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| Certification Vendor: | IAPP (International Association of Privacy Professionals) |
|---|---|
| Exam Name: | IAPP Certified Artificial Intelligence Governance Professional Exam |
| Exam Number: | AIGP |
| Exam Duration: | 165 (including 15-minute optional break) |
| Exam Format: | Scenario-based, Multiple-choice |
| Exam Price: | USD 649 (members) / USD 799 (non-members) |
| Related Certifications: | CIPT CIPM CIPP |
| Certificate Validity Period: | 2 years |
| Available Languages: | English |
| Passing Score: | 300 (scaled score out of 500) |
| Real Exam Qty: | 100 |
| Recommended Training: | Official AIGP Body of Knowledge & Study Guide IAPP Training & Resources |
| Exam Registration: | IAPP Official Registration Pearson VUE Scheduling |
| Sample Questions: | IAPP AIGP Sample Questions |
| Exam Way: | Online remote proctored or in-person at Pearson VUE test centers |
| Pre Condition: | No formal prerequisites; open to all professionals |
| Official Syllabus URL: | https://iapp.org/certify/aigp |
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NEW QUESTION # 97
All of the following types of testing can help evaluate the performance of a responsible AI system EXCEPT:
Answer: D
Explanation:
Statistical sampling is a data analysis technique rather than a testing method to evaluate responsible AI system performance, unlike risk probability/severity, adversarial robustness, and decision analysis.
NEW QUESTION # 98
All of the following are obligations of providers under the EU AI Act EXCEPT?
Answer: D
Explanation:
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.
NEW QUESTION # 99
CASE STUDY
Please use the following to answer the next question:
You have recently assumed the role of AI Governance leader for a California-based medical technology company. The organization primarily serves hospitals and has recently expanded to include walk-in clinics located within local pharmacies.
The company ' s core business focuses on diagnostic assistance powered by a large language model LLM and back-office process optimization using Agentic AI, including chatbots, medical record request handling, scheduling and billing.
In preparation for its next round of funding, the board has asked you to prepare an AI Risk report to demonstrate to investors how the company is addressing AI-related risks. In preparing the report you learn that last year the company generated 30 million dollars in gross revenue across the US, EU, India, and South Korea and that vendors are engaged for various activities, including model testing and providing third-party AI solutions for chatbots.
Which of the following responses is least likely to meet mandatory transparency requirements in any of the applicable laws?
Answer: B
Explanation:
The correct answer is C because simply publishing a general AI policy or code of conduct does not satisfy specific transparency obligations required by AI regulations. Transparency requirements focus on providing clear, context-specific disclosures to affected individuals about how AI is being used, particularly when it directly impacts them. For example, notifying users when they are interacting with an AI system or labeling AI-generated content are explicit transparency measures aligned with regulatory expectations. The AI Governance in Practice Report highlights that transparency includes informing users when they interact with AI systems and providing meaningful information about outputs and system behavior . Model cards and labels support system-level and output-level transparency, while real-time notices ensure user awareness. In contrast, a general policy link lacks immediacy, specificity, and user relevance, making it insufficient for mandatory transparency compliance.
NEW QUESTION # 100
Scenario:
An organization is developing a powerful general-purpose AI (GPAI) model that has systemic impact. The compliance team is assessing what legal obligations apply under the EU AI Act.
Under the EU AI Act, which of the following compliance actions applies only to General Purpose AI models with systemic risk?
Answer: A
Explanation:
The correct answer is A. Only GPAI models with systemic risk must publish a detailed summary of training data to meet transparency and accountability standards under the EU AI Act.
From the AI Governance in Practice Report 2024 (EU AI Act Section):
"For GPAI systems with systemic risk, providers must publish sufficiently detailed summaries of the content used to train the model." Also, the AIGP ILT Guide confirms:
"The obligation to disclose summaries of training data applies only to systemic-risk GPAI models, not all general-purpose models or high-risk systems." This unique requirement is part of the Act's effort to increase transparency and auditability for powerful foundational models.
NEW QUESTION # 101
The processes and methods that allow human users to understand and trust the outputs produced by AI are important in addressing which key regulatory concern?
Answer: C
Explanation:
The correct answeris ExplainableAi becauseit specifically refers tothe abilityof a systemto describethe logic behind its decisions oroutput ina way that is understandable to humans. This is a key part of regulatory and ethical frameworks and is directly related to addressingthe black-boxproblem inAI.
From the AIGP ILT Participant Guide (Module on Transparency and Explainability):
"Explainability refers to the understanding of how a black-box model works. The black-box problem exists because some models are too complex for human interpretation. Explainability methods aim to provide meaningful insight into the logic and decision-making of AI systems." Also, according to the AI Governance in Practice Report2025:
"Explainability refers to the representation of the underlying mechanisms of the AI system's operation... a key tenet of AI governance due to the desire to understand how AI systems are built, managed and maintained." Thus, while Trustworthy and Responsible AI are broader concepts,explainabilityspecifically targetsthe regulatoryconcern about understanding outputs.
NEW QUESTION # 102
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