AAIA Online Practice Questions

Explanation:
In a real-time prediction environment (e.g., fraud detection, medical triage, automotive risk), latency and inference speed directly affect safety, accuracy, and business performance.
If the SLA does not include guarantees for latency, the model may fail to deliver predictions in time, leading to:
Incorrect or delayed decisions
Transaction failures
Safety incidents in time-sensitive use cases
Compliance violations in regulated domains
Although audit trails (B) and governance frameworks (D) are important, the operational risk related to latency is the most immediate and severe.
Limited AI skills among cloud employees (A) is not directly relevant since customers maintain operational responsibility.
Reference: AAIA Domain 2: AI Operations ― Real-Time Systems, Performance Guarantees

Explanation:
The AAIA™ Study Guide defines the primary objective of AI governance as establishing structure and accountability for AI initiatives. This includes clearly assigning responsibilities across development, deployment, risk management, and auditing roles to ensure that AI is used responsibly and transparently.
“AI governance establishes the policies, roles, and oversight structures that guide the ethical and
secure deployment of AI. Clear accountability helps prevent unauthorized use and ensures strategic alignment.”
Options A and C are essential components of governance but are not its core definition.
Option D is a business outcome, not a governance goal. Thus, B is the most comprehensive and accurate objective.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: “AI Governance and Risk Management,” Subsection: “Governance Objectives and Structures”

Explanation:
Inconsistent data formatting means the AI model is receiving inputs that do not match what it was trained to understand. The best corrective action is to document and enforce technical specifications for incoming data (option A).
AAIA highlights this as a fundamental data governance requirement:
Field formats
Data types
Encoding rules
Required attributes
Normalization methods
Without strict specifications, the model cannot reliably parse or classify inputs.
Option B does not address the root cause.
Option C improves human response but not data consistency.
Increasing model complexity (D) adds risk and does not fix inconsistent formatting.
Reference: AAIA Domain 2: Input Standards and Data Consistency
AAIA Domain 1: Governance and Data Pipeline Controls

Explanation:
Detecting data drift is critical in maintaining the reliability and accuracy of AI models, especially in dynamic environments like healthcare where patient populations and data characteristics can change over time. According to the ISACA Advanced in AI Audit™ (AAIA™) Study Guide, data drift refers to changes in the input data's statistical properties compared to the data on which the model was originally trained. If not detected, data drift can degrade model performance and lead to erroneous predictions.
The most effective approach to detect data drift is to continuously compare the statistical distributions of incoming (production) data with those of the training data set. This allows organizations to identify deviations in data patterns, which can be early indicators that the AI model’s predictions may no longer be valid or optimal.
As stated in the AAIA™ Study Guide under "AI Model Monitoring and Maintenance":
“Monitoring input data for distributional changes compared to the model’s training data is an
essential step in identifying data drift. Statistical tests and visualizations can help auditors and AI operators detect when the underlying data characteristics have shifted, prompting further investigation or retraining needs.”
While options such as retraining the model (option C) or adversarial testing (option D) are valuable for ongoing performance and robustness, they do not inherently detect data drift―they respond to or stress-test existing issues. Applying overrides (option B) is a human-in-the-loop safeguard, not a method for drift detection.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Model Monitoring and
Maintenance," Subsection: "Detection and Management of Data Drift"

Explanation:
Audit trails in AI systems serve to document the inputs, processes, and outputs of AI decisions, allowing stakeholders and auditors to trace how decisions were made. The AAIA™ Study Guide identifies transparency and traceability as the core functions of an AI audit trail.
“Maintaining audit logs is critical to explainability and accountability. It ensures that decisions made by AI systems can be reconstructed and assessed for accuracy, ethics, and legality.”
Although compliance (D) is an outcome and fairness (B) a goal, only A captures the foundational purpose of audit trails in AI systems.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: “AI Governance and Risk Management,” Subsection: “Auditability and Logging Mechanisms”