ISACA AAIA Online Praxisprüfung - AAIA Exam

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ISACA AAIA Prüfungsplan:

ThemaEinzelheiten
Thema 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.
Thema 2
  • 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.
Thema 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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AAIA Exam & AAIA Dumps

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ISACA Advanced in AI Audit AAIA Prüfungsfragen mit Lösungen (Q139-Q144):

139. Frage
An IS auditor reviewing the latest AI chatbot release identifies that, despite high accuracy rates, non-English users complain about the model ' s poor accuracy. Which of the following controls is BEST at ensuring detection of subgroup regressions?

Antwort: C

Begründung:
A common pitfall in AI performance is " aggregate accuracy, " which masks poor performance in specific demographics or subgroups. To detect these " subgroup regressions, " auditors should look for " comparative evaluations " where the model is tested separately for different languages. " Parity thresholds " establish the maximum allowable difference in performance between the majority group (English) and minority groups.
This ensures fairness and consistent user experience. Post-go-live human review is reactive, and translating everything to English ignores the nuances of the original language that likely caused the errors in the first place.


140. Frage
A healthcare organization uses an AI model to analyze patient data and provide diagnostic recommendations. Which of the following MOST effectively detects data drift related to the model's predictions?

Antwort: B


141. Frage
When auditing a machine learning (ML) solution, false positives can BEST be assessed by examining the level of:

Antwort: B

Begründung:
Precision measures the proportion of true positives among all positive predictions. A low precision rate indicates a high rate of false positives. The AAIA™ Study Guide recommends using precision when the goal is to minimize incorrect positive alerts, which is especially relevant in fraud detection, cybersecurity, and classification models.
"Precision is the key metric when false positives have a significant operational cost. It provides insight into the model's ability to avoid incorrect positive classifications." Accuracy and recall give broader insights, but only precision directly measures false positive risk.
Completeness is not a standard ML metric.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Operations and Performance," Subsection: "Performance Metrics for Classification Models"


142. Frage
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?

Antwort: D

Begründung:
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.


143. Frage
Which of the following is the BEST way to ensure an AI model's outputs are effectively controlled for bias?

Antwort: B

Begründung:
The most objective evidence of bias control is "Performance Parity". If the accuracy, precision, and recall are "Similar across various demographic groups," it demonstrates that the model is functioning equitably for all users.


144. Frage
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