Pass Guaranteed Quiz 2026 Pass-Sure ISACA AAIA: Test ISACA Advanced in AI Audit Collection

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ISACA AAIA Exam Syllabus Topics:

Topic	Details
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 AAIA study guide

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ISACA Advanced in AI Audit Sample Questions (Q142-Q147):

NEW QUESTION # 142
Which of the following is the GREATEST data quality risk when using an AI tool to assist with audit procedures?

A. Applying automated anomaly detection without human oversight
B. Training models on historical audit results generated prior to AI adoption
C. Embedding AI audit tools in transactional systems without user training
D. Utilizing unstructured data sources without standardized preprocessing

Answer: D

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 # 143
The PRIMARY purpose of maintaining an audit trail in AI systems is to:

A. Facilitate transparency and traceability of decisions.
B. Ensure compliance with regulatory standards for AI.
C. Measure computational efficiency.
D. Analyze model accuracy and fairness.

Answer: A

NEW QUESTION # 144
An AI tool is being implemented for a regional healthcare organization. Which of the following training methods BEST ensures the AI output does not reveal whether someone's personal data was used?

A. Transfer learning using public health data sets
B. Data augmentation during training to improve privacy
C. Differential privacy applied during model training
D. Supervised learning with labeled patient records

Answer: C

Explanation:
Differential privacy introduces carefully calibrated noise during training or query responses so that it becomes mathematically difficult to infer whether any specific individual's record is included in the training set. For healthcare data--highly sensitive and subject to strict privacy laws--this technique directly supports privacy-by-design, reducing the risk that model outputs leak membership information or reconstruct personal records.

NEW QUESTION # 145
An insurance organization deployed an AI tool for assigning customer risk levels. An IS auditor discovers that the learning algorithm is vulnerable to adversarial attacks. Which of the following is the BEST course of action?

A. Assess the response time to the attacks.
B. Evaluate the number of system logs maintained.
C. Review the speed of learning adaptation.
D. Validate the process for handling manipulated inputs.

Answer: D

Explanation:
Adversarial attacks involve " manipulated inputs " (poisoning or evasion) designed to trick a model into making incorrect decisions (e.g., assigning a high-risk driver a low-risk premium). To mitigate this, the auditor must " Validate the process for handling manipulated inputs. " This includes checking for robust input validation, anomaly detection on incoming data, and " adversarial training " where the model is intentionally exposed to these attacks during development to build resilience. Focusing on logs or speed (Options A, B, and C) does not address the fundamental vulnerability of the algorithm to technical manipulation.

NEW QUESTION # 146
An IS auditor is interviewing management about implemented controls around machine learning (ML) models deployed in the production environment. Which of the following schedules for reviewing the performance of a deployed model would be of GREATEST concern to the auditor?

A. On an annual recurring basis
B. After functionality changes
C. One time prior to migrating to production
D. After changes to hardware and software platforms

Answer: C

Explanation:
Only reviewing an ML model's performance one time prior to migrating to production (option C) is of greatest concern. The AAIA™ Study Guide emphasizes that "AI and ML models require continuous monitoring and periodic performance reviews in production to detect issues such as data drift, model degradation, or evolving risk factors." A single pre-production review fails to capture these changes and risks, potentially resulting in undetected failures or compliance issues.
Periodic (including annual) and event-driven reviews are necessary to ensure ongoing model reliability.
Reference:ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "Continuous Monitoring and Review of Deployed AI Models"

NEW QUESTION # 147
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