AIGP Exam Dumps - Artificial Intelligence Governance Professional

The primary concern for a company adopting a policy prohibiting the use of generative AI is the risk of accidental disclosure of confidential and proprietary information. Generative AI tools can inadvertently leak sensitive data during the creation process or through data sharing. This risk outweighs the other reasons listed, as protecting sensitive information is critical to maintaining the company’s competitive edge and legal compliance. This rationale is discussed in the sections on risk management and data privacy in the IAPP AIGP Body of Knowledge.

The correct answer is D because conformity requirements are primarily intended to ensure that AI systems meet applicable legal, regulatory, and safety standards before deployment. AI governance frameworks, including the EU AI Act and international standards, require conformity assessments to verify that systems are safe, reliable, and compliant with risk management, documentation, and performance obligations. These assessments help identify and mitigate risks prior to market release, particularly for high-risk AI systems that may impact individuals’ rights, health, or safety. Conformity ensures accountability, transparency, and trustworthiness, which are central principles of responsible AI governance. The other options relate to usability or technical considerations, but they do not address the primary purpose of conformity assessments, which is regulatory compliance and risk mitigation prior to deployment.

During the first month of monitoring the model for bias, it is most important to continue disparity testing. Disparity testing involves regularly evaluating the model ' s decisions to identify and address any biases, ensuring that the model operates fairly across different demographic groups.

The GDPR privacy principles that this scenario violates are Purpose Limitation and Data Minimization. Purpose Limitation requires that personal data be collected for specified, explicit, and legitimate purposes and not further processed in a manner that is incompatible with those purposes. Data Minimization mandates that personal data collected should be adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed. In this case, collecting extensive personal information (e.g., IP address, location, gender) and potentially using it beyond the necessary scope for the app ' s functionality could violate these principles by collecting more data than needed and possibly using it for purposes not originally intended.

Importers of high-risk AI systems into the EU havespecific responsibilitiesunder the EU AI Act. They arenotthe parties responsible for affixing the CE marking or providing technical documentation—but they must verify that these have been done by the provider.

From theAI Governance in Practice Report 2025:

“Importers must verify that the appropriate conformity assessment has been carried out, the technical documentation is available, and the CE marking has been affixed.” (p. 34–35)

Thus:

A. Provide technical documentation– done by theprovider.

B. Affix the CE marking–provider ' sresponsibility.

C. Verify the Declaration of Conformity–importer obligation.

D. Conduct a DPIA– relevant under data protection laws,not requiredunder the EU AI Act forimporters.

To protect the company ' s intellectual property, the most pertinent contractual provision is ensuring that the AI provider will defend and indemnify the company against infringement claims. This clause means the provider will take responsibility for any intellectual property disputes that arise, thereby safeguarding the company from potential legal and financial repercussions related to the use of the tool. Other options, while beneficial, do not directly address the protection of intellectual property. This concept is detailed in the contractual best practices section of the IAPP AIGP Body of Knowledge.

When monitoring the functional performance of a model deployed into production, concerns typically include feature drift, model drift, and data loss. Feature drift refers to changes in the input features that can affect the model ' s predictions. Model drift is when the model ' s performance degrades over time due to changes in the data or environment. Data loss can impact the accuracy and reliability of the model. However, system cost, while important for budgeting and financial planning, is not a direct concern when monitoring the functional performance of a deployed model. Reference: AIGP Body of Knowledge on Model Monitoring and Maintenance.

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.