AAISM Practice Test

AAISM Practice Test practice questions, page 6 of 12, with answer review and explanations.

Question 26

A large language model (LLM) has been manipulated to provide advice that serves an attacker's objectives. Which of the following attack types does this situation represent?

  1. Data poisoning
  2. Evasion attack
  3. Privilege escalation
  4. Model inversion

Question 27

Which area of intellectual property law presents the GREATEST challenge in determining copyright protection for AI-generated content?

  1. Enforcing trademark rights associated with AI systems
  2. Protecting trade secrets in AI technologies
  3. Determining the rightful ownership of AI-generated creations
  4. Establishing licensing frameworks for AI-generated works

Question 28

A financial institution plans to deploy an AI system to provide credit risk assessments for loan applications. Which of the following should be given the HIGHEST priority in the system's design to ensure ethical decision making and prevent bias?

  1. Regularly update the model with new customer data to improve prediction accuracy.
  2. Restrict the model's decision-making criteria to objective financial metrics only.
  3. Train the system to provide advisory results with final decisions made by human experts.
  4. Integrate a mechanism for customers to appeal decisions directly within the system.

Question 29

Which of the following security framework elements BEST helps to safeguard the integrity of outputs generated by AI algorithms?

  1. Management is prepared to disclose AI system architecture to stakeholders.
  2. Ethical standards are incorporated into security awareness programs.
  3. Risk exposure due to bias in AI outputs is kept within an acceptable range.
  4. Responsibility is defined for legal actions related to AI regulatory requirements.

Question 30

Which of the following is the BEST mitigation control for membership inference attacks on AI systems?

  1. AI threat modeling
  2. Differential privacy
  3. Cybersecurity-oriented red teaming
  4. Model ensemble techniques