automation bias.″
- Prioritize, triage and respond to internal and external
risks. Ensure processes are in place to deactivate or
localize AI systems as necessary (e.g., due to regulatory
requirements or performance issues).
- Continuously improve and maintain deployed systems by
tuning and retraining with new data, human feedback, etc.
- Determine the need for challenger models to supplant the
champion model.
- Version each model and connect them to the data sets
they were trained with.
- Continuously monitor risks from third parties, including
bad actors.
- Maintain and monitor communication plans and inform
user when AI system updates its capabilities. Assess
potential harms of publishing research derived from AI
models.

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Topic Details
- Conduct bug bashing and red teaming exercises.
- Forecast and reduce risks of secondary/unintended uses
and downstream harm of AI models.
Contemplating Ongoing Issues and Concerns
Awareness of legal issues
- How will a coherent tort liability framework be created to
adapt to the unique circumstances of AI and allocate
responsibility among developers, deployers and users?
- What are the challenges surrounding AI model and data
licensing?
- Can we develop systems that respect IP rights?
Awareness of user
concerns
- How do we properly educate users about the functions
and limitations of AI systems?
- How do we upskill and reskill the workforce to take full
advantage of AI benefits?
- Can there be an opt-out for a non-AI alternative?
Awareness of AI auditing
and accountability issues
- How can we build a profession of certified third-party
auditors globally – and consistent frameworks and
standards for them?
- What are the markers/indicators that determine when an
AI system should be subject to enhanced accountability,
such as third-party audits (e.g., automated decision-
making, sensitive data, others)?
- How do we enable companies to remain productive using
automated checks for AI governance and associated
ethical issues, while adapting this automation quickly to the
evolving standards and technology?
Experience the Actual Exam Structure with AIGP Sample
Questions:
Before jumping into the actual exam, it is crucial to get familiar with the IAPP Certified
Artificial Intelligence Governance Professional (AIGP) exam structure. For this purpose,
we have designed real exam-like sample questions. Solving these questions is highly
beneficial to getting an idea about the exam structure and question patterns. For more
understanding of your preparation level, go through the Artificial Intelligence
Governance Professional AIGP practice test questions. Find out the beneficial sample
questions below-

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01. How can companies remain productive while using automated checks for AI
governance?
a) By frequently updating their compliance protocols
b) Ignoring evolving standards
c) Avoiding automation in governance checks
d) Sticking to outdated technologies
02. Ensuring interoperability of AI risk management with other operational risk
strategies involves what?
a) Overlooking other risk management frameworks
b) Integrating AI risk considerations into the broader risk management plan
c) Focusing only on AI-specific risks
d) Eliminating traditional risk management strategies
03. Which strategies are crucial for upskilling and reskilling the workforce to
leverage AI benefits?
(Choose Three)
a) Offering advanced AI courses only to IT professionals
b) Implementing company-wide training programs
c) Establishing partnerships with educational institutions
d) Providing incentives for self-learning
e) Ignoring traditional skill sets
04. Identify a characteristic of an AI system that supports democratic processes
effectively:
a) Enhancing public trust and transparency.
b) Prioritizing automation over human judgment.
c) Maximizing AI system opacity.
d) Focusing on profitability over ethics.
05. According to the EU AI Act, which type of AI system is subject to the strictest
regulations?
a) High-risk
b) Limited risk
c) Low risk
d) Prohibited

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06. Identify the major aspects of the OECD framework for classifying AI systems.
(Choose Three)
a) Technology used
b) Level of automation
c) Role of humans
d) Environmental impact
e) Output of the system
07. Under what conditions can innovative AI be tested according to the EU AI Act,
without standard compliance?
a) In any condition if profitability is demonstrated.
b) Never; all AI must comply without exemptions.
c) When specific exemptions for research apply.
d) Only if the AI is used by government entities.
08. Which are types of machine learning training methods?
(Choose Two)
a) Supervised
b) Unsupervised
c) Inductive
d) Reinforcement
09. Identify the key components of the AI technology stack.
(Choose Two)
a) Platforms and applications
b) Chemical engineering techniques
c) Model types
d) Sports analytics
10. Mapping, planning, and scoping an AI project should ideally result in what?
a) Unclear project directives
b) Defined roles and responsibilities
c) Increased project costs
d) Extended project timelines

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Answers for AIGP Sample Questions
Answer 01:- a
Answer 02:- b
Answer 03:- b, c, d
Answer 04:- a
Answer 05:- d
Answer 06:- a, b, e
Answer 07:- c
Answer 08:- a, b
Answer 09:- a, c
Answer 10:- b

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