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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 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 3	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 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.

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q14-Q19):

NEW QUESTION # 14
What type of organizational risk is associated with Al's resource-intensive computing demands?

A. People risk.
B. Environmental risk.
C. Security risk.
D. Third-party risk.

Answer: B

Explanation:
AI's resource-intensive computing demands pose significant environmental risks. High-performance computing required for training and deploying AI models often leads to substantial energy consumption, which can result in increased carbon emissions and other environmental impacts. This is particularly relevant given the growing concern over climate change and the environmental footprint of technology. Organizations need to consider these environmental risks when developing AI systems, potentially exploring more energy-efficient methods and renewable energy sources to mitigate the environmental impact.

NEW QUESTION # 15
Machine learning is best described as a type of algorithm by which?

A. Systems can mimic human intelligence with the goal of performing routine tasks.
B. Previously unknown properties are discovered in data and used to predict and make improvements in the data.
C. Statistical inferences are drawn from a sample with the goal of predicting human intelligence.
D. Systems can automatically improve from experience through predictive patterns.

Answer: D

Explanation:
Machine learning is defined as systems that automatically improve their performance through experience by identifying predictive patterns in data.

NEW QUESTION # 16
A company has developed a proprietary AI model that analyzes consumer online behavior and predicts what prices consumers would be willing to pay for certain products, so that retailers may modify pricing accordingly. To test the model, the company has:
- Performed an impact assessment.
- Conducted repeatability tests.
- Exposed the model to edge cases and potential malicious input.
- Conducted adversarial testing to ID security threats.
- Assessed and mitigated discrimination risks.
Which additional responsible AI principle has the company failed to assess?

A. Fairness.
B. Robustness.
C. Data Integrity.
D. Transparency.

Answer: D

Explanation:
While the company addressed security, fairness, and robustness through testing and risk assessment, transparency, ensuring stakeholders understand how the model makes decisions, has not been explicitly assessed.

NEW QUESTION # 17
What is the main purpose of accountability structures under the Govern function of the NIST Al Risk Management Framework?

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

Answer: C

Explanation:
The NIST AI Risk Management Framework's Govern function emphasizes the importance of establishing accountability structures that empower and train cross-functional teams. This is crucial because cross- functional teams bring diverse perspectives and expertise, which are essential for effective AI governance and risk management. Training these teams ensures that they are well-equipped to handle their responsibilities and can make informed decisions that align with the organization's AI principles and ethical standards. Reference:
NIST AI Risk Management Framework documentation, Govern function section.

NEW QUESTION # 18
CASE STUDY
Please use the following to answer the next question:
A company is considering the procurement of an AI system designed to enhance the security of IT infrastructure. The AI system analyzes how users type on their laptops, including typing speed, rhythm and pressure, to create a unique user profile. This data is then used to authenticate users and ensure that only authorized personnel can access sensitive resources.
All of the following are obligations of the company as a data controller when implementing its AI system EXCEPT?

A. Ensuring that third-party processors are based in the same country as the company.
B. Allowing data subject access requests (DSARs).
C. Implementing technical and organizational measures.
D. Conducting a Data Protection Impact Assessment (DPIA) / Privacy Impact Assessment (PIA).

Answer: A

Explanation:
While the company must ensure data protection compliance, it is not required that third-party processors be located in the same country; instead, adequate data protection measures and legal safeguards must be in place.

NEW QUESTION # 19
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