IAPP Artificial Intelligence Governance Professional AIGP Exam Questions

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Total 215 questions
Question 1

Scenario:

A European AI technology company was found to be non-compliant with certain provisions of the EU AI Act. The regulator is considering penalties under the enforcement provisions of the regulation.

According to the EU AI Act, which of the following non-compliance examples could lead to fines of up to 15 million or 3% of annual worldwide turnover(whichever is higher)?



Answer : B

The correct answer isB. The EU AI Act assigns atiered penalty systembased on the severity of the violation. A breach ofobligations related to high-risk AI systemsfalls into the mid-tier category, triggering fines of 15 million or 3% of annual global turnover.

From the AIGP ILT Guide -- EU AI Act Module:

''Providers of high-risk AI systems must comply with strict documentation, testing, monitoring, and registration obligations. Breaches of these result in significant fines of up to 15 million or 3% of turnover.''

AI Governance in Practice Report 2025 supports this:

''Non-compliance with obligations under Title III (high-risk systems) leads to financial penalties under Article 71(3) of the EU AI Act.''

Note: Thehighest penalty (35 million or 7%)applies toprohibited AI uses, not to obligations for high-risk systems.

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Question 2

CASE STUDY

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.

When prioritizing the updates to its policies, rules and procedures to include the new AI system for user authentication, the organization should:



Answer : C

The correct answer is.C This action ties directly into principles of data minimization, purpose limitation, and lawfulness of processing, which are central to privacy and AI governance.

From the AIGP Body of Knowledge, Section on Privacy Considerations:

''Personal data must only be processed for specified and lawful purposes. Organizations must consider whether they have a legal basis for processing such data under data protection laws like the GDPR or CCPA.''

Additionally, AI Governance in Practice Report 2025 emphasizes:

''One of the most significant challenges when designing and developing AI systems is ensuring the data used is appropriate for the intended purpose... Managing unnecessary data, especially data that may contain sensitive attributes, can increase risk.''

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Question 3

Cloud-based deployment of AI often has which of the following advantages?



Answer : B

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.


Question 4

What is the best method to apply to an AI model so that it is statistically unlikely that a specific piece of training data can be identified from a model's output?



Answer : C

The correct answer is C because differential privacy is specifically designed to ensure that the inclusion or exclusion of any single data point does not significantly affect the output of a model. This provides strong mathematical guarantees that individual training data cannot be reverse-engineered or identified from model outputs. Differential privacy works by introducing controlled noise into the data or model outputs, thereby protecting individual-level information while preserving overall statistical patterns. In AI governance, it is widely recognized as a key privacy-enhancing technology for mitigating risks such as data leakage, membership inference attacks, and re-identification. Other options, such as homomorphic encryption, focus on secure computation, while data sharding and compartmentalization address data management but do not provide formal privacy guarantees at the model output level.


Question 5

A company deploys an AI model for fraud detection in online transactions. During its operation, the model begins to exhibit high rates of false positives, flagging legitimate transactions as fraudulent.

Which is the best step the company should take to address this development?



Answer : C

When an AI system causessignificant false positives, especially in sensitive contexts likefraud detection, the priority is tohalt harmful activityand perform a full assessment. Continued use without understanding the fault may cause furthercustomer harmand legal exposure.

From theAI Governance in Practice Report 2025:

''Incident management plans should enable identification, escalation, and system rollback to prevent continued harm from malfunctioning AI systems.'' (p. 12, 35)


Question 6

All of the following are obligations of providers under the EU AI Act EXCEPT?



Answer : D

D is the exception because the EU AI Act distinguishes obligations imposed on providers from those imposed on deployers. Under Article 16, providers of high-risk AI systems must maintain required documentation and automatically generated logs under their control. Article 17 requires providers to establish a documented quality management system, which includes setting up, implementing, and maintaining a post-market monitoring system. These provisions support A, B, and C. By contrast, Article 26 requires deployers to take appropriate organizational and technical measures to use high-risk systems according to the provider's instructions and to assign human oversight to suitably competent, trained, and authorized natural persons. Providers must design high-risk systems so effective human oversight is possible, but actually ensuring oversight during operational use in accordance with instructions is principally a deployer obligation.

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Question 7

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 identify security threats

Assessed and mitigated discrimination risks

Which additional responsible AI principle has the company failed to assess?



Answer : D

The correct answer is D because the company has already addressed several key responsible AI principles, including fairness through discrimination risk mitigation, robustness through adversarial and edge-case testing, and reliability via repeatability testing. However, there is no indication that the company has addressed transparency, which involves providing clear information about how the model operates, how pricing decisions are made, and how outputs may affect consumers. Transparency is a critical AI governance principle, especially in consumer-facing applications like dynamic pricing, where decisions can significantly impact individuals. Governance frameworks emphasize that users and stakeholders should be informed about AI-driven decisions and their implications. Without transparency, even technically sound and fair systems may lack trustworthiness and fail to meet regulatory or ethical expectations.


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