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IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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 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 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 (Q66-Q71):

NEW QUESTION # 66
Please select 3 of the 5 options below. No partial credit will be given.
All of the following are unique characteristics of AI that require a comprehensive approach to governance EXCEPT?

Answer: C,D,E

Explanation:
A: Autonomy - NOT selected
Reason:
Autonomy is repeatedly cited as one of the defining characteristics that necessitates governance.
NIST AI RMF references the risks of autonomous behavior.
ISO/IEC 42001 emphasizes governance controls for "systems operating with varying levels of autonomy." Thus, Autonomy IS a unique characteristic, so it is not an answer.
B: Automation - SELECTED
Reason:
"Automation" is not a unique AI property. Automation predates AI and applies to robotics, scripts, business process automation, etc.
AI governance literature does not classify automation as a unique AI-specific characteristic.
Automation is a result of AI, but not a distinguishing property.
Therefore, Automation is correctly selected as NOT a unique AI characteristic.
C: Adaptability - NOT selected
Reason:
Adaptability (systems that update, learn, or change behavior) is recognized universally as a core distinctive trait of AI.
EU AI Act emphasizes governance requirements for "adaptive systems."
NIST AI RMF highlights "learning and emergent behavior."
Thus, Adaptability IS a unique characteristic, so it should not be chosen as an exception.
D: Speed and scale - SELECTED
Reason:
While AI can operate at high speed and scale, these are not unique characteristics of AI.
Traditional computing systems, cloud workloads, and automation pipelines operate at similar scale.
AI governance documents mention speed/scale as a risk amplifier, not a defining characteristic.
Therefore, Speed and scale should be selected.
E: Superintelligence - SELECTED
Reason:
"Superintelligence" is not a characteristic of current AI systems and is not referenced as a governance-relevant attribute in mainstream policy documents (OECD, NIST, ISO, EU AI Act).
It is speculative rather than a defining feature.
Therefore, it is correctly chosen as NOT a unique characteristic requiring governance today.
Explanation:
AI governance frameworks consistently define the "unique characteristics" of AI that create novel governance challenges.
Across the OECD AI Principles, NIST AI RMF, ISO/IEC 42001, and the EU AI Act, the key characteristics requiring governance are:
Autonomy - AI systems act without direct human intervention and take context-dependent decisions.
Adaptability - AI systems learn, evolve, and update their behavior over time, making outputs non-deterministic.
These traits fundamentally distinguish AI from traditional software and shape all major governance activities (risk assessment, monitoring, assurance, accountability, alignment, etc.).
Below is why each option is correct or incorrect:


NEW QUESTION # 67
What is the primary objective of continuous monitoring in the lifecycle of an AI tool?

Answer: D

Explanation:
The correct answer is B because continuous monitoring is a core component of AI governance that ensures systems remain effective, reliable, and aligned with their intended objectives over time. AI systems can degrade due to data drift, model drift, or changing real-world conditions, making ongoing performance tracking essential. Monitoring allows organizations to detect anomalies, biases, or performance issues and take corrective actions such as retraining or recalibration. Governance frameworks emphasize post- deployment oversight to ensure systems continue to operate safely and within acceptable risk thresholds.
Option A incorrectly suggests fully autonomous evolution without oversight, which contradicts governance principles. Options C and D address specific operational concerns but do not capture the primary purpose of continuous monitoring, which is maintaining performance, accountability, and alignment with defined goals.


NEW QUESTION # 68
According to the GDPR's transparency principle, when an Al system processes personal data in automated decision-making, controllers are required to provide data subjects specific information on?

Answer: C

Explanation:
The GDPR's transparency principle requires that when personal data is processed for automated decision-making, including profiling, data subjects must be informed about the existence of such automated decision-making. Additionally, they must be provided with meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing for them. This requirement ensures that data subjects are fully aware of how their personal data is being used and the potential impacts, thereby promoting transparency and trust in the processing activities.


NEW QUESTION # 69
Which of the following best defines an "Al model"?

Answer: A

Explanation:
An AI model is best defined as a program that has been trained on a set of data to find patterns within that data. This definition captures the essence of machine learning, where the model learns from the data to make predictions or decisions. Reference: AIGP BODY OF KNOWLEDGE, which provides a detailed explanation of AI models and their training processes.


NEW QUESTION # 70
When evaluating which AI use cases to implement, an organization should consider all of the following EXCEPT:

Answer: A

Explanation:
TEVV (testing, evaluation, verification, and validation) metrics apply after a use case is selected and designed. They are not a primary factor when initially evaluating which AI use cases to pursue.


NEW QUESTION # 71
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