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IAPP AIGP Exam Overview:
| Certification Vendor: | IAPP |
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| Exam Name: | IAPP Certified Artificial Intelligence Governance Professional |
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| Exam Number: | AIGP |
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| Real Exam Qty: | 100 |
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| Available Languages: | English |
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| Exam Price: | USD 550 |
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| Related Certifications: | CIPT CIPP/US CIPP/A CIPP/E CIPM |
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| Exam Format: | Multiple-choice |
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| Passing Score: | 300 |
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| Certificate Validity Period: | 2 Years |
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| Exam Duration: | 150 minutes |
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| Sample Questions: | IAPP AIGP Sample Questions |
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| Exam Way: | Online (OnVUE) or Test Center (Pearson VUE) |
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| Pre Condition: | No specific prerequisites. Recommended background in privacy, compliance, legal, or technology. |
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| Official Syllabus URL: | https://iapp.org/certify/aigp/ |
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>> AIGP Antworten <<
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IAPP AIGP Prüfungsplan:
| Thema | Einzelheiten |
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| Thema 1 | - 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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| Thema 2 | - 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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| Thema 3 | - 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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| Thema 4 | - 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.
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IAPP Certified Artificial Intelligence Governance Professional AIGP Prüfungsfragen mit Lösungen (Q94-Q99):
94. Frage
CASE STUDY
Please use the following to answer the next question:
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.
To address these concerns, the company is considering using a third-party AI tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party AI-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 deploy technology solutions into the organization's operations in a responsible, cost-effective manner.
The organization is aware of the risks presented by AI hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the AI hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
Which of the following measures should the company adopt to best mitigate its risk of reputational harm from using the AI tool?
- A. Require the procurement and deployment teams to agree upon the AI tool.
- B. Continue to require the company's hiring personnel to manually screen all applicants.
- C. Ensure the vendor provides indemnification for the AI tool.
- D. Test the AI tool pre- and post-deployment.
Antwort: D
Begründung:
Testing the AI tool before and after deployment helps identify biases and errors early, reducing the risk of reputational harm by ensuring the system performs fairly and reliably.
95. Frage
All of the following are examples of biometric data in the US EXCEPT?
- A. GPS location of a user's fitness watch.
- B. Iris scans.
- C. Walking gait.
- D. Keystroke dynamics.
Antwort: A
Begründung:
Biometric data in the U.S. refers to data that relates to measurable biological and behavioral characteristics that can be used to identify an individual. Examples include fingerprints, facial recognition, iris scans, and behavior-based data like gait or keystrokes.
According to definitions and discussions from theAI Governance in Practice Report2025and U.S. privacy frameworks:
"Biometric data includes physical and behavioral human characteristics that can be used to digitally identify a person to grant access to systems, devices, or data. Examples include facial images, iris patterns, gait analysis, and voice recognition." (Report context based on common frameworks in U.S. AI law and the use of biometrics in AI governance.) Here's how the options relate:
* A. Iris scans- These are physical biometric identifiers.
* B. Walking gait- Behavioral biometric used increasingly in surveillance and identification.
* C. Keystroke dynamics- Behavioral biometric based on typing patterns.
* D. GPS location of a user's fitness watch- This isnotbiometric data. It islocation data, which may be sensitive or personal, but not biometric.
96. Frage
MULTI-SELECT
Please select 3 of the 5 options below. No partial credit will be given.
Which of the following PETs would be effective in developing AI systems?
- A. Data minimization.
- B. Federated learning.
- C. Data cleansing.
- D. Anonymization.
- E. Data mapping.
Antwort: A,B,D
Begründung:
A, B, and E are the intended privacy-preserving choices. Data minimization limits processing to information that is adequate, relevant, and necessary for the defined purpose, reducing privacy exposure during AI development. Anonymization protects individuals by transforming information so that people are no longer identifiable under the applicable standard. Federated learning is a recognized privacy-enhancing technology that enables models to be trained across distributed datasets without centrally sharing the underlying raw training data. The ICO specifically identifies federated learning and related privacy-preserving methods as useful during AI training and explains the relationship between anonymization and data minimization. Data mapping is primarily a governance and inventory activity used to understand data flows, while data cleansing principally improves data quality by correcting or removing inaccurate, incomplete, or unsuitable records; neither is inherently a PET.
97. Frage
An organization establishes a cross-functional AI governance committee including legal, engineering, compliance, and HR teams. What is the primary purpose of this approach?
- A. To reduce the need for external audits
- B. To ensure diverse expertise and risk perspectives
- C. To centralize technical decision-making
- D. To accelerate AI deployment timelines
Antwort: B
98. Frage
Which of the following are subjects covered by a typical impact assessment?
- A. Fundamental rights, data protection and safety.
- B. Third-party risk, model risk and legal risk.
- C. Datasets, behavior and tooling.
- D. Toxicity, accuracy and development.
Antwort: A
99. Frage
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