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IAPP AIGP Exam Overview:

Certification Vendor:	IAPP
Exam Name:	IAPP Certified Artificial Intelligence Governance Professional
Exam Number:	AIGP
Related Certifications:	CIPT CIPM CIPP/US CIPP/E CIPP/A
Exam Duration:	150 minutes
Passing Score:	300
Available Languages:	English
Real Exam Qty:	100
Exam Price:	USD 550
Certificate Validity Period:	2 Years
Exam Format:	Multiple-choice
Sample Questions:	IAPP AIGP Sample Questions
Exam Way:	Online (OnVUE) or Test Center (Pearson VUE)
Pre Condition:	No specific prerequisites. Recommended background in privacy, compliance, legal, or technology.
Official Syllabus URL:	https://iapp.org/certify/aigp/

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IAPP AIGP Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 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.
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 model’s operational life.
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.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q151-Q156):

NEW QUESTION # 151
CASE STUDY
Please use the following to answer the next question:
A leading insurance provider that offers a range of coverage options to individuals has decided to utilize AI to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies. The company has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM").
The company intends to use its historical customer data - including applications, policies and claims - and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed to a human underwriter for final review.
The company and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. They have designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness and reliability of its output.
After the first month in production, the company realizes that the LLM declines a higher percentage of women's applications.
What is the best strategy to mitigate the bias uncovered in the loan applications?

A. Retrain the model with data that reflects demographic parity.
B. Procure a third-party statistical bias assessment tool.
C. Delete all gender-based data in the data set.
D. Document all instances of bias in the data set.

Answer: A

Explanation:
Retraining the model with data that reflects demographic parity helps reduce bias by balancing representation and mitigating unfair disparities in model outcomes.

NEW QUESTION # 152
All of the following types of testing can help evaluate the performance of a responsible AI system EXCEPT:

A. Decision analysis.
B. Risk probability/severity.
C. Statistical sampling.
D. Adversarial robustness.

Answer: C

Explanation:
Statistical sampling is a data analysis technique rather than a testing method to evaluate responsible AI system performance, unlike risk probability/severity, adversarial robustness, and decision analysis.

NEW QUESTION # 153
When evaluating which AI use cases to implement, an organization should consider all of the following EXCEPT:

A. Equitable access to the AI tool.
B. Potential positive and negative impacts of the system.
C. The users and their expectations.
D. Related TEVV and system metrics.

Answer: D

Explanation:
TEVV (testing, evaluation, verification, and validation) metrics apply after a use case is selected and designed. They are not a primary factor when initially evaluating which AI use cases to pursue.

NEW QUESTION # 154
Your organization is searching for a new way to help accurately forecast sales predictions by various types of customers.
Which of the following is the best type of model to choose if your organization wants to customize the model and avoid lock-in?

A. A free large language model.
B. A proprietary generative AI model.
C. A classic machine learning model.
D. A subscription-based, multimodal model.

Answer: C

Explanation:
Forcustomizable, interpretable modelsthat allow organizations toretain control and avoid vendor lock-in, classic ML models(e.g., regression, decision trees, random forests) are optimal.
From theAI Governance in Practice Report 2024:
"Organizations seeking transparency, customizability, and control often prefer classic ML models due to their flexibility and ease of governance." (p. 33)
* AandCmay have limited transparency and are often tied to specific providers.
* Dinvolves ongoing costs and limited model control.

NEW QUESTION # 155
To assist its internal recruiters with filtering job applications, a company decides to develop in- house an AI model for the screening and ranking of job applicants' resumes. Which of the following is a unique issue this company might face compared to using a third-party AI service?

A. The company needs to spend more money to deploy and maintain its AI model.
B. The company has less control over the decisions made by its AI model.
C. The company needs to invest in technical expertise to deploy and maintain its AI model.
D. The company is solely responsible for compliance with relevant regulations and standards of its AI model.

Answer: D

Explanation:
When developing an AI model in-house, the company bears sole responsibility for ensuring compliance with relevant regulations and standards, unlike when using third-party services where some responsibility may be shared.

NEW QUESTION # 156
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