AAIA Practice Test

AAIA Practice Test practice questions, page 21 of 23, with answer review and explanations.

Question 101

An AI healthcare diagnostic tool requires large volumes of patient data, raising concerns about privacy and data breaches. Which of the following is the MOST effective strategy to mitigate this risk?

  1. Encrypt the data and transmit it through a secure channel.
  2. Limit the tool's access to only publicly available data sets.
  3. Collect data from all patients to use for data analysis.
  4. Use synthetic data or anonymized data sets for model training.

Question 102

Which of the following is MOST important to consider when evaluating ethical risk related to data used for training an AI model?

  1. Ability to generate diverse outputs
  2. Sensitivity and origin of training data
  3. Frequency of model updates
  4. Cleaning and validation methods for training data

Question 103

Which of the following is the GREATEST concern when an audit team relies on generative AI to create audit reports?

  1. The reports may be more likely to reflect outdated information.
  2. The reports may contain misstatements resulting from hallucinations.
  3. The reports may use inconsistent formatting from prior audit findings.
  4. The reports may tend to use generic language for audit issues.

Question 104

Which of the following is the MOST important reason to conduct regular threat modeling exercises for AI systems and data?

  1. To proactively identify potential vulnerabilities in AI systems
  2. To assess the performance of AI algorithms
  3. To comply with AI regulatory requirements
  4. To prevent instances of AI model drift

Question 105

Which of the following is the MOST important reason for applying regular software updates to AI systems operating in high-risk environments?

  1. To safeguard the systems against AI-powered zero-day exploits
  2. To accelerate model training cycles and enhance processing speed
  3. To reduce the need for human oversight of model outputs
  4. To address vulnerabilities and reduce the risk of output integrity attacks