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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Security Management (AAISM) Exam |
| Exam Number: | AAISM |
| Exam Price: | USD $399 |
| Passing Score: | 450/800 |
| Exam Duration: | 150 minutes |
| Available Languages: | English, Spanish |
| Certificate Validity Period: | 3 years |
| Exam Format: | Scenario-based Questions, Multiple Choice |
| Related Certifications: | ISACA Advanced in AI Security Management (AAISM) |
| Real Exam Qty: | 90 |
| Sample Questions: | ISACA AAISM Sample Questions |
| Exam Way: | Online remote proctored exam or test center exam |
| Pre Condition: | Candidates must hold an active CISM or CISSP certification. |
| Official Syllabus URL: | https://www.isaca.org/credentialing/aaism/aaism-exam-content-outline |
このほど、卒業生であれば、社会人であれば、ずっと「就職難」問題が存在し、毎年、「就職氷河期」といった言葉が聞こえてくる。ブームになるIT技術業界でも、多くの人はこういう悩みがあるんですから、ISACAのAAISMの能力を把握できるのは欠かさせないない技能であると考えられます。もし我々社のPass4TestのAAISM問題集を手に入れて、速くこの能力をゲットできます。それで、「就職難」の場合には、他の人々と比べて、あなたはずっと優位に立つことができます。
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質問 # 310
Implementing which of the following would MOST effectively address bias in generative AI models?
正解：D
解説：
AAISM identifies fairness constraints (e.g., constrained optimization, debiasing objectives, conditional generation controls, and post-processing calibrations) as the most direct, measurable method to mitigate disparate outcomes in generative systems. While data augmentation can help with coverage, and adversarial training improves robustness, fairness constraints explicitly target distributional fairness and outcome equity in generated content, aligning with governance and compliance goals.
References: AI Security Management™ (AAISM) Body of Knowledge - Fairness & Bias Management in Generative AI; Metrics, Constraints, and Remediation. AAISM Study Guide - Fairness Objectives, Post-hoc Debiasing, and Evaluation Protocols.
質問 # 311
Which of the following is the BEST control to mitigate injection attacks on an AI agent?
正解：C
解説：
Injection attacks against AI agents commonly rely on malicious or manipulated prompts designed to alter system behavior or bypass safeguards. Implementing input filtering directly addresses this threat by detecting and blocking harmful inputs before they are processed by the AI system.
質問 # 312
An AI research team is developing a natural language processing model that relies on several open-source libraries. Which of the following is the team's BEST course of action to ensure the integrity of the software packages used?
正解：A
解説：
AAISM's technical control guidance emphasizes that when using open-source libraries, the best safeguard for integrity is to scan the packages for malware before installation. This ensures that compromised or malicious code does not enter the AI system environment. Maintaining lists aids consistency but not security. Always using the latest versions may introduce unverified vulnerabilities. Retraining models addresses functionality but not software integrity. Therefore, the strongest protective measure is pre-installation malware scanning of open-source packages.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Software Supply Chain Security) AI Security Management Study Guide - Open-Source Package Risk Mitigation
質問 # 313
Which of the following should be done FIRST when developing an acceptable use policy for generative AI?
正解：C
解説：
According to the AAISM framework, the first step in drafting an acceptable use policy is defining the scope and intended use of the AI system. This ensures that governance, regulatory considerations, risk assessments, and alignment with organizational policies are all tailored to the specific applications and functions the AI will serve. Once scope and intended use are clearly defined, legal, regulatory, and risk considerations can be systematically applied. Without this step, policies risk being generic and misaligned with business objectives.
質問 # 314
When deriving statistical information generated by AI systems, which of the following types of risk is MOST important to address?
正解：D
解説：
The most critical risk when deriving statistical insights from AI-generated data is systemic bias in data.
According to the AI Security Management (AAISM) framework, systemic bias directly undermines the fairness, reliability, and validity of analytical results derived from AI systems. If the input data or learned model patterns are biased-reflecting skewed representation, sampling imbalance, or embedded prejudice- the statistical outputs will propagate and amplify these biases, leading to misinformed decisions and compliance violations.
Why Option A is Correct:
* Systemic bias affects the integrity and trustworthiness of AI-generated statistical information.
* It can introduce discriminatory outcomes, ethical breaches, and regulatory non-compliance-key concerns in AAISM's AI Risk Management and Governance principles.
* Mitigating systemic bias requires data quality assessments, fairness audits, bias detection tools, and model interpretability measures to ensure the derived insights are accurate and ethically sound.
Why Other Options Are Incorrect:
* Option B: Incomplete outputs can affect accuracy but are typically handled through process monitoring or retraining, not as a primary risk factor in statistical validity.
* Option C: Lack of data normalization is a technical preprocessing issue, not a governance-level risk impacting statistical trustworthiness.
* Option D: Hallucinations occur mainly in generative models (e.g., LLMs) and affect content generation, not statistical computation pipelines.
Exact Extract from Official AAISM Study Guide:
"Systemic bias in AI training and inference data represents the most material statistical risk. Bias propagates through derived metrics, predictive models, and decision outputs, compromising fairness, accuracy, and compliance. AI Security Management requires implementing bias detection, fairness testing, and governance mechanisms to identify and mitigate such systemic bias before using AI-generated analytics for organizational or regulatory reporting." References:
AI Security Management (AAISM) Body of Knowledge: AI Risk Identification and Evaluation, Bias and Fairness Management in AI Systems.
AI Security Management Study Guide: Systemic Bias Mitigation Techniques, Fairness Assurance in AI Analytics.
ISO/IEC 23894:2023 - Clause 7.2: Bias identification and treatment within AI risk frameworks.
質問 # 315
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