P.S.ShikenPASSがGoogle Driveで共有している無料の2026 ISACA AAIAダンプ：https://drive.google.com/open?id=1KFTN7p8SRMhoml4-UIQEznIDmnBvVUye
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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Audit |
| Exam Number: | AAIA |
| Passing Score: | 65% |
| Available Languages: | English |
| Exam Format: | Computer-Based, Remotely Proctored, Multiple Choice |
| Real Exam Qty: | 90 |
| Exam Duration: | 120 minutes |
| Related Certifications: | FCCA CISA CPA ACCA CIA |
| Sample Questions: | ISACA AAIA Sample Questions |
| Exam Way: | Online remotely proctored computer-based exam |
| Pre Condition: | Candidates must hold an active CISA certification or another qualified audit-related designation such as CIA, CPA, ACCA, FCCA, Canadian CPA, Australian CPA/FCPA, or Japanese CPA. |
| Official Syllabus URL: | https://www.isaca.org/credentialing/aaia |
近年、IT業種の発展はますます速くなることにつれて、ITを勉強する人は急激に多くなりました。人々は自分が将来何か成績を作るようにずっと努力しています。ISACAのAAIA試験はIT業種に欠くことができない認証ですから、試験に合格することに困っている人々はたくさんいます。ここで皆様に良い方法を教えてあげますよ。ShikenPASSが提供したISACAのAAIAトレーニング資料を利用する方法です。あなたが試験に合格することにヘルプをあげられますから。それにShikenPASSは１００パーセント合格率を保証します。あなたが任意の損失がないようにもし試験に合格しなければShikenPASSは全額で返金できます。
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質問 # 54
Which of the following is the MOST important reason for measuring the AI system ' s performance against predefined metrics in a staging environment?
正解：B
解説：
The AAIA™ Study Guide emphasizes the importance of a rigorous " Staging " or " User Acceptance Testing
" (UAT) phase in the AI lifecycle. Measuring performance against predefined metrics (such as Precision, Recall, or F1-Score) in a non-production environment is critical to " Validate that the system meets business and technical requirements prior to release " . This step prevents the deployment of models that may exhibit bias, inaccuracy, or logic errors in the real world. While privacy compliance (Option A) and dataset freshness (Option B) are vital, they are components of the broader validation process that ensures the model is functional, safe, and fit for purpose before impacting live operations.
質問 # 55
Which of the following is an IS auditor MOST likely to use in order to ensure an AI model has the ability to make correct predictions?
正解：A
解説：
The confusion matrix is a key performance evaluation tool in machine learning and AI auditing.
According to the AAIATM Study Guide, a confusion matrix presents detailed information about actual versus predicted classifications, allowing auditors to assess accuracy, precision, recall, and F1 scores.
"A confusion matrix reveals not just how often predictions are correct, but also the types of errors being made--false positives and false negatives--thereby providing a clear view of the model's predictive reliability." Adversarial testing evaluates robustness, group analysis identifies bias across subgroups, and latency testing examines performance speed--not predictive accuracy. Thus, D is the most relevant for ensuring correct predictions.
質問 # 56
Which of the following is the GREATEST risk associated with normalizing a data set before splitting it into training, testing, and validation sets?
正解：D
解説：
Data normalization involves scaling data (e.g., ensuring all values are between 0 and 1). If you normalize the entire dataset before splitting it, the " Training Set " will be influenced by information from the " Testing Set " (such as the global maximum and minimum values). This is a form of " Data Leakage. " According to the AAIA™ manual, this " indirect knowledge " makes the model ' s test performance appear much better than it actually is, leading to a false sense of security. The correct procedure is to split the data first , then calculate normalization parameters using only the training data and apply those parameters to the test data.
質問 # 57
Which of the following is the GREATEST data quality risk when using an AI tool to assist with audit procedures?
正解：A
解説：
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
質問 # 58
A car rental company is developing an AI system to dynamically adjust rental pricing based on demand, location, and customer profiles. Which of the following is the MOST important reason to conduct specific testing during development?
正解：D
解説：
Dynamic pricing algorithms can unintentionally discriminate against protected groups if trained on biased data or poorly designed features. The AAIA highlights fairness testing as a mandatory requirement in any AI solution that impacts customers financially or socially.
Specific ethical tests are needed to ensure:
* Pricing does not vary unfairly based on demographics
* Sensitive attributes ( ethnicity, age, gender ) are not inferred or misused
* Customer segmentation does not disproportionately disadvantage protected groups
* Historical biases do not propagate into automated pricing
Options A, B, and C are important development tasks but do not address the highest-risk area: preventing discriminatory pricing. Fairness evaluation is a critical AAIA requirement.
References:
AAIA Domain 5: Ethical AI, Fairness, Discrimination Testing
AAIA Domain 1: AI Governance and Impact Assessments
質問 # 59
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AAIA学習範囲: https://www.shikenpass.com/AAIA-shiken.html
2026年ShikenPASSの最新AAIA PDFダンプおよびAAIA試験エンジンの無料共有：https://drive.google.com/open?id=1KFTN7p8SRMhoml4-UIQEznIDmnBvVUye