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IAPP AIGP Exam Overview:

Certification Vendor:	IAPP
Exam Name:	IAPP Certified Artificial Intelligence Governance Professional
Exam Number:	AIGP
Passing Score:	300
Available Languages:	English
Certificate Validity Period:	2 Years
Related Certifications:	CIPM CIPP/US CIPT CIPP/E CIPP/A
Exam Price:	USD 550
Real Exam Qty:	100
Exam Format:	Multiple-choice
Exam Duration:	150 minutes
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 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 2	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 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.
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 (Q187-Q192):

NEW QUESTION # 187
According to November 2023 White House Executive Order, which of the following best describes the guidance given to governmental agencies on the use of generative Al as a workplace tool?

A. Limit access to specific uses of generative Al.
B. Limit access of generative Al to engineers and developers.
C. Impose a ban on the use of generative Al in agencies that protect national security.
D. Impose a general ban on the use of generative Al.

Answer: A

Explanation:
The November 2023 White House Executive Order provides guidance that governmental agencies should limit access to specific uses of generative AI. This means that generative AI tools should be used in a controlled manner, where their applications are restricted to well-defined, approved use cases that ensure the security, privacy, and ethical considerations are adequately addressed. This approach allows for the benefits of generative AI to be harnessed while mitigating potential risks and abuses.

NEW QUESTION # 188
When assessing the success of an AI system in meeting its objectives, which of the following approaches best aligns with the requirement to ensure a comprehensive evaluation while avoiding automation bias?

A. Conduct a thorough review that includes the AI system's input and output data, human interpretation, and potential secondary or unintended outputs.
B. Evaluate and align pre-defined benchmarks which will provide evidence of having achieved your system goals.
C. Focus on user feedback about the AI system's performance to directly measure the system's effectiveness in meeting the objectives.
D. Rely on the AI system's output to determine if the goals were achieved as this will provide a reliable and objective measure.

Answer: A

NEW QUESTION # 189
You asked a generative Al tool to recommend new restaurants to explore in Boston, Massachusetts that have a specialty Italian dish made in a traditional fashion without spinach and wine. The generative Al tool recommended five restaurants for you to visit.
After looking up the restaurants, you discovered one restaurant did not exist and two others did not have the dish.
This information provided by the generative Al tool is an example of what is commonly called?

A. Hallucination.
B. Model collapse.
C. Prompt injection.
D. Overfitting.

Answer: A

Explanation:
In the context of AI, particularly generative models, "hallucination" refers to the generation of outputs that are not based on the training data and are factually incorrect or non-existent. The scenario described involves the generative AI tool providing incorrect and non-existent information about restaurants, which fits the definition of hallucination. Reference: AIGP BODY OF KNOWLEDGE and various AI literature discussing the limitations and challenges of generative AI models.

NEW QUESTION # 190
Which of the following considerations is the most important in mitigating the potential of bias in training and testing data?

A. Receiving explicit consent to use the data.
B. Assessing the sufficiency of the third-party data.
C. Validating that the data is representative.
D. Using privacy-enhancing tools to protect the data.

Answer: C

Explanation:
Ensuring that training and testing data is representative of the real-world population is critical to mitigating bias in AI models.

NEW QUESTION # 191
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 is solely responsible for compliance with relevant regulations and standards of its AI model.
B. The company needs to invest in technical expertise to deploy and maintain its AI model.
C. The company needs to spend more money to deploy and maintain its AI model.
D. The company has less control over the decisions made by its AI model.

Answer: A

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 # 192
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