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IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • Understanding How to Govern AI Deployment and Use: This section of the exam measures skills of technology deployment leads and covers the responsibilities associated with selecting, deploying, and using AI models in a responsible manner. It includes evaluating key factors and risks before deployment, understanding different model types and deployment options, and ensuring ongoing monitoring and maintenance. The domain applies to both proprietary and third-party AI models, emphasizing the importance of transparency, ethical considerations, and continuous oversight throughout the model’s operational life.
Topic 2
  • Understanding the Foundations of AI Governance: This section of the exam measures skills of AI governance professionals and covers the core concepts of AI governance, including what AI is, why governance is needed, and the risks and unique characteristics associated with AI. It also addresses the establishment and communication of organizational expectations for AI governance, such as defining roles, fostering cross-functional collaboration, and delivering training on AI strategies. Additionally, it focuses on developing policies and procedures that ensure oversight and accountability throughout the AI lifecycle, including managing third-party risks and updating privacy and security practices.
Topic 3
  • Understanding How Laws, Standards, and Frameworks Apply to AI: This section of the exam measures skills of compliance officers and covers the application of existing and emerging legal requirements to AI systems. It explores how data privacy laws, intellectual property, non-discrimination, consumer protection, and product liability laws impact AI. The domain also examines the main elements of the EU AI Act, such as risk classification and requirements for different AI risk levels, as well as enforcement mechanisms. Furthermore, it addresses the key industry standards and frameworks, including OECD principles, NIST AI Risk Management Framework, and ISO AI standards, guiding organizations in trustworthy and compliant AI implementation.
Topic 4
  • Understanding How to Govern AI Development: This section of the exam measures the skills of AI project managers and covers the governance responsibilities involved in designing, building, training, testing, and maintaining AI models. It emphasizes defining the business context, performing impact assessments, applying relevant laws and best practices, and managing risks during model development. The domain also includes establishing data governance for training and testing, ensuring data quality and provenance, and documenting processes for compliance. Additionally, it focuses on preparing models for release, continuous monitoring, maintenance, incident management, and transparent disclosures to stakeholders.

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Quiz IAPP - AIGP - IAPP Certified Artificial Intelligence Governance Professional Exam Tips

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q102-Q107):

NEW QUESTION # 102
An organization that provides AI services to clients has decided to adopt ISO/IEC 42001:2023.
How does the standard define the relationship between the AI policy and the other policies already in place at the organization?

Answer: C

Explanation:
ISO/IEC 42001:2023 establishes an Artificial Intelligence Management System designed to integrate AI governance into an organization ' s existing management structure rather than create an isolated governance regime. Its AI-policy requirements state that the policy should refer, where relevant, to other organizational policies. Therefore, A most accurately describes the relationship. The standard does not require the organization to absorb all related security, privacy, quality, risk, or compliance policies into one AI policy, making B incorrect. C is inconsistent with integrated governance because AI issues commonly intersect with information security, privacy, procurement, data governance, human resources, and enterprise risk management. Management review is an important management-system activity, but D does not state the relationship required between the AI policy and existing policies. ISO/IEC 42001 consequently favors coordinated and mutually aligned organizational governance.


NEW QUESTION # 103
The White House Executive Order from November 2023 requires companies that develop dual- use foundation models to provide reports to the federal government about all of the following EXCEPT:

Answer: D

Explanation:
Under the White House Executive Order on Artificial Intelligence (EO 14110), companies developing dual-use foundation models are required to report specific information to the federal government. These requirements include:
1. Ongoing or planned activities related to training, developing, or producing dual-use foundation models, including physical and cybersecurity protections taken to assure the integrity of the training process against sophisticated threats.
2. Ownership and possession of the model weights of any dual-use foundation models, and the physical and cybersecurity measures taken to protect those model weights.
3. The results of any developed dual-use foundation model's performance in relevant AI red-team testing, including a description of any associated measures the company has taken to meet safety objectives, such as mitigations to improve performance on these red-team tests and strengthen overall model security.
4. Other information pertaining to the safety and reliability of dual-use foundation models, or activities or risks that present concerns to U.S. national security.
Notably, the EO does not mandate companies to conduct or report on environmental impact studies for each dual-use foundation model. Therefore, option C is not a required reporting obligation under the Executive Order.


NEW QUESTION # 104
CASE STUDY
Please use the following to answer the next question:
A company is considering the procurement of an AI system designed to enhance the security of IT infrastructure. The AI system analyzes how users type on their laptops, including typing speed, rhythm and pressure, to create a unique user profile. This data is then used to authenticate users and ensure that only authorized personnel can access sensitive resources.
When prioritizing the updates to its policies, rules and procedures to include the new AI system for user authentication, the organization should:

Answer: B

Explanation:
Since the AI system processes personal behavioral data (typing patterns) for authentication, the organization must ensure that this personal data is processed only for specific and lawful purposes, aligning with data protection principles.


NEW QUESTION # 105
CASE STUDY
Please use the following to answer the next question:
A mid-size US healthcare network has decided to develop an AI solution to detect a type of cancer that is most likely to arise in adults. Specifically, the healthcare network intends to create a recognition algorithm that will perform an initial review of all imaging and then route records to a radiologist for secondary review pursuant to agreed-upon criteria such as a confidence score below a threshold.
To date, the healthcare network has:
Defined its AI ethical principles
Conducted discovery to identify the intended uses and success criteria for the system Established an AI risk committee Assembled a cross-functional team with clear roles and responsibilities Created policies and procedures to document standards, workflows, timelines and risk thresholds during the project The healthcare network intends to retain a cloud provider to host the solution. It also intends to retain a large consulting firm to supplement its small data science team and help develop the algorithm using the healthcare network ' s existing data and de-identified data that is licensed from a large US clinical research partner.
Which of the following steps can best mitigate the possibility of discrimination prior to training and testing the AI solution?

Answer: C

Explanation:
The correct answer is C because performing an impact assessment is the most effective proactive measure to identify and mitigate discrimination risks before model training and testing. AI governance frameworks emphasize early-stage risk identification, particularly for high-stakes domains like healthcare, where bias can lead to harmful or inequitable outcomes. Impact assessments, such as algorithmic or data protection impact assessments, evaluate potential harms, affected populations, and fairness risks prior to development. This enables organizations to design appropriate safeguards, adjust data selection, and implement mitigation strategies before biases become embedded in the model. Options like audits and bias bounty programs are reactive or post-development controls, while simply acquiring more data does not guarantee reduced bias without proper analysis. A structured impact assessment aligns with risk-based governance and supports fairness, accountability, and regulatory compliance.


NEW QUESTION # 106
According to the GDPR, an individual has the right to have a human confirm or replace an automated decision unless that automated decision?

Answer: B

Explanation:
According to the GDPR, individuals have the right to not be subject to a decision based solely on automated processing, including profiling, which produces legal effects or similarly significantly affects them. However, there are exceptions to this right, one of which is when the decision is based on the data subject's explicit consent. This means that if an individual explicitly consents to the automated decision-making process, there is no requirement for human intervention to confirm or replace the decision. This exception ensures that individuals can have control over automated decisions that affect them, provided they have given clear and informed consent.


NEW QUESTION # 107
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