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| Section | Objectives |
|---|---|
| AI Governance and Risk | |
| AI Operations | |
| AI Auditing Tools and Techniques |
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NEW QUESTION # 278
Which of the following is the BEST approach to mitigate the risk of " AI model degradation " ?
Answer: D
Explanation:
Model degradation occurs as the " Freshness " of the training data wanes and real-world conditions evolve.
The most robust control is " Periodic human reviews " (Human-in-the-Loop). Human experts can identify " drift " in logic or common-sense failures that automated systems might miss. Relying on model-generated data (Option A) can lead to " Model Collapse, " where the AI begins to drift into nonsensical patterns by reinforcing its own previous outputs. Human oversight ensures the model remains grounded in reality and aligned with business objectives.
NEW QUESTION # 279
After AI training data has been tested for biases, which of the following is MOST important to check to validate the effectiveness of the testing?
Answer: A
Explanation:
Even after identifying and mitigating bias, organizations must ensure thatAI outputs do not create unacceptable risks.
AAIA emphasizes that bias mitigation must result in:
* Fair outcomes
* Justifiable predictions
* No disproportionate harm to any demographic group
* Alignment with organizational risk tolerance
Option B reflects this requirement, ensuring that the model's real-world impact aligns with documented risk thresholds.
Option A is supportive but not validation of effectiveness.
Option C is performance-related, not fairness-related.
Option D is privacy-related, not bias-related.
Thus, confirming output impacts against risk tolerance is the most important validation step.
References:
AAIA Domain 5: Ethical AI, Fairness Validation
AAIA Domain 1: Risk Governance and Threshold Assessments
NEW QUESTION # 280
An AI-based marketing analytics tool is trained on data that is five years old. Which of the following is MOST likely to occur?
Answer: D
Explanation:
Model drift (also called concept drift) occurs when the data used for training no longer reflects the current environment. In marketing, consumer behaviors, trends, and demographics change rapidly; therefore, a model trained on five-year-old data will suffer from significant drift. The AAIA™ manual explains that this results in the model making predictions based on obsolete patterns, leading to poor accuracy and potentially biased or irrelevant marketing recommendations. Options A, B, and C refer to security attacks, whereas " Model drift " is the natural operational degradation that occurs when AI systems are not regularly updated with fresh, representative data.
NEW QUESTION # 281
Which of the following strategies used by modelers to enhance data accuracy has the GREATEST risk of bias and information loss?
Answer: B
Explanation:
Imputing missing values using the mean, median, or mode is a common technique to fill blanks in datasets.
However, according to the AAIA™ Study Guide, this method introduces a significant risk of information distortion, especially if the data is not normally distributed or if imputed values disproportionately impact underrepresented groups.
"Simple imputation can reduce data variability and reinforce existing biases. It may also misrepresent the true distribution of the data, leading to skewed model outputs." Other options (B, C, D) may involve transformations, but they do not inherently cause as much bias or data misrepresentation as A.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Data Imputation and Transformation Risks"
NEW QUESTION # 282
A healthcare organization uses patient data to train an AI model for early disease detection. Which of the following practices provides the BEST assurance that personal data is secure and its integrity is maintained?
Answer: D
Explanation:
In healthcare AI applications, protecting patient data is critical. The AAIA™ Study Guide identifies anonymization as one of the most effective strategies to preserve privacy and maintain data integrity. When combined with quality checks, it ensures data accuracy and compliance with health data protection regulations (e.g., HIPAA, GDPR).
"Anonymizing sensitive data removes identifying attributes, significantly reducing risk if data is accessed or leaked. Ongoing data quality checks ensure the integrity and utility of the anonymized dataset." While encryption (A) and access controls (C) are necessary technical safeguards, D provides the strongest dual assurance of privacy and accuracy. Option B focuses on model management rather than data security.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Privacy and Security of Sensitive AI Data"
NEW QUESTION # 283
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