ISACA AAISM Guaranteed Success, AAISM Reliable Practice Questions

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ISACA AAISM Exam Syllabus Topics:

Topic	Details
Topic 1	AI Risk Management: This section of the exam measures the skills of AI Risk Managers and covers assessing enterprise threats, vulnerabilities, and supply chain risk associated with AI adoption, including risk treatment plans and vendor oversight.
Topic 2	AI Governance and Program Management: This section of the exam measures the abilities of AI Security Governance Professionals and focuses on advising stakeholders in implementing AI security through governance frameworks, policy creation, data lifecycle management, program development, and incident response protocols.
Topic 3	AI Technologies and Controls: This section of the exam measures the expertise of AI Security Architects and assesses knowledge in designing secure AI architecture and controls. It addresses privacy, ethical, and trust concerns, data management controls, monitoring mechanisms, and security control implementation tailored to AI systems.

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AAISM Reliable Practice Questions, Pdf AAISM Exam Dump

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q229-Q234):

NEW QUESTION # 229
Which of the following would MOST effectively obtain ongoing support from stakeholders to align AI initiatives with business objectives?

A. Addressing and optimizing AI-related risk
B. Developing and monitoring the AI strategic roadmap
C. Quantifying and communicating the value of AI solutions
D. Conducting periodic organization-wide AI staff training

Answer: C

Explanation:
Sustained stakeholder sponsorship hinges on demonstrated, quantified business value communicated in terms they own (KPIs, ROI, cost-to-serve, risk-adjusted outcomes). AAISM frames stakeholder alignment as a value-assurance loop: define value hypotheses, measure realized value, and continuously communicate results to sponsors. While an AI roadmap (C), risk optimization (B), and training (A) are important, they support rather than drive ongoing executive buy-in. Quantified value narratives secure resources and reinforce alignment to strategic goals.
References:* AI Security Management (AAISM) Body of Knowledge: Strategy & Value Realization- value metrics, benefits tracking, stakeholder reporting* AAISM Study Guide: Business alignment for AI- OKRs/KPIs, ROI cases, benefits realization management

NEW QUESTION # 230
Secure aggregation enhances the security of federated learning systems by:

A. Encrypting individual model updates during transmission to ensure only the server can access the data
B. Processing client updates in isolation to reduce the risk of exposing sensitive information
C. Applying differential privacy techniques to mask sensitive information in training data
D. Ensuring individual client contributions remain confidential even if the server is compromised

Answer: D

Explanation:
Secure aggregation cryptographically aggregates client updates so that the server learns only the sum
/aggregate, not any single client's update. Properly implemented, the server cannot recover individual contributions-even if compromised-thereby preserving client confidentiality. Option C (encryption in transit) is insufficient because decryption at the server reveals updates; Option A is procedural, not cryptographic; Option B (differential privacy) is a separate technique and not the defining property of secure aggregation.
References: AAISM Body of Knowledge: Privacy-Preserving ML-Federated Learning and Secure Aggregation; AAISM Study Guide: Threat Models for Aggregation Servers and Confidentiality Guarantees.

NEW QUESTION # 231
Which of the following AI data management techniques involves creating validation and test data?

A. Annotating
B. Splitting
C. Learning
D. Training

Answer: B

Explanation:
Data splitting partitions a labeled dataset into training, validation, and test subsets to enable unbiased model tuning and evaluation. Training (A) consumes the training split; annotating (B) adds labels; learning (D) is a general term for model optimization, not a data management step.
References: AI Security Management™ (AAISM) Body of Knowledge - Data Lifecycle Controls; Dataset Partitioning for Validation and Testing. AAISM Study Guide - Train/Validation/Test Splits and Evaluation Integrity.

NEW QUESTION # 232
Which of the following strategies is the MOST effective way to protect against AI data poisoning?

A. Ensuring the model is trained on diverse data sources
B. Using robust data validation techniques and anomaly detection
C. Increasing model complexity to better handle data variations
D. Incorporating more features and data into model training

Answer: B

Explanation:
AAISM directs organizations to prevent training-time attacks by hard-gating data ingestion with provenance checks, schema and label validation, sanitization, and anomaly/outlier detection prior to model training. These controls most directly block poisoned records from entering the pipeline and are prioritized over architectural complexity or sheer data volume. Diversity of sources can improve representativeness but does not reliably stop adversarial contamination.
References: AI Security Management™ (AAISM) Body of Knowledge - Adversarial ML: Training-Time Threats; Secure Data Ingestion & Validation Controls; AI Risk Treatment and Assurance. AAISM Study Guide - Poisoning Prevention Gates; Provenance, Quality, and Anomaly Screening in ML Pipelines.

NEW QUESTION # 233
Which of the following is MOST important to consider when validating a third-party AI tool?

A. Terms and conditions
B. Industry analysis and certifications
C. Right to audit
D. Roundtable testing

Answer: C

Explanation:
The AAISM framework specifies that when adopting third-party AI tools, the right to audit is the most critical contractual and governance safeguard. This ensures that the organization can independently verify compliance with security, privacy, and ethical requirements throughout the lifecycle of the tool. Terms and conditions provide general usage guidance but often limit liability rather than ensuring transparency. Industry certifications may indicate good practice but do not substitute for direct verification. Roundtable testing is useful for evaluation but lacks enforceability. Only the contractual right to audit provides formal assurance that the tool operates in accordance with organizational policies and external regulations.
References:
AAISM Exam Content Outline - AI Governance and Program Management (Third-Party Governance) AI Security Management Study Guide - Vendor Oversight and Audit Rights

NEW QUESTION # 234
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ISACA AAISM Guaranteed Success, AAISM Reliable Practice Questions

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ISACA AAISM Guaranteed Success, AAISM Reliable Practice Questions

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AAISM Reliable Practice Questions, Pdf AAISM Exam Dump

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q229-Q234):

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