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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| Topic 2: AI-Assisted Security | 24% | - Operational Use of AI
|
| Topic 3: Securing AI Systems | 40% | - AI System Protection
|
| Topic 4: Basic AI Concepts Related to Cybersecurity | 17% | - Generative AI Concepts
|
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NEW QUESTION # 88
An employee wants a consulting company to procure a data set that contains age, ethnicity, and diabetes status. During development, the employer wants to ensure the integrity of the data.
Which of the following is the best strategy to accomplish this task?
Answer: D
Explanation:
Basic Concept: Data integrity ensures that data has not been tampered with, corrupted, or modified during storage or transmission. For AI training data that is procured from external sources, cryptographic integrity verification is essential to confirm the data arrived unmodified. CompTIA SecAI+ Study Guide covers data integrity controls for AI data pipelines.
Why A is Correct: Implementing checksums provides cryptographic verification of data integrity. A checksum or hash value such as SHA-256 is computed from the dataset at the source. The receiver computes the same hash and compares it to the provided value. Any modification to the data during transit or storage will produce a different hash, immediately detecting tampering or corruption. This is the most reliable, automated, and scalable strategy for ensuring the integrity of procured training data.
Why B is Wrong: Human evaluation can verify data quality and relevance but is impractical for verifying integrity across large datasets of medical records. Human reviewers cannot detect subtle bit-level corruption or intentional small modifications, and the process is not scalable.
Why C is Wrong: Querying the model tests model performance rather than verifying the integrity of the underlying training data. The model cannot tell you whether its training data was modified after collection or during procurement.
Why D is Wrong: Log monitoring tracks system activities and events over time. While useful for auditing access to data, it cannot retroactively confirm that data content has not been modified and does not provide cryptographic integrity guarantees.
NEW QUESTION # 89
Which control BEST prevents attackers from harvesting password hashes during lateral movement?
Answer: A
Explanation:
LSASS-protection solutions prevent hash extraction.
NEW QUESTION # 90
A recently deployed AI system becomes persistently unavailable. A restart temporarily fixes the issue, but the issue happens again. Upon examination of API logs, an analyst finds that external calls continued to use system resources after the action completed.
Which of the following is the best way to improve availability of the system?
Answer: D
Explanation:
Basic Concept: When API sessions or connections remain active and consuming resources after their intended operations have completed, they create resource leaks that progressively degrade system availability. Session lifecycle management is critical for maintaining AI system health. CompTIA SecAI+ Study Guide covers session management as an availability control for AI systems.
Why B is Correct: Enforcing session expiration ensures that external API sessions and connections are automatically terminated after a defined idle period or maximum duration. This prevents resource-consuming zombie sessions from accumulating and exhausting system memory, thread pools, or connection limits. The observed pattern - persistent unavailability that resolves temporarily with restart - is classic resource leak behavior from sessions that never close, making session expiration the direct fix.
Why A is Wrong: Token limits cap the number of tokens processed per request. While useful for controlling per-request resource consumption, they do not address the root cause of sessions persisting and consuming resources long after their operations complete.
Why C is Wrong: Increasing system memory defers the problem rather than solving it. The leak will eventually consume the additional memory too, requiring another restart. Addressing the root cause through session management is superior to scaling resources to accommodate the leak.
Why D is Wrong: MFA adds an additional authentication factor for users accessing the system. It is a security control for identity verification, not a mechanism for managing session lifecycle or preventing resource exhaustion from lingering sessions.
NEW QUESTION # 91
Which of the following improves the observability and auditing of an AI system?
Answer: C
Explanation:
Basic Concept: Observability in AI systems refers to the ability to monitor, log, trace, and audit the behavior of AI models in production. MLOps is the operational discipline that establishes the processes, tooling, and practices for managing AI systems throughout their lifecycle. CompTIA SecAI+ Study Guide covers MLOps as a key mechanism for AI system transparency and auditability.
Why C is Correct: MLOps implements comprehensive monitoring, logging, versioning, and audit pipelines for AI systems. It provides observability through model performance tracking, data drift detection, prediction logging, lineage tracking, and audit trails. MLOps platforms enable organizations to understand what their AI models are doing, why they are making certain decisions, and how their behavior changes over time, directly improving observability and auditing.
Why A is Wrong: Redeploying a model is an operational action taken to restore a previous version or apply updates. It does not improve monitoring infrastructure, logging capabilities, or auditing frameworks for ongoing observability.
Why B is Wrong: Manual detection relies on human observation to identify issues. It is labor-intensive, inconsistent, and not scalable for AI systems processing high volumes of data. It does not provide systematic observability or comprehensive audit trails.
Why D is Wrong: Anomaly detection identifies unusual patterns in data or behavior. While useful as a monitoring component within an observability strategy, it is a single technique and does not encompass the full observability and auditing capabilities provided by a comprehensive MLOps implementation.
NEW QUESTION # 92
Which of the following International Organization for Standardization (ISO) standards should be selected for certification to use for third-party assurance for responsible AI practices?
Answer: D
Explanation:
Basic Concept: ISO develops international standards for management systems across various domains. For organizations seeking third-party certification demonstrating commitment to responsible AI governance practices, the appropriate ISO standard must specifically address AI management systems. CompTIA SecAI+ Exam Objectives cover ISO standards relevant to AI governance under Domain 4.
Why D is Correct: ISO 42001 is the International Standard for Artificial Intelligence Management Systems (AIMS). It provides a framework for establishing, implementing, maintaining, and continually improving an AI management system within organizations. ISO 42001 certification provides third-party assurance specifically for responsible AI practices including risk management, transparency, human oversight, and ethical AI governance - directly answering the question.
Why A is Wrong: ISO 20000 is the standard for IT Service Management (ITSM). It provides requirements for establishing and maintaining a service management system for IT services. It does not address AI governance or responsible AI practices.
Why B is Wrong: ISO 27001 is the standard for Information Security Management Systems (ISMS). It addresses general information security risk management, not AI-specific governance or responsible AI practices such as fairness, transparency, and AI lifecycle management.
Why C is Wrong: ISO 27701 extends ISO 27001 to address Privacy Information Management (PIMS), covering personal data protection requirements aligned with GDPR. While relevant to data privacy in AI systems, it does not specifically certify responsible AI governance practices.
NEW QUESTION # 93
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