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[General] AAIA Key Concepts - Exam AAIA Collection Pdf

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【General】 AAIA Key Concepts - Exam AAIA Collection Pdf

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ISACA Advanced in AI Audit Sample Questions (Q35-Q40):NEW QUESTION # 35
When auditing an AI system, which of the following steps ensures that AI model behavior is aligned with organizational objectives?
  • A. Problem framing
  • B. Model training
  • C. Algorithm debugging
  • D. Data transformation
Answer: A
Explanation:
Problem framing (option D) is the process of clearly defining the purpose, scope, and desired outcomes of an AI system before it is built or deployed. According to the ISACA AAIA™ Study Guide, "problem framing is the critical first step that aligns model development and subsequent behaviors with the strategic and operational objectives of the organization." If the AI problem is not framed in accordance with organizational goals, even a technically successful AI model could generate outputs that do not support the organization's mission or priorities.
Algorithm debugging, data transformation, and model training are all important phases but rely on the initial problem framing to ensure their efforts are correctly directed.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Project Lifecycle Alignment"

NEW QUESTION # 36
A generative AI system has a validation control in place to reject inappropriate questions by checking them against built-in ethical standards. Which of the following enables malicious actors to circumvent this control through prompt engineering?
  • A. Submitting the same questions in a foreign language translated by another AI-based system
  • B. Randomly placing keywords unrelated to the main topic
  • C. Asking the same questions later when the algorithm has changed after further learning
  • D. Presenting theoretical situations to justify the reason for asking the questions
Answer: D

NEW QUESTION # 37
Which of the following do supervised AI learning models PRIMARILY use to train algorithms?
  • A. Labeled data sets
  • B. Randomized data sets
  • C. Unlabeled data sets
  • D. Clustered data sets
Answer: A
Explanation:
Supervised learning is a foundational type of machine learning in which the model is trained on a labeled dataset. According to the AAIA™ 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.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Types of AI Learning Models"

NEW QUESTION # 38
An IS auditor is assessing the implementation of AI tools for evidence collection involving multiple data sources. Which of the following outcomes BEST indicates that AI-driven evidence collection has improved the audit process?
  • A. Extended reporting timelines that allow for AI model retraining
  • B. Ability to rely on unstructured data with minimal cleansing
  • C. Reduced time spent gathering data with fewer errors in evidence compilation
  • D. Elimination of human judgment in data and evidence analysis
Answer: C
Explanation:
AI-driven evidence collection should enhanceefficiency and accuracy. The BEST indicator of improvement isreduced time spent gathering data with fewer errors(B), showing that AI has streamlined data extraction, consolidation, and initial validation without compromising quality. AAIA's content on AI in audit processes highlights benefits such asautomation of repetitive tasks, improved coverage, and higher-quality evidence.
Extended timelines for retraining (A) suggest inefficiency rather than improvement. Eliminating human judgment (C) is neither realistic nor recommended; professional skepticism and auditor judgment remain essential. Relying on unstructured data with minimal cleansing (D) can increase risk of misinterpretation or noise. Therefore, more efficient and accurateevidence compilationis the clearest positive outcome.
References:
ISACA,AAIA Exam Content Outline- Domain 3: AI Auditing Tools and Techniques (efficiency and quality improvements in audit).
ISACA internal audit and data analytics guidance on AI's value in evidence collection.

NEW QUESTION # 39
When auditing a research agency's use of generative AI models for analyzing scientific data, which of the following is MOST critical to evaluate in order to prevent hallucinatory results and ensure the accuracy of outputs?
  • A. The measures in place to ensure the appropriateness and relevance of input data for generative AI models
  • B. The frequency of data audits verifying the integrity and accuracy of inputs
  • C. The algorithms for generative AI models designed to detect and correct data bias before processing
  • D. The effectiveness of data anonymization processes that help preserve data quality
Answer: A
Explanation:
Ensuring that input data is appropriate and relevant (option D) is the most critical factor in preventing hallucinations-where generative models produce fabricated or misleading outputs. The AAIA™ Study Guide notes, "Generative models are highly sensitive to input data; inaccurate, irrelevant, or inappropriate inputs increase the likelihood of nonsensical or incorrect outputs." While bias detection, data quality audits, and anonymization are important, ensuring the relevance and suitability of input data is foundational for reliable generative AI performance.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Input Data Governance for Generative AI"

NEW QUESTION # 40
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