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IAPP AIGP Exam Syllabus Topics:

SectionObjectives
AI Risk Management and Impact Assessment- Risk identification and mitigation
  • 1. AI risk categories and classification
    • 2. Bias, fairness, and transparency risks
      AI Lifecycle Governance Implementation- Operational governance controls
      • 1. Model development oversight
        • 2. Deployment and monitoring controls
          Foundations of AI Systems and Governance- Governance principles and accountability
          • 1. Responsible AI principles
            • 2. Organizational governance roles
              - AI system concepts, lifecycle, and terminology
              • 1. AI definitions and taxonomy
                • 2. AI system lifecycle overview
                  Regulatory and Legal Frameworks for AI- Global AI governance regulations
                  • 1. NIST AI Risk Management Framework
                    • 2. EU AI Act principles

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                      IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q25-Q30):

                      NEW QUESTION # 25
                      An organization establishes a cross-functional AI governance committee including legal, engineering, compliance, and HR teams. What is the primary purpose of this approach?

                      Answer: A


                      NEW QUESTION # 26
                      A US hospital plans to develop an AI that will review available patient data in order to propose an initial diagnosis to licensed physicians. The hospital will implement a policy that requires physicians to consider the AI proposal, but conduct their own physical examinations prior to making a final diagnosis.
                      An important ethical concern with this plan is?

                      Answer: C

                      Explanation:
                      The core ethical concern when deploying diagnostic AI in a healthcare setting is ensuringfairness and accuracy across diverse patient populations. If the AI is trained on a dataset that isnot representativeof the population it will serve, it risks reinforcing health disparities and leading to misdiagnoses.
                      From theAI Governance in Practice Report2025:
                      "Training datasets lacking in diversity can produce outputs that systematically underperform for certain groups... this can lead to inaccurate or biased outcomes in healthcare settings." (p. 41)
                      "Bias, discrimination and fairness challenge... inadequate or nonrepresentative training data can result in AI systems that propagate historical disparities." (p. 42) While physician oversight may reduce risk,biased data can still shape clinical decision-making.
                      * A- Economic benefit is not central to ethical risk here.
                      * C- Important but less critical than data representativeness.
                      * D- Error rate matters but is addressed via validation; it's not the core ethical issue.


                      NEW QUESTION # 27
                      What is the best reason for a company adopt a policy that prohibits the use of generative Al?

                      Answer: A

                      Explanation:
                      The primary concern for a company adopting a policy prohibiting the use of generative AI is the risk of accidental disclosure of confidential and proprietary information. Generative AI tools can inadvertently leak sensitive data during the creation process or through data sharing. This risk outweighs the other reasons listed, as protecting sensitive information is critical to maintaining the company's competitive edge and legal compliance. This rationale is discussed in the sections on risk management and data privacy in the IAPP AIGP Body of Knowledge.


                      NEW QUESTION # 28
                      CASE STUDY
                      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.
                      The data processed by the AI system would be classified as:

                      Answer: C

                      Explanation:
                      The correct answer isD.Keystroke dynamics, used to identify individuals, fallunder biometricdata, which isa specialcategory of personaldata underthe GDPR and other frameworks.
                      From the AI Governance in Practice Report2025:
                      "Keystroke dynamics may constitute biometric data if used to uniquely identify an individual... Biometric data is classified as special category personal data and requires higher protection standards." Also reflected in ILT Participant Guide:
                      "Biometric data, such as facial images, voiceprints, iris scans or keystroke patterns, are treated as special category data when they are used for the purpose of uniquely identifying individuals."


                      NEW QUESTION # 29
                      CASE STUDY
                      Please use the following answer the next question:
                      A mid-size US healthcare network has decided to develop an Al solution to detect a type of cancer that is most likely 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 a radiologist for secondary review pursuant agreed-upon criteria (e.g., a confidence score below a threshold).
                      To date, the healthcare network has taken the following steps: defined its Al ethical principles: conducted discovery to identify the intended uses and success criteria for the system: established an Al governance committee; assembled a broad, crossfunctional team with clear roles and responsibilities; and 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 and a consulting firm to 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.
                      In the design phase, what is the most important step for the healthcare network to take when mapping its existing data to the clinical research partner data?

                      Answer: D

                      Explanation:
                      In the design phase of integrating data from different sources, identifying fits and gaps is crucial. This process involves understanding how well the data from the clinical research partner aligns with the healthcare network's existing data. It ensures that the combined data set is coherent and can be effectively used for training the AI algorithm. This step helps in spotting any discrepancies, inconsistencies, or missing data that might affect the performance and accuracy of the AI model. It directly addresses the integrity and compatibility of the data, which is foundational before applying any privacy-enhancing technologies, labeling, or evaluating the origin of the data. Reference: AIGP Body of Knowledge on Data Integration and Quality.


                      NEW QUESTION # 30
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