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>> AAISM Practice Exam Questions <<
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NEW QUESTION # 393
An AI application development team has been given access to user information and now must format it to be readable by the AI model. During which phase of the data life cycle would this MOST likely occur?
Answer: B
Explanation:
In the AI data lifecycle, converting raw user information into model-readable form (e.g., cleaning, parsing, feature engineering, encoding, normalization/standardization, and schema alignment) is performed during data preparation. This phase establishes input quality and structure prior to training and inference and may include normalization as one of several preparation steps. Data collection acquires data, minimization limits data to what's necessary, and normalization is a specific technique-not the overarching phase.
References: AI Security Management (AAISM) Body of Knowledge: Data Lifecycle-Preparation and Ingestion Controls; AAISM Study Guide: Data Preparation Activities (cleaning, labeling, feature engineering, encoding) and Quality Gates.
NEW QUESTION # 394
Which of the following is the MOST effective defense against cyberattacks that alter input data to avoid detection by the model?
Answer: D
Explanation:
Evasion attacks manipulate inputs to induce misclassification while leaving the model unchanged.
AAISM prescribes adversarial robustness controls, with adversarial training as a primary measure:
incorporate adversarially perturbed examples into training/validation to harden decision boundaries and improve resilience across threat models (e.g., Lp-bounded perturbations).
NEW QUESTION # 395
Which of the following should be the MOST important consideration when conducting an AI impact assessment?
Answer: A
Explanation:
An AI impact assessment prioritizes alignment with stated business objectives and intended purpose of the AI use case. Establishing whether the AI system measurably supports defined objectives (value, outcomes, success criteria) is the primary lens before assessing secondary concerns (workforce effects, awareness initiatives, reputational considerations). Governance requires confirming purpose limitation, success metrics, and risk-benefit trade-offs tied to organizational objectives; only then are downstream impacts evaluated.
References: AI Security Management (AAISM) Body of Knowledge - AI Governance & Impact Assessments; Purpose and Objective Definition; Risk-Benefit Evaluation. AAISM Study Guide - Business Alignment, Intended Use, and Success Criteria in AI Impact Assessments.
NEW QUESTION # 396
An organization is deploying an automated AI cybersecurity system. Which strategy MOST effectively minimizes human error and improves security?
Answer: A
Explanation:
AAISM states that the effectiveness of automated AI cybersecurity systems depends heavily on well-trained detection models using high-quality historical attack data.
Historical data improves:
* detection accuracy
* reduction of false positives
* reduction of human misinterpretation
Manual monitoring (A) increases human error. ML "ensuring responsibility" (C) is not a defined control. Pen testing (D) does not reduce human mistakes.
References: AAISM Study Guide - AI in Cybersecurity; Model Training for Threat Detection.
NEW QUESTION # 397
Which of the following is the BEST control for preventing deepfakes?
Answer: D
Explanation:
Output provenance verification (e.g., robust watermarking, cryptographic signing, content credentials, and chain-of-custody attestations) is the primary preventive and detective control to combat deepfakes at scale. It enables receivers and downstream systems to verify that media originates from trusted sources and has not been tampered with. While risk assessments (Option B) and governance policies (Option C) set expectations, they do not technically prevent forged media. Input validation (Option D) does not address media authenticity once generated or received.
References:
AAISM Body of Knowledge: Content Authenticity, Watermarking, and Provenance; Trustworthy AI Outputs and Media Integrity Controls.
AAISM Study Guide: Mitigations for Synthetic Media Risks; Watermark/Signature Verification Pipelines; Content Credentials in Enterprise Controls.
NEW QUESTION # 398
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