IAPP AIGP Exam Dumps

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.

Training data is used to enable a model to detect and learn patterns. During the training phase, the model learns from the labeled data, identifying patterns and relationships that it will later use to make predictions on new, unseen data. This process is fundamental in building an AI model's capability to perform tasks accurately. Reference: AIGP Body of Knowledge on Model Training and Pattern Recognition.

Differential privacy is a technique used to ensure that the inclusion or exclusion of a single data point does not significantly affect the outcome of any analysis, providing a way to mathematically prove that no specific piece of training data has more than a negligible effect on the model or its output. This is achieved by introducing randomness into the data or the algorithms processing the data. In the context of training large language models (LLMs), differential privacy helps in protecting individual data points while still enabling the model to learn effectively. By adding noise to the training process, differential privacy provides strong guarantees about the privacy of the training data.

The correct answers are options 1, 3, and 4.

Option 1 is correct: Pre-deployment assessment must include technical validation of model performance, documentation of failure modes, and confirmation of a human-in-the-loop workflow. Because this is a safety-critical application where the model assists (not replaces) human judgment, maintaining human authority is essential to responsible AI governance.

Option 2 is incorrect: Removing human review in a safety-critical context is irresponsible governance. Even if a model achieves high accuracy, relying on it for fully autonomous enforcement of safety protocols without human oversight violates core AI governance principles. Monthly audits alone are insufficient; continuous monitoring is typically required for deployment.

Option 3 is correct: Organizational readiness includes stakeholder understanding, documented governance policies for monitoring and updates, and escalation protocols. These governance activities ensure the organization can manage the model responsibly after deployment.

Option 4 is correct: Pre-deployment assessment should verify alignment with regulatory standards (e.g., OSHA requirements for safety inspections) and confirm that liability and insurance frameworks cover AI-related risks. This is part of comprehensive risk assessment before deployment.

Option 5 is incorrect: While infrastructure capacity matters, it is not a primary governance consideration for pre-deployment readiness. Additionally, retraining should be driven by performance degradation or changing risk profiles—not arbitrary annual schedules—or the model could be unnecessarily updated, introducing new risks.

C is the correct statement, subject to the statutory thresholds. Article 36 of South Korea's AI Basic Act requires an AI business operator without an address or business office in Korea to designate a domestic representative when it meets prescribed criteria concerning users, revenue, or other specified conditions. The implementing decree establishes those thresholds, including qualifying revenue and Korean-user levels. The Act also applies extraterritorially where conduct outside Korea affects the Korean market or users. Generative AI is not categorically excluded, eliminating B. The Korean framework does not simply reproduce the EU AI Act's prohibited-practices regime described in D, and 'frontier models' are not established as the distinct regulatory category suggested by A. Therefore, domestic-representative requirements make C the best answer.

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