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ISACA AAIA Exam Syllabus Topics:

Topic	Details
Topic 1	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.
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	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.

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ISACA Advanced in AI Audit Sample Questions (Q149-Q154):

NEW QUESTION # 149
Which of the following is the BEST indicator that an organization has mature MLOps practices?

A. All models are retrained manually once per year
B. Automated CI/CD pipelines with testing, validation, monitoring, and rollback capabilities
C. Models are deployed directly by data scientists without a pipeline
D. Model documentation is optional

Answer: B

Explanation:
Mature MLOps integrates automated testing, validation gates, continuous monitoring, and rollback mechanisms into the deployment pipeline, mirroring DevOps discipline applied to machine learning.

NEW QUESTION # 150
Which of the following is MOST important to have in place when initially populating data into a data frame for an AI model?

A. The code for separating data into training and testing data sets
B. An approved risk assessment for including, excluding, or subsequently dropping data attributes from the model
C. An analysis of exploratory data that checks for incorrect data types, null values, and duplicate entries
D. The box charts, histograms, scatterplots, and Venn diagrams that identify correlations and outliers

Answer: C

Explanation:
Exploratory Data Analysis (EDA) is critical during the initial stages of AI model development. According to the AAIA™ Study Guide, performing EDA-including identifying null values, incorrect data types, or duplicates-ensures that the data fed into the model is clean and reliable.
"Initial data frames should be subject to thorough EDA to uncover data quality issues. These issues, if not addressed early, can severely affect model training and predictive accuracy." While separating data sets (B) and visualizations (A) are important steps in later phases, C is foundational to ensure readiness for model training. Risk assessments (D) are necessary but not the first operational step.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Exploratory Data Analysis and Preprocessing"

NEW QUESTION # 151
An organization uses an AI image generation platform to create promotional materials. An IS auditor identifies that the platform includes copyrighted images in its training dat a. Which of the following is the auditor's BEST recommendation to address this issue?

A. Use a platform that certifies the provenance and licensing of its training data.
B. Suspend the use of the platform until the training data is sanitized.
C. Implement a manual review process to ensure no copyrighted images are used in generated outputs.
D. Label all AI-generated images to disclaim the possibility of third-party content.

Answer: A

NEW QUESTION # 152
Which of the following would be the BEST time to create a data governance plan for a new AI solution?

A. Prior to moving into production
B. During data collection
C. During the testing phase
D. As part of the initial design

Answer: D

Explanation:
In AI development, " Privacy by Design " and " Governance by Design " are fundamental principles. The data governance plan should be created " As part of the initial design " phase. This ensures that the requirements for data quality, lineage, labeling, and privacy are integrated into the architecture from the beginning.
According to the AAIA™ framework, attempting to retrofit governance during the testing (Option A) or production (Option B) phases is often impossible or cost-prohibitive, as the data may have already been corrupted or collected in a non-compliant manner. Planning at the design stage mitigates the risk of building on a flawed foundation.

NEW QUESTION # 153
When reviewing contracts or other lengthy documentation in the planning phase, which of the following tools would BEST extract relevant information?

A. Predictive analytics
B. Robotic process automation (RPA)
C. Natural language processing
D. Autoregressive sequencing model

Answer: C

Explanation:
Natural Language Processing (NLP) enables systems to understand, summarize, and extract relevant insights from unstructured text data such as contracts, emails, or reports. The AAIA™ Study Guide recognizes NLP as the optimal tool for document review during audit planning or risk assessment phases.
"NLP techniques can efficiently process large volumes of documentation, highlight key clauses, and identify inconsistencies or risk indicators. This enhances audit productivity and thoroughness." While RPA (A) automates repetitive tasks, it doesn't interpret content. Predictive analytics (C) forecasts trends, and autoregressive models (B) generate sequences, not summarize documents.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "NLP Applications in Audit Planning and Risk Identification"

NEW QUESTION # 154
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