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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q68-Q73):
NEW QUESTION # 68
Which model is best for efficiency and agility, and tailored for lower-resource settings?
Answer: A
Explanation:
Small language models (SLMs)arelightweight, requireless compute, and arebetter suited to low-resource or edge environments, making them ideal for agility and efficiency.
From general AI best practices:
"SLMs can be deployed in environments with limited computing power, ensuring lower cost and faster integration in constrained contexts." (aligned with industry-wide AI deployment strategies)
NEW QUESTION # 69
Scenario:
A public sector agency is reviewing proposed AI use cases for improving services. It wants to prioritize implementations that deliver value but minimize unintended negative consequences.
When evaluating which AI use cases to implement, an organization should consider all of the following EXCEPT:
Answer: B
Explanation:
The correct answer is A. While TEVV is important in later lifecycle phases, it is not the primary consideration when evaluating and prioritizing use cases.
From the AIGP Body of Knowledge - Use Case Assessment Module:
"Use case evaluation focuses on value, impact, fairness, and accessibility-technical testing considerations come later." ILT Guide confirms:
"Organizations should first assess whether the AI system provides equitable outcomes and aligns with stakeholder expectations. TEVV is part of implementation, not initial prioritization." Thus, A is not a top-level consideration during use case selection.
NEW QUESTION # 70
What is the key feature of Graphical Processing Units (GPUs) that makes them well-suited to running Al applications?
Answer: C
Explanation:
GPUs (Graphical Processing Units) are well-suited to running AI applications due to their ability to run many tasks concurrently, which significantly enhances processing speed. This parallel processing capability makes GPUs ideal for handling the large-scale computations required in AI and deep learning tasks. Reference: AIGP BODY OF KNOWLEDGE, which explains the importance of compute infrastructure in AI applications.
NEW QUESTION # 71
Under the NIST Al Risk Management Framework, all of the following are defined as characteristics of trustworthy Al EXCEPT?
Answer: A
Explanation:
The NIST AI Risk Management Framework outlines several characteristics of trustworthy AI, including being secure and resilient, explainable and interpretable, and accountable and transparent. While being tested and effective is important, it is not explicitly listed as a characteristic of trustworthy AI in the NIST framework.
The focus is more on the system's ability to function safely, securely, and transparently in a way that stakeholders can understand and trust. Reference: AIGP Body of Knowledge, NIST AI RMF section.
NEW QUESTION # 72
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.
All of the following are obligations of the company as a data controller when implementing its AI system EXCEPT?
Answer: A
Explanation:
The correct answer is A. While location of processors may have implications (such as for data transfers under GDPR), there is no absolute requirement that third-party processors be based in the same country.
From the AI Governance in Practice Report 2024 and ILT Guide:
"Data controllers are responsible for ensuring that third-party processors have adequate protections, but not necessarily that they reside in the same jurisdiction. What is required is legal safeguards (e.g., SCCs) for international transfers, not same-country location." In contrast, DPIAs, DSARs, and implementation of technical/organizational safeguards are explicitly required under GDPR and responsible AI frameworks.
NEW QUESTION # 73
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