AAIA Exam Format | Exam AAIA Quiz

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ISACA AAIA Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Audit (AAIA) Exam
Exam Number:	AAIA
Exam Duration:	120 minutes
Exam Price:	USD 459 (member), USD 599 (non-member)
Certificate Validity Period:	Not publicly specified (requires ongoing CPE maintenance after certification)
Exam Format:	Computer-based, Multiple-choice, Closed-book, Remote proctoring or test center
Passing Score:	65%
Related Certifications:	CPA CIA CISA
Available Languages:	English
Real Exam Qty:	55
Recommended Training:	Official AAIA Training Course Providers ISACA AI Audit Training Resources
Exam Registration:	ISACA AAIA Certification Page
Sample Questions:	ISACA AAIA Sample Questions
Exam Way:	Computer-based exam delivered via PSI test centers or remote proctoring
Pre Condition:	Must hold an active CISA, CIA, CPA, or equivalent ISACA-approved advanced auditing certification
Official Syllabus URL:	https://www.isaca.org/credentialing/aaia

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Exam AAIA Quiz | Reliable AAIA Test Objectives

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ISACA AAIA Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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.

ISACA Advanced in AI Audit Sample Questions (Q280-Q285):

NEW QUESTION # 280
Which role is BEST suited to define the implementation roadmaps for adopting AI solutions?

A. Steering committee
B. Risk management committee
C. Product management
D. Internal audit

Answer: A

Explanation:
A steering committee is responsible for enterprise-wide strategic decisions, technology alignment, investment prioritization, and governance oversight.
AAIA states that AI implementation roadmaps must be driven by a group that can evaluate:
* Organizational strategy
* Technology readiness
* Budget allocations
* Risk appetite
* Regulatory obligations
* Cross-functional impacts
The risk committee (A) focuses only on risk, not implementation. Product management (C) has a narrow scope. Internal audit (D) evaluates controls, not roadmaps.
Thus, the steering committee is the appropriate authority.
References:
AAIA Domain 1: AI Governance Structures
AAIA Domain 4: Strategic Alignment and Oversight

NEW QUESTION # 281
What should be the IS auditor ' s GREATEST concern when an organization is mixing AI-generated content into training data without labeling?

A. The model outputs become inconsistent across similar queries.
B. The model loses alignment safeguards.
C. The model reinforces its own mistakes as if they were facts.
D. The model propagates outdated information.

Answer: C

Explanation:
This is known as " Data Contamination " or " Model Autophagy " . When an AI model is trained on data it (or another AI) previously generated, it creates a feedback loop. Errors, hallucinations, and biases in the generated content are re-ingested as " ground truth, " which causes the model to " Collapse " and lose its ability to accurately reflect the real world. The ISACA AAIA™ manual emphasizes that training data must be
" Primary " and " Authentic " to ensure model integrity. Failing to distinguish between human and AI content prevents the organization from detecting and correcting systemic errors in the model ' s logic.

NEW QUESTION # 282
Which of the following controls helps mitigate the risk of competitors poisoning data utilized by a machine learning (ML) model performing sentiment analysis of product reviews?

A. Hiring a marketing firm to text links to customers requesting product reviews for monetary compensation
B. Requiring customers to authenticate access to their accounts prior to writing product reviews
C. Peer reviewing code that acquires product reviews from social media posts
D. Augmenting the unbalanced product review data set with the use of oversampling by the model developer

Answer: B

Explanation:
To prevent data poisoning, especially in systems relying on publicly submitted content such as product reviews, access authentication is critical. The AAIATM Study Guide specifies that authenticated input sources help ensure data integrity and traceability, reducing the likelihood of adversarial or malicious contributions.
"Limiting review input to authenticated users restricts unauthorized actors--such as competitors or bots--from submitting biased or harmful data. This control protects model training and outputs from being manipulated."

NEW QUESTION # 283
An IS auditor identified that an AI model based on historical data caused " data lag, " leading to poor real- time decision-making. Which of the following is the BEST mitigation strategy?

A. Expand the volume of available test data.
B. Validate the diversity of data.
C. Perform feature weight updating.
D. Verify that the data source is credible.

Answer: C

Explanation:
" Data lag " occurs when the model relies too heavily on old patterns that no longer reflect the current reality (e.g., pre-pandemic shopping habits). To mitigate this, " feature weight updating " is used to give more importance to the most recent data points while " decaying " the influence of older data. This allows the model to adapt more quickly to real-world shifts without requiring a full rebuild from scratch. While data diversity and volume are important for general performance, they do not specifically address the temporal " lag " issue like weight adjustment does.

NEW QUESTION # 284
An organization is evaluating whether to classify a new AI use case as "high-risk" under its internal AI risk framework. Which factor is MOST relevant to this determination?

A. The potential impact on individuals' rights, safety, or significant life opportunities (e.g., employment, credit, healthcare)
B. The programming language used
C. The size of the development team
D. The cloud provider hosting the model

Answer: A

Explanation:
Risk classification frameworks (including the EU AI Act's approach) generally tier risk based on the potential impact of the AI system's decisions on individuals, not on technical implementation details.

NEW QUESTION # 285
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