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Free CSPAI Exam Braindumps
Certified Security Professional in Artificial Intelligence Questions 11–15 (page: 4)

Updated On: 10-Feb-2026
Page 4 of 11
PDF with 50 Questions

What is a primary step in the risk assessment model for GenAI data privacy?

  1. Ignoring data sources to speed up assessment.
  2. Conducting data flow mapping to identify privacy risks.
  3. Limiting assessment to model outputs only.
  4. Relying on vendor assurances without verification.

Answer(s): B



Which of the following is a characteristic of domain-specific Generative AI models?

  1. They are designed to run exclusively on quantum computers
  2. They are tailored and fine-tuned for specific fields or industries
  3. They are only used for computer vision tasks
  4. They are trained on broad datasets covering multiple domains

Answer(s): B



In line with the US Executive Order on AI, a company's AI application has encountered a security vulnerability.
What should be prioritized to align with the order's expectations?

  1. Implementing a rapid response to address and remediate the vulnerability, followed by a review of security practices.
  2. Immediate public disclosure of the vulnerability.
  3. Halting all AI projects until a full investigation is complete.
  4. Ignoring the vulnerability if it does not affect core functionalities.

Answer(s): A



An organization is evaluating the risks associated with publishing poisoned datasets.
What could be a significant consequence of using such datasets in training?

  1. Increased model efficiency in processing and generation tasks.
  2. Enhanced model adaptability to diverse data types.
  3. Compromised model integrity and reliability leading to inaccurate or biased outputs
  4. Improved model performance due to higher data volume.

Answer(s): C



During the development of AI technologies, how did the shift from rule-based systems to machine learning models impact the efficiency of automated tasks?

  1. Enabled more dynamic decision-making and adaptability with minimal manual intervention
  2. Enhanced the precision and relevance of automated outputs with reduced manual tuning.
  3. Improved scalability and performance in handling diverse and evolving data.
  4. Increased system complexity and the requirement for specialized knowledge,

Answer(s): A



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