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IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 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.
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 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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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q128-Q133):

NEW QUESTION # 128
Cloud-based deployment of AI often has which of the following advantages?

Answer: C

Explanation:
The correct answer is B because one of the primary advantages of cloud-based AI deployment is its ability to easily scale resources up or down based on demand. Cloud platforms provide elastic infrastructure, allowing organizations to dynamically allocate compute power, storage, and processing capacity as needed. This is especially important for AI systems that experience fluctuating workloads, such as peak inference requests or large-scale model training. While cloud deployment can improve accessibility and efficiency, it does not inherently reduce latency compared to edge deployments, nor does it automatically enhance privacy or transparency. In AI governance, scalability is a key operational benefit that supports performance monitoring, cost efficiency, and responsiveness, enabling organizations to manage AI systems effectively across different environments and usage levels.


NEW QUESTION # 129
AU.S. mortgage company developed an AI platform that was trained using anonymized details from mortgage applications, including the applicant's education, employment and demographic information, as well as from subsequent payment or default information. The AI platform will be used to automatically grant or deny new mortgage applications, depending on whether the platform views an applicant as presenting a likely risk of default. Which of the following laws is NOT relevant to this use case?

Answer: A

Explanation:
Title VII of the Civil Rights Act primarily governs employment discrimination and is not directly relevant to mortgage lending decisions, unlike the other listed laws.


NEW QUESTION # 130
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 signup 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?

Answer: D

Explanation:
The GDPR privacy principles that this scenario violates are Purpose Limitation and Data Minimization. Purpose Limitation requires that personal data be collected for specified, explicit, and legitimate purposes and not further processed in a manner that is incompatible with those purposes. Data Minimization mandates that personal data collected should be adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed. In this case, collecting extensive personal information (e.g., IP address, location, gender) and potentially using it beyond the necessary scope for the app's functionality could violate these principles by collecting more data than needed and possibly using it for purposes not originally intended.


NEW QUESTION # 131
CASE STUDY
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 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 deploy 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.
All of the following are potential negative consequences created by using the AI tool to help make hiring decisions EXCEPT?

Answer: D

Explanation:
The correct answer isB. "Candidate quality" isnota negative consequence of using AI-rather, it is the intendedbenefitof using such tools (e.g., more efficient filtering of strong candidates).
From the AIGP ILT Guide:
"Automation bias, disparate impact, and privacy risks are well-documented concerns in AI-assisted hiring. These risks may arise when AI models replicate biases present in training data or obscure the decision logic." AI Governance in Practice Report 2025 (Bias and Fairness Section) also warns:
"Improper AI use in hiring can lead to disparate impact, where neutral criteria disproportionately disadvantage protected groups." Candidate quality is agoal, not a risk, makingB the correct answerfor what isnota negative outcome.


NEW QUESTION # 132
During the planning and design phases of the AI development life cycle, bias can be reduced by all of the following EXCEPT:

Answer: C

Explanation:
Human oversight is primarily a mitigation strategy during deployment and operation phases, whereas bias reduction during planning and design focuses on stakeholder involvement, feature selection, and data collection.


NEW QUESTION # 133
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