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IAPP AIGP Exam Overview:
| Certification Vendor: | IAPP (International Association of Privacy Professionals) |
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| Exam Name: | IAPP Certified Artificial Intelligence Governance Professional Exam |
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| Exam Number: | AIGP |
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| Passing Score: | 300 (scaled score out of 500) |
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| Exam Duration: | 165 (including 15-minute optional break) |
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| Real Exam Qty: | 100 |
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| Exam Format: | Multiple-choice, Scenario-based |
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| Related Certifications: | CIPT CIPM CIPP |
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| Certificate Validity Period: | 2 years |
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| Available Languages: | English |
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| Exam Price: | USD 649 (members) / USD 799 (non-members) |
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| Recommended Training: | IAPP Training & Resources Official AIGP Body of Knowledge & Study Guide |
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| Exam Registration: | Pearson VUE Scheduling IAPP Official Registration |
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| Sample Questions: | IAPP AIGP Sample Questions |
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| Exam Way: | Online remote proctored or in-person at Pearson VUE test centers |
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| Pre Condition: | No formal prerequisites; open to all professionals |
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| Official Syllabus URL: | https://iapp.org/certify/aigp |
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| Topic | Details |
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| Topic 1 | - 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.
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| Topic 2 | - 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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| Topic 3 | - 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 modelโs operational life.
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| Topic 4 | - 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.
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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q188-Q193):
NEW QUESTION # 188
CASE STUDY
Please use the following answer the next question:
XYZ Corp., a premier payroll services company that employs thousands of people globally, is embarking on a new hiring campaign and wants to implement policies and procedures to identify and retain the best talent. The new talent will help the company's product team expand its payroll offerings to companies in the healthcare and transportation sectors, including in Asia.
It has become time consuming and expensive for HR to review all resumes, and they are concerned that human reviewers might be susceptible to bias.
Address these concerns, the company is considering using a third-party Al tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party Al-enabled hiring solution, as long as it would achieve its goals and comply with all applicable laws.
The organization has a large procurement team that is responsible for the contracting of technology solutions.
One of the procurement team's goals is to reduce costs, and it often prefers lower-cost solutions. Others within the company are responsible for integrating and deploying technology solutions into the organization's operations in a responsible, cost-effective manner.
The organization is aware of the risks presented by Al hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the Al hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
The frameworks that would be most appropriate for XYZ's governance needs would be the NIST Al Risk Management Framework and?
- A. NIST Cyber Security Risk Management Framework (CSF 2.0).
- B. IEEE Ethical System Design Risk Management Framework (IEEE 7000-21).
- C. Human Rights, Democracy, and Rule of Law Impact Assessment (HUDERIA).
- D. NIST Information Security Risk (NIST SP 800-39).
Answer: B
Explanation:
The IEEE Ethical System Design Risk Management Framework (IEEE 7000-21) would be most appropriate for XYZ Corp's governance needs in addition to the NIST AI Risk Management Framework. The IEEE framework specifically addresses ethical concerns during system design, which is crucial for ensuring the responsible use of AI in hiring. It complements the NIST framework by focusing on ethical risk management, aligning well with XYZ Corp's goals of deploying AI responsibly and mitigating associated risks.
NEW QUESTION # 189
A US-based mortgage lender has purchased a chatbot. They plan to have the chatbot collect information from consumers who are interested in loans and offer the consumers 2-3 different options based on its current pricing and product offerings, which change frequently. This chatbot was initially developed and previously deployed by a Russian airline for booking flights.
The best option for the part of the process that generates the loan offers is?
- A. Retrieval-Augmented Generation.
- B. Quantum computing
- C. Expert System.
- D. Multimodal Generative AI.
Answer: C
NEW QUESTION # 190
An app developer is creating a to-do app that compiles users ' to-do lists, conducts basic online research about the tasks, prioritizes tasks, and allows users to add descriptions and notes. However, during the app ' s download process, it requests the user ' s name, address, phone number, political and union affiliations.
Which privacy principle relevant to the responsible development of AI systems is most likely violated by this data collection practice?
- A. Data portability and subject access rights.
- B. Purpose limitation and explicit consent.
- C. Transparency and explainability.
- D. Data minimization and privacy by design.
Answer: D
Explanation:
Data minimization requires organizations to collect and process only personal information that is adequate, relevant, and necessary for the specified purpose. A basic to-do application may reasonably require limited account or contact information, but political opinions and trade-union affiliations have no apparent necessity for compiling, researching, or prioritizing tasks. Those attributes are also treated as particularly sensitive under European data-protection law. Privacy by design requires privacy safeguards and data-protection principles to be incorporated into the product from the design stage and reflected in default settings.
Therefore, unnecessary collection at installation directly implicates both minimization and privacy by design.
Purpose limitation may also become relevant, but explicit consent does not by itself cure unjustified overcollection. Transparency, portability, and access rights address different privacy obligations and do not best characterize the defect described here.
NEW QUESTION # 191
Which of the following are not considered biometric data under U.S. privacy laws?
- A. Iris scans
- B. Keystroke dynamics
- C. Walking gait
- D. GPS location of a user's fitness watch
Answer: D
Explanation:
The correct answer is D. GPS location data is not biometric data-it is considered geolocation data, which is personal data but not biometric under most U.S. laws.
From the AIGP ILT Guide (Data Privacy Module):
"Biometric data includes measurable biological or behavioral characteristics such as iris scans, facial recognition, voice prints, and keystroke patterns when used for identification." AI Governance in Practice Report 2024 (Privacy and Data Protection section):
"Location data, while sensitive, is not considered biometric unless it's tied to a uniquely identifying biological trait." Thus, GPS location data, while potentially sensitive, is not classified as biometric.
NEW QUESTION # 192
A French medical research center wishes to develop an AI-based system that will predict the risk of serious diseases based on patients ' genetic data. To do so, it contracts with a technology company and provides it with patients ' data previously obtained by the center during research.
To guarantee compliance when processing special categories of personal data, the medical research center must ensure that?
- A. The patients ' health and genetic data is anonymized.
- B. The technology company is located in the EU and is not cloud-based.
- C. The AI-based system is designed for the purposes of preventive medicine.
- D. The patients have given explicit consent to using the data.
Answer: D
Explanation:
Genetic and health information are special categories of personal data under Article 9 of the GDPR, for which processing is generally prohibited unless a valid Article 9 exception applies. Among the available answers, explicit consent provides the clearest applicable condition: Article 9(2)(a) permits processing where the data subject has explicitly consented to processing for specified purposes. Importantly, explicit consent is not the only possible legal condition under the GDPR; scientific research and healthcare processing may sometimes rely on other Article 9 conditions where applicable law and safeguards permit. However, A merely describing a preventive-medicine purpose does not itself establish the necessary legal condition. D is irrelevant because EU establishment and non-cloud processing do not establish lawfulness. B would materially change the analysis only if the data were genuinely anonymized so that they were no longer personal data.
NEW QUESTION # 193
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