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ISACA AAIA Exam Syllabus Topics:

TopicDetails
Topic 1
  • AI GOVERNANCE AND RISK: It encompasses understanding different AI models and their life cycles, guiding AI strategy, defining roles and policies, managing AI-related risks, overseeing data privacy and governance, and ensuring adherence to ethical practices, standards, and regulations.
Topic 2
  • AI Operations: It covers managing AI-specific data needs—including collection, quality, security, and classification—applying development lifecycle methodologies with privacy and security by design, change and incident management, testing AI solutions, identifying AI-related threats and vulnerabilities, and supervising AI deployments.
Topic 3
  • Auditing Tools and Techniques: This section of the exam measures the skills of AI auditors and centers on auditing AI systems using appropriate tools and methods. It includes audit planning and design, sampling methodologies specific to AI, collecting audit evidence, using data analytics for quality assurance, and producing AI audit outputs and reports, including follow-up and quality control measures.

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ISACA Advanced in AI Audit Sample Questions (Q278-Q283):

NEW QUESTION # 278
Which of the following is the GREATEST data quality risk when using an AI tool to assist with audit procedures?

Answer: A

Explanation:
Unstructured data without standardized preprocessing (option A) creates the highest data quality risk because AI models depend heavily on the cleanliness, consistency, and structure of input data.
AAIA warns that improperly processed unstructured data leads to:
* Incorrect text extraction
* Lost contextual meaning
* Feature extraction errors
* Misclassification
* Inaccurate audit evidence
Option B is a bias or relevance risk, not data quality.
Option C is a governance/training issue.
Option D is an oversight risk, not a data quality issue.
Therefore, using unstructured data without preprocessing is the most direct threat to data quality.
References:
AAIA Domain 2: Data Preprocessing and Quality
AAIA Domain 3: AI-Assisted Audit Evidence Integrity


NEW QUESTION # 279
An IS auditor is planning for an audit of an organization ' s use of AI to screen applicants for open positions.
Which of the following is MOST important to include within the audit scope?

Answer: C

Explanation:
In recruitment AI, the integrity and fairness of the output are almost entirely dependent on the " Sources of data used. " If the training data comes from historical records that contain human bias (e.g., only hiring from certain universities or genders), the AI will codify and automate that bias. The ISACA AAIA™ manual states that auditing the " Data Source " and " Data Lineage " is the most effective way to identify the root cause of algorithmic discrimination. While data retention (Option B) is a general privacy concern, it is secondary to the high-impact risk of biased decision-making inherent in the data sources.


NEW QUESTION # 280
A company deploys a third-party AI vendor's model without visibility into its training data or architecture. What is the PRIMARY audit concern?

Answer: B

Explanation:
"Black box" third-party models limit the organization's ability to independently validate fairness, robustness, and explainability -- key AI risk domains -- making vendor due diligence and contractual transparency clauses essential.


NEW QUESTION # 281
An organization utilizes an off-the-shelf generative AI solution for internal business processing.
According to the principle of shared responsibility, which of the following areas remains the PRIMARY responsibility of the organization?

Answer: D

Explanation:
In a "Software as a Service" (SaaS) or off-the-shelf AI model deployment, the "Shared Responsibility Model" dictates that the vendor is responsible for the "Security OF the model" (infrastructure, base training, core safety), while the customer is responsible for the "Security IN the model" (usage). The organization's primary duty is "Defining usage policies and enforcing IAM" to ensure that only authorized employees use the tool and that they do not input sensitive corporate data into unvetted prompts. This governance ensures the AI is used ethically and securely within the organization's specific operational context.


NEW QUESTION # 282
An IS auditor is reviewing a dataset used by a university to train a predictive machine learning model. Which of the following MOST likely indicates risk that the model could not process all data and make necessary correlations?

Answer: C

Explanation:
A numeric field stored as an object (string) format (option C) indicates improper data typing. Models cannot correctly compute correlations or statistical relationships when numerical values are stored as textual data.
AAIA emphasizes that incorrect datatypes are one of the most common causes of ML model misbehavior, including:
* Failure to compute averages, correlations, or mathematical operations
* Silent errors in preprocessing
* Skewed learning patterns
* Incorrect feature importance evaluationsThus, the Final Grade Percent field in object format is the most significant indicator of processing risk.Options A, B, and D are valid datatypes for their respective fields and pose no inherent model processing risk.
References:
AAIA Domain 2: Data Quality, Data Types, and Preprocessing.
AAIA Domain 3: AI Readiness and Data Validation.


NEW QUESTION # 283
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