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| Section | Objectives |
|---|---|
| AI Auditing Tools and Techniques | |
| AI Operations | |
| AI Governance and Risk |
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NEW QUESTION # 301
Which of the following AI documents would support an IS auditor assessing hyperparameter tuning records?
Answer: A
Explanation:
Hyperparameter tuning involves adjusting settings like learning rate, batch size, or layer depth to optimize performance. This process is documented in "Model development logs," which record the different "experiments" run by data scientists, the parameters used in each, and the resulting performance metrics (e.g., accuracy or loss). For an auditor, these logs are the "Audit Trail" of the model's construction.
NEW QUESTION # 302
Which of the following presents the GREATEST risk when an organization deploys a machine learning model in a public cloud environment for real-time predictions?
Answer: A
Explanation:
In areal-time predictionenvironment (e.g., fraud detection, medical triage, automotive risk),latency and inference speeddirectly affect safety, accuracy, and business performance.
If the SLAdoes 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, theoperational riskrelated to latency is the most immediate and severe.
Limited AI skills among cloud employees (A) is not directly relevant since customers maintain operational responsibility.
References:
AAIA Domain 2: AI Operations - Real-Time Systems, Performance Guarantees
NEW QUESTION # 303
Which of the following is the PRIMARY purpose of an AI acceptable use policy?
Answer: D
Explanation:
An AI acceptable use policy (AUP) defines how AI tools and technologies should be ethically and responsibly used within an organization. According to the AAIA™ Study Guide, the primary goal of an AUP is to prevent misuse and promote adherence to ethical, legal, and operational standards.
"An AI acceptable use policy provides governance over how AI tools may be used, especially regarding data handling, fairness, and prohibited uses. It aligns employee actions with organizational values and compliance requirements." Monitoring procedures (B), training (C), and taxonomy explanations (D) may be included in broader AI documentation, but the AUP's core purpose is ethical usage governance.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Policies, Standards, and Ethical Frameworks for AI"
NEW QUESTION # 304
Which of the following would be MOST useful for an IS auditor when testing high-impact rare scenarios that have not yet occurred in a production environment?
Answer: C
Explanation:
For "edge cases" or "rare scenarios" (often called "black swan" events), historical data is insufficient because the events haven't happened yet. "Synthetic data" is artificially generated data that maintains the statistical properties of real data but includes simulated rare events. This allows auditors to "stress test" how the AI model would react to extreme conditions, such as a massive market crash or a rare medical emergency. Anonymized or de-identified data only reflects past occurrences. Using synthetic data is a proactive testing strategy to ensure the AI's safety and robustness in unpredictable environments.
NEW QUESTION # 305
Which of the following do supervised AI learning models PRIMARILY use to train algorithms?
Answer: D
Explanation:
Supervised learning is a foundational type of machine learning in which the model is trained on a labeled dataset. According to the AAIATM Study Guide, labeled data includes input features along with the correct output, enabling the model to learn the mapping function accurately.
"In supervised learning, models learn from input-output pairs provided in the training data. This method enables predictive modeling tasks such as classification and regression." Unlabeled data (A) is used in unsupervised learning; clustered data (B) is a technique rather than a data type; and randomized data (D) refers to distribution strategy, not labeling. Hence, labeled data is the correct answer.
NEW QUESTION # 306
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