AIGP Practice Questions - AIGP Training Solutions

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IAPP AIGP Exam Overview:

Certification Vendor:IAPP (International Association of Privacy Professionals)
Exam Name:IAPP Certified Artificial Intelligence Governance Professional Exam
Exam Number:AIGP
Related Certifications:CIPM
CIPT
CIPP
Exam Price:USD 649 (members) / USD 799 (non-members)
Passing Score:300 (scaled score out of 500)
Available Languages:English
Certificate Validity Period:2 years
Exam Format:Multiple-choice, Scenario-based
Real Exam Qty:100
Exam Duration:165 (including 15-minute optional break)
Recommended Training:IAPP Training & Resources
Official AIGP Body of Knowledge & Study Guide
Exam Registration:IAPP Official Registration
Pearson VUE Scheduling
Sample Questions:IAPP AIGP Sample Questions
Exam Way:Online remote proctored or in-person at Pearson VUE test centers
Pre Condition:No formal prerequisites; open to all professionals
Official Syllabus URL:https://iapp.org/certify/aigp

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IAPP AIGP Training Solutions | Latest AIGP Guide Files

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IAPP AIGP Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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 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.

IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q91-Q96):

NEW QUESTION # 91
An autonomous vehicle maker deploys an AI system whereby the car is equipped with AI that makes decisions locally, without calling in to the AI's central server.
- The AI is capable of making decisions without waiting for a response
from a remote system.
- The AI can continue to function if it temporarily loses its
connection to the central server, such as driving through tunnels or
areas where its signal is lost.
- The AI can periodically receive updates to its model from its central server.
- A subset of anonymized data can be used to train the manufacturer's
AI model, depending on user privacy settings.
The manufacturer has deployed the AI using:

Answer: A

Explanation:
An edge model processes data and makes decisions locally on the device, enabling operation without continuous connection to a central server.


NEW QUESTION # 92
All of the following are penalties and enforcements outlined in the EU Al Act EXCEPT?

Answer: B

Explanation:
The EU AI Act outlines specific penalties and enforcement mechanisms to ensure compliance with its regulations. Among these, fines for violations of banned AI applications can be as high as €35 million or 7% of the global annual turnover of the offending organization, whichever is higher. Proportional caps on fines are applied to SMEs and startups to ensure fairness. General Purpose AI rules are to apply after a 6-month period as a specific provision to ensure that stakeholders have adequate time to comply. However, there is no provision for an "AI Pact" acting as a transitional bridge until the regulations are fully enacted, making option C the correct answer.


NEW QUESTION # 93
All of the following are common optimization techniques in deep learning to determine weights that represent the strength of the connection between artificial neurons EXCEPT?

Answer: B

Explanation:
Autoregression is not a common optimization technique in deep learning to determine weights for artificial neurons. Common techniques include gradient descent, momentum, and backpropagation. Autoregression is more commonly associated with time-series analysis and forecasting rather than neural network optimization.
Reference: AIGP BODY OF KNOWLEDGE, which discusses common optimization techniques used in deep learning.


NEW QUESTION # 94
The most important factor in ensuring fairness when training an Al system is?

Answer: C

Explanation:
Ensuring fairness when training an AI system largely depends on the data attributes and variability. This involves having a diverse and representative dataset that accurately reflects the population the AI system will serve. Fairness can be compromised if the data is biased or lacks variability, as the model may learn and perpetuate these biases. Diverse data attributes ensure that the model learns from a wide range of examples, reducing the risk of biased predictions. Reference: AIGP Body of Knowledge on Ethical AI Principles and Data Management.


NEW QUESTION # 95
In procuring an AI system from a vendor, which of the following would be important to include in a contract to enable proper oversight and auditing of the system?

Answer: A

Explanation:
Ensuringoversight and auditabilityrequires that the organization hassufficient access to data, documentation, and model internalsor outputs necessary for evaluation.
From theAI Governance in Practice Report 2025:
"Access to technical documentation and system internals is essential to enable effective auditing, conformity checks, and accountability mechanisms." (p. 11, 34) Ais about liability, not auditability.
Bmatters for IP rights, not oversight.
Crelates to lifecycle responsibility but doesn't guarantee audit access.


NEW QUESTION # 96
......

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