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Which of the following is the MOST important task when gathering data during the AI system development process?
A. Training the system
B. Stratifying the data
C. Cleaning the data
D. Isolating the system
Answer: C
Explanation:
Data cleaning is a foundational task in the AI development lifecycle. The AAIA™ Study Guide identifies data quality-ensuring completeness, accuracy, consistency, and correctness-as critical to building effective and unbiased AI systems. Cleaning the data involves removing duplicates, correcting errors, addressing missing values, and standardizing formats.
"Data cleaning is a prerequisite for effective training and evaluation. Poor-quality data leads to inaccurate or misleading model outputs, increasing operational and ethical risks." While training (D) is essential, it must occur only after the data has been adequately prepared. Stratification (A) supports certain modeling approaches but is secondary to data integrity. Therefore, C is the most important task at the data-gathering stage.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Data Collection and Preparation"
NEW QUESTION # 57
An IS auditor is evaluating an organization's incident management program to ensure it is sufficiently prepared to manage AI-related incidents. Which of the following is MOST important for the auditor to validate?
A. The program prioritizes incidents based on alignment with industry leading practices.
B. The program includes processes to respond to AI model drift and data integrity attacks.
C. The program uses past AI-related incidents and resolutions to categorize current incidents.
D. The program mandates retraining AI systems after incidents are investigated.
Answer: B
NEW QUESTION # 58
Which of the following is the PRIMARY objective of AI governance?
A. Defining clear roles and responsibilities for AI development, use, and oversight
B. Ensuring controls over AI are designed well and operate effectively
C. Implementing compliance and ethics controls for AI initiatives
D. Promoting a positive return on investment (ROI) from AI projects
Answer: A
NEW QUESTION # 59
A healthcare organization uses data clustering to group patients by medical history for personalized treatment recommendations. Which of the following is the GREATEST privacy risk associated with this practice?
A. Irrelevant features in the data may result in inaccurate or biased treatments.
B. Clusters can reveal sensitive personal information depending on how the information is presented.
C. Clustering increases the complexity of the model, making data harder to anonymize.
D. The clustering requires more data, increasing the risk of a privacy breach.
Answer: B
Explanation:
Clustering, especially in sensitive domains like healthcare, can inadvertently expose confidential patient data if the resulting groups are too specific or reveal underlying health conditions. The AAIA™ Study Guide warns that clustering can increase privacy risks when small, homogenous groups are formed that effectively re-identify individuals or reveal sensitive traits.
"Clustering results must be carefully reviewed to prevent indirect re-identification or unintended exposure of sensitive traits. Ethical handling of aggregated patient data is essential to protect individual privacy." While A and B involve general concerns, and C focuses on performance, D directly addresses the most significant privacy threat: exposure through cluster outputs.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Ethical and Legal Considerations in AI," Subsection: "Data Anonymization and Re-identification Risks"
NEW QUESTION # 60
Which of the following presents the MOST significant barrier to generative AI model explainability?
A. Bias within data sets used for model training
B. Insufficient staff experience with generative AI tools
C. Rapid evolution of algorithm capabilities
D. Lack of alignment between stakeholder groups
Answer: C
Explanation:
The rapid evolution of modern generative AI architectures (option B) is the largest barrier to explainability.
Complex deep learning models like LLMs, diffusion models, and transformer-based architectures involve millions or billions of parameters, making it extremely challenging to determine precisely how outputs are produced.
AAIA notes that explainability challenges arise because:
* Model structures are highly complex
* Parameter interactions are nonlinear
* Internal representations are not human-interpretable
* Continuous updates make documentation outdated
* Training data and latent representations create opaque reasoning chains Bias (A) affects fairness, not explainability.
Stakeholder alignment (C) is a governance issue.
Lack of staff experience (D) is a training problem, not a structural barrier.
The inherent technical complexity and speed of model evolution are the primary obstacles.
References:
AAIA Domain 5: Explainability Challenges
AAIA Domain 1: Advanced AI Model Architectures
NEW QUESTION # 61
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