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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 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.

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IAPP AIGP Pdf Dumps & AIGP Original Questions

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q104-Q109):

NEW QUESTION # 104
After completing model testing and validation, which of the following is the most important step that an organization takes prior to deploying the model into production?

A. Define a model-validation methodology.
B. Perform a readiness assessment.
C. Document maintenance teams and processes.
D. Identify known edge cases to monitor post-deployment.

Answer: B

Explanation:
Performing a readiness assessment ensures the model and supporting systems are fully prepared for deployment, addressing risks and compliance before going live.

NEW QUESTION # 105
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 # 106
The White House Executive Order from November 2023 requires companies that develop dual-use foundation models to provide reports to the federal government about all of the following EXCEPT?

A. Any current training or development of dual-use foundation models.
B. The physical and cybersecurity protection measures of their dual-use foundation models.
C. Any environmental impact study for each dual-use foundation model.
D. The results of red-team testing of each dual-use foundation model.

Answer: C

Explanation:
The White House Executive Order from November 2023 requires companies developing dual-use foundation models to report on their current training or development activities, the results of red-team testing, and the physical and cybersecurity protection measures. However, it does not mandate reports on environmental impact studies for each dual-use foundation model. While environmental considerations are important, they are not specified in this context as a reporting requirement under this Executive Order.
Reference: AIGP BODY OF KNOWLEDGE, sections on compliance and reporting requirements, and the White House Executive Order of November 2023.

NEW QUESTION # 107
You are an engineer that developed an Al-based ad recommendation tool.
Which of the following should be monitored to evaluate the tool's effectiveness?

A. GPU performance, to evaluate the tool's robustness.
B. Output data, assess the delta between the prediction and actual ad clicks.
C. Input data, to ensure the ads are reaching the target audience.
D. Algorithmic patterns, to show the model has a high degree of accuracy.

Answer: B

Explanation:
To evaluate the effectiveness of an AI-based ad recommendation tool, the most relevant metric is the output data, specifically assessing the delta between the prediction and actual ad clicks. This metric directly measures the tool's accuracy and effectiveness in making accurate recommendations that lead to user engagement. While monitoring algorithmic patterns and input data can provide insights into the model's behavior and targeting accuracy, and GPU performance can indicate the robustness and efficiency of the tool, the primary indicator of effectiveness for an ad recommendation tool is how well it predicts actual ad clicks.
Reference: AIGP BODY OF KNOWLEDGE, sections on AI performance metrics and evaluation methods.

NEW QUESTION # 108
Pursuant to the White House Executive Order of November 2023, who is responsible for creating guidelines to conduct red-teaming tests of Al systems?

A. National Institute of Standards and Technology (NIST).
B. Office of Science and Technology Policy (OSTP).
C. National Science and Technology Council (NSTC).
D. Department of Homeland Security (DHS).

Answer: A

Explanation:
The White House Executive Order of November 2023 designates the National Institute of Standards and Technology (NIST) as the responsible body for creating guidelines to conduct red-teaming tests of AI systems. NIST is tasked with developing and providing standards and frameworks to ensure the security, reliability, and ethical deployment of AI systems, including conducting rigorous red-teaming exercises to identify vulnerabilities and assess risks in AI systems.

NEW QUESTION # 109
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