Free IAPP AIGP Practice Exam with Questions & Answers

TheEU AI Actapplies extraterritorially, meaning it affects entitiesoutside the EUwhen their AI systemsimpact individuals within the EU. However, it doesnotapply to systems that are developed, sold, or usedentirely outside of the EU—such as in theUK, unless they affect the EU market or individuals.

From theAI Governance in Practice Report 2025:

“The act imposes regulatory obligations… depending on their capabilities, reach and computing power, certain GPAI systems are considered to present systemic risk and attract broadly similar obligations to those applicable to high-risk AI systems.” (p. 7)

“The EU AI Act is the world’s first comprehensive AI regulation… requirements apply to providers, deployers, importers, and distributors of AI systems when such systems are placed on the EU market.” (p. 7–8)

Thus:

A. Open source releasedoes not exclude applicability if deployed in the EU.

B. Deployment at an EU borderclearly invokes jurisdiction.

D. Training by an EU companycreates jurisdictional links.

C. Deployment only at UK checkpoints, withno EU use or impact, isoutside scope.

Minimizing human involvement to the extent practicable is not a best practice during the testing of an ML model. Human oversight is crucial during testing to ensure that the model performs correctly and ethically, and to interpret any anomalies or issues that arise. Best practices include using representative test data, anonymizing data to the extent practicable, and performing testing specific to the intended uses of the model. Reference: AIGP Body of Knowledge on AI Model Testing and Human Oversight.

Performing an impact assessment is the best step to mitigate the possibility of discrimination before training and testing the AI solution. An impact assessment, such as a Data Protection Impact Assessment (DPIA) or Algorithmic Impact Assessment (AIA), helps identify potential biases and discriminatory outcomes that could arise from the AI system. This process involves evaluating the data and the algorithm for fairness, accountability, and transparency. It ensures that any biases in the data are detected and addressed, thus preventing discriminatory practices and promoting ethical AI deployment. Reference: AIGP Body of Knowledge on Ethical AI and Impact Assessments.

The EU AI Act categorizes certain applications of AI as high-risk due to their potential impact on fundamental rights and safety. High-risk applications include those used in critical areas such as employment, education, and essential public services. A government-run social scoring tool, which assesses individuals based on their social behavior or perceived trustworthiness, falls under this category because of its profound implications for privacy, fairness, and individual rights. This contrasts with other AI applications like resume scanning tools or customer service chatbots, which are generally not classified as high-risk under the EU AI Act.

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.

The correct answer isE – Superintelligence.

While the other options (Autonomy, Automation, Adaptability, Speed and Scale) are commonly cited as real-world characteristics that affect governance today,superintelligenceremainsa theoretical concept.

From the AIGP ILT Guide and AI Governance in Practice Report 2025:

“Core AI characteristics—such as automation, adaptability, speed, and autonomy—require active governance due to their impact on decision-making, legal liability, and risk.”

However,superintelligenceis speculative and not a current feature of AI systems under governance frameworks like theEU AI Act or NIST RMF.

Thus, it ' snot a current characteristicrequiring governance in real-world enterprise settings.

Theprimary considerationin selecting any AI system, especially aproprietary model, is itsfit for business purpose. Whether it serves the intended goals is foundational before evaluating technical or governance features.

From theAI Governance in Practice Report 2025:

“AI governance starts with defining the corporate strategy for AI… and aligning systems with business purpose and operational context.” (p. 11)

B, C, Dare relevant for evaluation, but onlyafterconfirming business applicability.

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.

The U.S. mortgage company ' s AI platform relates to housing and credit, making the Fair Housing Act (A), Fair Credit Reporting Act (B), and Equal Credit Opportunity Act (C) relevant. Title VII of the Civil Rights Act of 1964 deals with employment discrimination and is not directly relevant to the mortgage application context (D).

A multi-modal model is an AI system that can process and analyze multiple types of data, such as text, video, images, and sound. This type of AI integrates different data sources to enhance its understanding and decision-making capabilities. In the given scenario, the AI technology that tags content including names of animals, humans, and objects falls under this category. Reference: AIGP BODY OF KNOWLEDGE, which outlines the capabilities and use cases of multi-modal models​​.