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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
| Topic 2: Basic AI Concepts Related to Cybersecurity | 17% | - Generative AI Concepts
|
| Topic 3: Securing AI Systems | 40% | - AI System Protection
|
| Topic 4: AI-Assisted Security | 24% | - Operational Use of AI
|
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NEW QUESTION # 117
Which techniques belong to the MITRE ATT&CK Command-and-Control phase? (Choose two.)
Answer: A,E
Explanation:
Beaconing and covert channels maintain communication with the attacker.
NEW QUESTION # 118
Which of the following job roles in an organizational governance structure develops a model from business use cases?
Answer: A
Explanation:
Basic Concept: In AI governance, each role holds distinct responsibilities. Understanding these roles is core to CompTIA SecAI+ Domain 4 (AI Governance, Risk, and Compliance).
Why D is Correct: The Data Scientist is responsible for translating business use cases into working AI/ML models. They analyze business requirements, identify the appropriate machine learning approach, and develop models that fulfill specific business objectives. According to the CompTIA SecAI+ Study Guide, data scientists bridge raw data and actionable AI solutions by building and validating models derived from business-driven needs.
Why A is Wrong: A Platform Architect designs and manages the infrastructure and technical platforms hosting AI systems. Their focus is architectural design of the environment, not model development from business use cases.
Why B is Wrong: An AI Risk Analyst identifies, evaluates, and mitigates risks associated with AI adoption.
Their role is governance and risk-oriented, not model creation.
Why C is Wrong: An MLOps Engineer operationalizes, deploys, monitors, and maintains AI models in production. They take models already built by data scientists and ensure reliable operation at scale, not develop them from business use cases.
NEW QUESTION # 119
A cybersecurity analyst wants to choose a machine learning (ML) model to classify log entries while providing the best explainability.
Which of the following models should the analyst use?
Answer: D
Explanation:
Basic Concept: Different ML model architectures offer varying degrees of explainability. In cybersecurity, understanding why a model classified a log entry as malicious or benign is critical for analyst trust, investigation, and regulatory compliance. CompTIA SecAI+ covers model explainability under responsible AI and basic AI concepts.
Why C is Correct: Decision trees are inherently interpretable models that classify data through a series of transparent if-then rules. Every classification decision can be traced through the exact path of conditions that led to it, showing precisely which log entry features triggered the classification. Analysts can read and understand the decision path, making decision trees the gold standard for explainable ML classification in security applications where understanding the reason for a classification is as important as the classification itself.
Why A is Wrong: Large language models are complex transformer architectures with hundreds of billions of parameters. They function as black boxes - their internal decision-making processes are not human- interpretable, making them poor choices when explainability is the primary requirement.
Why B is Wrong: Neural networks are non-linear black box models. While they can achieve high classification accuracy, their multi-layer architecture makes it extremely difficult to explain why specific decisions were made in human-understandable terms.
Why D is Wrong: Generative adversarial networks are designed for generating synthetic data, not for classification tasks. They consist of competing generator and discriminator networks and are fundamentally unsuitable for log entry classification with explainability requirements.
NEW QUESTION # 120
A company uses human review for software development validation and wants to add another validation layer. Which of the following should a security administrator use to accomplish task?
Answer: A
Explanation:
AI-assisted approval adds an automated validation layer alongside human review, helping detect potential security or quality issues in software development before deployment. This strengthens the overall validation process.
NEW QUESTION # 121
Users report that the output of a generative AI application seems unrelated to the prompts and contains offensive content. A security team investigates and determines that there was an on-path attack.
Which of the following is the most likely attack method?
Answer: D
Explanation:
Basic Concept: An on-path (formerly man-in-the-middle) attack intercepts communication between two parties, allowing the attacker to read, modify, or inject content. In the context of a generative AI application, an on-path attack on the session between user and AI service can manipulate prompts being sent to the model or responses being returned to users. CompTIA SecAI+ covers AI-specific attack vectors under securing AI systems.
Why B is Correct: Session hijacking involves an attacker taking control of an active user session by capturing or forging session tokens. In this attack, the attacker intercepts the communication channel between users and the AI application, allowing them to modify prompts sent to the model or replace legitimate model responses with offensive content. This explains why outputs seem unrelated to prompts and contain offensive material.
Why A is Wrong: Application server hijacking involves gaining unauthorized control of the server hosting the application. While severe, this would typically manifest as complete service disruption or data exfiltration rather than targeted modification of individual user session content.
Why C is Wrong: Domain hijacking involves unauthorized transfer of a domain name registration, redirecting all users to a different IP address. This would affect all users simultaneously and typically redirect to a completely different site rather than manipulating individual AI responses.
Why D is Wrong: Model hijacking refers to attacks that steal or replicate an AI model, not to intercepting and modifying the communication between users and an existing model during active sessions.
NEW QUESTION # 122
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