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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI-Assisted Security | 24% | - Security Operations Enhancement
|
| Topic 2: AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
| 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 # 19
A team of data scientists is ready to release a model for enterprise use. The team wants to protect the model from unintentional changes or tampering. Which of the following is the most appropriate action?
Answer: D
Explanation:
Protecting the model from tampering requires controlled and auditable access. By integrating the model with an API secured by IAM roles, only authenticated and authorized users or systems can interact with it, ensuring integrity and preventing unauthorized changes.
NEW QUESTION # 20
Which of the following describes the most significant risk associated with AI agents that make autonomous decisions?
Answer: B
Explanation:
Output bias is the best answer because an autonomous AI agent can convert biased model judgments into consequential decisions without requiring a human to review each result. CompTIA SecAI+ identifies introduction of bias as a significant AI risk and separately identifies autonomous systems as an important governance concern. Responsible AI principles such as fairness, reliability, accountability, explainability, and human oversight exist partly to control this problem.
If an agent ' s model produces systematically biased recommendations, those biased outputs can directly influence hiring, access decisions, financial transactions, security actions, or other automated processes.
Autonomy therefore increases the impact of output bias because an erroneous or unfair recommendation may become an action before a person can intervene.
Accidental data leakage is also an important AI risk, but the question emphasizes autonomous decision- making , making biased decisions the more directly associated concern. Overfitting is primarily a model- development problem in which a model memorizes training characteristics and fails to generalize effectively.
Increased workload is contrary to one of the primary motivations for using agents, which is automation.
Therefore, output bias represents the most relevant risk among the available choices.
NEW QUESTION # 21
SIMULATION
Instructions
Click the (+) to assign each threat category into its appropriate framework.
If at any time you would like to bring back the initial state of the simulation, please click the Reset All button.
An architect is modeling an agentic system to meet security standards.
Answer:
Explanation:
Explanation:
MAESTRO: Overreliance, Insecure plug-in design
OWASP Top 10: Broken access control, Identification and authentication failures OWASP Top 10 LLM: Prompt injection, Model denial of service STRIDE: Elevation of privilege, Repudiation, Supply chain vulnerabilities, Insecure design MAESTRO addresses AI-specific misuse such as excessive trust in outputs (overreliance) and unsafe plug-in design.
OWASP Top 10 maps to classic web threats: broken access control and authentication failures are key risks.
OWASP Top 10 LLM focuses on LLM-related threats like prompt injection and denial of service targeting the model.
STRIDE categories cover privilege escalation, repudiation (denying actions), supply chain risks, and insecure design flaws.
NEW QUESTION # 22
Which of the following describes the process of adjusting the distribution of underrepresented classes in an AI model data set to improve model performance?
Answer: C
Explanation:
Data balancing is the process specifically intended to correct disproportionate class distributions within a machine learning dataset. CompTIA SecAI+ lists data balancing as a distinct data-processing concept alongside data cleansing, verification, lineage, provenance, and augmentation. When one class substantially outnumbers another, the model may learn to favor the dominant class and perform poorly on underrepresented examples. Balancing techniques can modify this distribution through approaches such as oversampling minority classes, undersampling majority classes, assigning class weights, or generating suitable additional minority examples. The objective is to give the model adequate exposure to each relevant class and improve generalization across the complete problem space. Data augmentation creates additional or modified training examples and can sometimes contribute to balancing, but augmentation is broader and does not specifically mean adjusting class distribution. Data lineage records where data originated and how it changed throughout its lifecycle. Data cleansing removes or corrects inaccurate, duplicate, malformed, or otherwise unsuitable records. Because the question specifically asks about changing the representation of underrepresented classes, data balancing is the precise CompTIA concept being tested.
NEW QUESTION # 23
Which of the following controls is the best way to mitigate a denial-of-service (DoS) attack?
Answer: A
Explanation:
Rate limiting restricts the number of requests within a set timeframe, preventing attackers from overwhelming the system with excessive traffic, making it the best control to mitigate a DoS attack.
NEW QUESTION # 24
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