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| Certification Vendor: | IAPP (International Association of Privacy Professionals) |
|---|---|
| Exam Name: | IAPP Certified Artificial Intelligence Governance Professional (AIGP) Exam |
| Exam Number: | AIGP |
| Passing Score: | 300 (scaled score out of 100–500) |
| Related Certifications: | CIPP/US CIPT CIPM CIPP/E |
| Exam Format: | Single-select, Multi-select, Multiple-choice, Scenario-based questions |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 165 minutes |
| Available Languages: | English |
| Exam Price: | USD 799 (non-member) / USD 649 (IAPP member) |
| Real Exam Qty: | 100 (85 scored + ~15 unscored pilot questions) |
| Recommended Training: | IAPP Official AIGP Training and Resources AIGP Practice Exam (Official IAPP Store) |
| Exam Registration: | Official IAPP AIGP Exam Registration Pearson VUE Scheduling Portal (via IAPP account) |
| Sample Questions: | IAPP AIGP Sample Questions |
| Exam Way: | Computer-based exam delivered via Pearson VUE (test center or online proctored OnVUE) |
| Pre Condition: | None |
| Official Syllabus URL: | https://iapp.org/certify/aigp |
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NEW QUESTION # 140
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: D
Explanation:
The correct answer is B. The EU AI Act assigns a tiered penalty system based on the severity of the violation. A breach of obligations related to high-risk AI systems falls 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: The highest penalty (€35 million or 7%) applies to prohibited AI uses, not to obligations for high- risk systems.
NEW QUESTION # 141
Which of the following considerations is the most important in mitigating the potential of bias in training and testing data?
Answer: D
Explanation:
The correct answer is B because ensuring that training and testing data is representative is the most critical factor in mitigating bias in AI systems. AI governance frameworks emphasize that biased or unrepresentative datasets can lead to discriminatory outcomes, particularly when certain demographic groups are underrepresented or overrepresented. Representative data helps ensure that the model performs fairly and accurately across different populations. While privacy-enhancing tools and consent address legal and ethical data use, they do not directly prevent bias in model outcomes. Similarly, assessing the sufficiency of third- party data focuses on quantity rather than fairness or distribution. Effective bias mitigation begins with evaluating whether the dataset reflects the diversity and characteristics of the real-world population the AI system will impact.
NEW QUESTION # 142
Under the EU AI Act, which of the following compliance actions applies only to General Purpose AI models with systemic risk?
Answer: C
Explanation:
Under the EU AI Act, General-Purpose AI models with systemic risk are required to publish a detailed summary of the data used to train the model. This transparency measure ensures that users and regulatory bodies can assess the model's data sources and potential biases.
NEW QUESTION # 143
Which of the following is NOT a common type of machine learning?
Answer: C
Explanation:
The common types of machine learning include supervised learning, unsupervised learning, reinforcement learning, and deep learning. Cognitive learning is not a type of machine learning; rather, it is a term often associated with the broader field of cognitive science and psychology. Reference: AIGP BODY OF KNOWLEDGE and standard AI/ML literature.
NEW QUESTION # 144
CASE STUDY
Please use the following to answer the next question:
A mid-size US healthcare network has decided to develop an AI solution to detect a type of cancer that is most likely to arise in adults. Specifically, the healthcare network intends to create a recognition algorithm that will perform an initial review of all imaging and then route records to a radiologist for secondary review pursuant to agreed-upon criteria (e.g., a confidence score below a threshold).
To date, the healthcare network has:
- Defined its AI ethical principles.
- Conducted discovery to identify the intended uses and success
criteria for the system.
- Established an AI risk committee.
- Assembled a cross-functional team with clear roles and
responsibilities.
- Created policies and procedures to document standards, workflows,
timelines and risk thresholds during the project.
The healthcare network intends to retain a cloud provider to host the solution. It also intends to retain a large consulting firm to supplement its small data science team and help develop the algorithm using the healthcare network's existing data and de-identified data that is licensed from a large US clinical research partner.
What is the most important step for the healthcare network to take when mapping its existing data to the clinical research partner data?
Answer: A
Explanation:
Identifying fits and gaps in the combined data is crucial to understand whether the datasets are compatible and sufficient for training the AI model effectively.
NEW QUESTION # 145
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