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NEW QUESTION # 46
What is the main purpose of accountability structures under the Govern function of the NIST AI Risk Management Framework?
Answer: C
Explanation:
Accountability structures under the NIST AI Risk Management Framework's Govern function focus on empowering and training cross-functional teams to manage AI risks effectively.
NEW QUESTION # 47
Scenario:
A distributor operating in the EU is responsible for selling imported high-risk AI systems to businesses. The distributor wants to ensure they fulfill all applicable obligations under the EU AI Act.
All of the following are obligations of a distributor of high-risk AI systems under the EU AI Act EXCEPT?
Answer: A
Explanation:
The correct answer isC.Registration in the EU databaseis an obligation ofprovidersof high-risk AI systems- not distributors.
From the AIGP ILT Guide - Roles & Obligations Module:
"Distributors must verify CE marking, ensure instructions for use are provided, inform authorities of risks, and take corrective action when necessary. However, registration duties in the EU database lie with the provider." Also from the AI Governance in Practice Report2025:
"The AI Act differentiates responsibilities for developers, providers, importers, and distributors. Only providers of high-risk systems are obligated to register their systems in the EU AI Database." Distributors focus onverification and communication, not formal registration.
NEW QUESTION # 48
Business A sells software that provides users with writing and grammar assistance. Business B is a cloud services provider that trains its own AI models.
* Business A has decided to add generative AI features to their software.
* Rather than create their own generative AI model, Business A has chosen to license a model from Business B:
* Business A will then integrate the model into their writing assistance software to provide generative AI capabilities.
* Business A is most concerned that its writing assistance software could recommend toxic or obscene text to its users.
Which of the following governance processes should Business A take to best protect its users against potentially inappropriate text?
Answer: A
Explanation:
Business A is integrating a generative AI model licensed from a third party (Business B) and is primarily concerned with the risk of toxic or obscene outputs being delivered to users. In this scenario,testing and validationof the AI model for such content risks is the most direct and effective governance strategy.
According to theAI Governance in Practice Report 2024, organizations thatdeployAI must engage in performance monitoring protocolsand ensure systems perform adequately for theirintended purposes, including filtering harmful content:
"Operational governance... development of: #Performance monitoring protocols to ensure systems perform adequately for their intended purposes." (p. 12)
"Product governance... includes: #System impact assessments to identify and address risk prior to product development or deployment." (p. 11) Furthermore, under theEU AI Act, which sets the global standard many organizations aim to align with, there is a clear obligation to test and monitor systems for potential harmful behavior:
"The act imposes regulatory obligations... such as establishing appropriate accountability structures,assessing system impact, providing technical documentation,establishing risk management protocols and monitoring performance..." (p. 7) Option B directly reflects this best practice ofpre-deployment testing and validationto ensure that the model aligns with Business A's minimum content safety requirements.
Let's now evaluate the incorrect options:
* A. Fine-tuning on verified user-generated textmay improve model alignment but does not guarantee that the model will generalize correctly, especially if Business A lacks access to model internals (common in third-party licensing scenarios). Fine-tuning also introduces its own risks and may be contractually restricted.
* C. A user reporting featureisreactive, not preventive. While helpful for long-term monitoring and mitigation, it does not prevent the initial harm of toxic outputs, which isBusiness A's primary concern.
* D. Requesting documentation from Business Bis useful for transparency and risk management, but it does not replaceindependent verificationthat the model meets Business A's content safety standards.
Thus,testing the model's behavior for unacceptable outputs before deploymentis the most aligned approach with AI governance best practices and obligations.
NEW QUESTION # 49
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
Explanation:
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 2024 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.
NEW QUESTION # 50
A company initially intended to use a large data set containing personal information to train an Al model. After consideration, the company determined that it can derive enough value from the data set without any personal information and permanently obfuscated all personal data elements before training the model.
This is an example of applying which privacy-enhancing technique (PET)?
Answer: C
Explanation:
Anonymization is a privacy-enhancing technique that involves removing or permanently altering personal data elements to prevent the identification of individuals. In this case, the company obfuscated all personal data elements before training the model, which aligns with the definition of anonymization. This ensures that the data cannot be traced back to individuals, thereby protecting their privacy while still allowing the company to derive value from the dataset. Reference: AIGP Body of Knowledge, privacy-enhancing techniques section.
NEW QUESTION # 51
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