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IAPP AIGP Exam Overview:

Certification Vendor:	IAPP
Exam Name:	IAPP Certified Artificial Intelligence Governance Professional
Exam Number:	AIGP
Available Languages:	English
Certificate Validity Period:	2 Years
Passing Score:	300
Related Certifications:	CIPM CIPP/US CIPT CIPP/A CIPP/E
Exam Format:	Multiple-choice
Exam Price:	USD 550
Exam Duration:	150 minutes
Real Exam Qty:	100
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 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 3	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 4	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.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q34-Q39):

NEW QUESTION # 34
A hospital implements an AI system to assist doctors in diagnosing diseases based on historical patient data. Which one of the following model types best describes this system?

A. Deterministic.
B. Inference.
C. Statistical.
D. Probabilistic.

Answer: D

Explanation:
The AI system uses historical patient data to assess probabilities and uncertainties in diagnosing diseases, which aligns with a probabilistic model.

NEW QUESTION # 35
A company is creating a mobile app to enable individuals to upload images and videos, and analyze this data using ML to provide lifestyle improvement recommendations. The sign-up form has the following data fields:
1. First name
2. Last name
3. Mobile number
4. Email ID
5. New password
6. Date of birth
7. Gender
In addition, the app obtains a device's IP address and location information while in use.
What GDPR privacy principles does this violate?

A. Integrity and Confidentiality.
B. Accountability and Lawfulness.
C. Transparency and Accuracy.
D. Purpose Limitation and Data Minimization.

Answer: D

Explanation:
Collecting more personal data than necessary violates GDPR principles of Purpose Limitation (using data only for specific purposes) and Data Minimization (collecting only what is needed).

NEW QUESTION # 36
CASE STUDY
Please use the following to answer the next question:
A global marketing agency is adapting a large language model ("LLM") to generate content for an upcoming marketing campaign for a client's new product: a hard hat designed for construction workers of any gender to better protect them from head injuries.
The marketing agency is accessing the LLM through an application programming interface ("API") developed by a third-party technology company. They want to generate text to be used for targeted advertising communications that highlight the benefits of the hard hat to potential purchasers. Both the marketing agency and the technology company have taken reasonable steps to address AI governance.
The marketing company has:
- Entered into a contract with the technology company with suitable
representations and warranties.
- Completed an impact assessment on the LLM for this intended use.
- Built technical guidance on how to measure and mitigate bias in the
LLM.
- Enabled technical aspects of transparency, explainability, robustness and privacy.
- Followed applicable regulatory requirements.
- Created specific legal statements and disclosures regarding the use
of the AI on its client's advertising.
The technology company has:
- Provided guidance and resources to developers to address
environmental concerns.
- Build technical guidance on how to measure and mitigate bias in the
LLM.
- Provided tools and resources to measure bias specific to the LLM.
- Enabled technical aspects of transparency, explainability, robustness and privacy.
- Mapped and mitigated potential societal harms and large-scale
impacts.
- Followed applicable regulatory requirements and industry standards.
- Created specific legal statements and disclosures regarding the LLM,
including with respect to IP and rights to data.
All of the following results would be considered biased outputs from this AI system EXCEPT:

A. The generated ads are sent to construction companies, not individual workers.
B. The advertising text generated for female audiences focuses on color and style.
C. The images of female workers are hyper-sexualized.
D. The content generated for minority construction workers is insufficient.

Answer: A

Explanation:
Sending generated ads to construction companies rather than individual workers is a targeting choice, not an example of biased output from the AI system itself. The other options reflect biased or stereotypical content produced by the AI.

NEW QUESTION # 37
CASE STUDY
Please use the following to answer the next question:
A local police department in the United States procured an AI system to monitor and analyze social media feeds, online marketplaces and other sources of public information to detect evidence of illegal activities (e.g., sale of drugs or stolen goods). The AI system works by surveying the public sites in order to identify individuals that are likely to have committed a crime.
It cross-references the individuals against data maintained by law enforcement and then assigns a percentage score of the likelihood of criminal activity based on certain factors like previous criminal history, location, time, race and gender.
The police department retained a third-party consultant to assist in the procurement process, specifically to evaluate two finalists. Each of the vendors provided information about their system's accuracy rates, the diversity of their training data and how their system works. The consultant determined that the first vendor's system has a higher accuracy rate and based on this information, recommended this vendor to the police department.
The police department chose the first vendor and implemented its AI system. As part of the implementation, the department and consultant created a usage policy for the system, which includes training police officers on how the system works and how to incorporate it into their investigation process.
The police department has now been using the AI system for a year. An internal review has found that every time the system scored a likelihood of criminal activity at or above 90%, the police investigation subsequently confirmed that the individual had, in fact, committed a crime. Based on these results, the police department wants to forego investigations for cases where the AI system gives a score of at least 90% and proceed directly with an arrest.
What is the best reason the police department should continue to perform investigations even if the AI system scores an individual's likelihood of criminal activity at or above 90%?

A. Because investigations may uncover information relevant to sentencing.
B. Because the department did not perform an impact assessment for this intended use.
C. Because investigations may identify additional individuals involved in the crime.
D. Because AI systems that affect fundamental civil rights should not be fully automated.

Answer: D

Explanation:
AI systems impacting fundamental civil rights, such as arrests, require human oversight to prevent unfair automated decisions and ensure due process.

NEW QUESTION # 38
What is the main purpose of accountability structures under the Govern function of the NIST AI Risk Management Framework?

A. To empower and train appropriate cross-functional teams.
B. To enable and encourage participation by external stakeholders.
C. To determine responsibility for allocating budgetary resources.
D. To establish diverse, equitable and inclusive processes.

Answer: A

Explanation:
Accountability structures under the NIST AI Risk Management Framework's Govern function focus on empowering and training cross-functional teams to manage AI risks effectively.

NEW QUESTION # 39
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