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| Section | Weight | Objectives |
|---|---|---|
| Basic AI Concepts Related to Cybersecurity | 17% | - Generative AI Concepts
|
| Securing AI Systems | 40% | - AI System Protection
|
| AI-Assisted Security | 24% | - Operational Use of AI
|
| AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
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NEW QUESTION # 160
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: D
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 # 161
An analyst wants to develop a solution to review security information and event management (SIEM) logs for endpoint analytics. Which of the following is a benefit of a small language model (SLM) compared to a large language model (LLM) when cost efficiency is a consideration?
Answer: C
Explanation:
Domain-specific specialization is a major advantage of using a small language model when cost efficiency matters. CompTIA SecAI+ requires candidates to distinguish both LLMs and SLMs within natural language processing concepts. An SLM can be trained or fine-tuned for a narrowly defined task such as interpreting SIEM events, endpoint terminology, security alerts, and incident patterns. Because it does not require the enormous parameter count and broad general-purpose knowledge of a large model, an SLM can provide efficient task-specific inference while requiring less memory and computational capacity. This can reduce infrastructure and operational costs substantially. Broad dataset training is characteristic of general-purpose foundation models and tends to increase rather than reduce training and compute requirements. Data aggregation is a preprocessing or analytics function and is not an inherent SLM advantage. Data privacy can improve when smaller models are deployed locally, but local deployment is an architectural choice rather than the defining cost-related benefit represented by the available choices. A model concentrated on domain- specific cybersecurity information can therefore deliver the required SIEM-analysis capability more economically than a general-purpose LLM.
NEW QUESTION # 162
A security alert triggers an agentic system. An analyst notices the following payload in the logs"
The alert includes multiple shell commands that are not typically run as part of any hardening.
Which of the following is the most effective control to implement?
Answer: B
Explanation:
The payload in the alert attempts to trick the system into executing unauthorized shell commands.
The most effective control is to implement allow-list validation (approved strings) before execution. This ensures that only predefined, safe commands are executed, blocking prompt injection attempts that introduce malicious code such as the fake patch script.
NEW QUESTION # 163
A developer is selecting authentication controls for an AI system.
Which of the following is the best way to prevent threat actor replay attacks?
Answer: D
Explanation:
Basic Concept: A replay attack occurs when an attacker captures a valid authentication token or credential and reuses it to impersonate a legitimate user. Preventing replay attacks requires ensuring that captured credentials cannot be successfully reused after a defined period or after their intended single use. CompTIA SecAI+ Study Guide covers replay attack prevention under AI system authentication.
Why C is Correct: Expiring session tokens have a limited validity window, typically a few minutes to hours.
If an attacker captures a token, they can only use it until it expires. Short expiration times dramatically reduce the window of opportunity for replay attacks. This is the most direct and effective control specifically targeting replay attack prevention, as expired tokens are rejected even if intercepted.
Why A is Wrong: IdP federation enables single sign-on across multiple systems using federated identity providers. While it standardizes authentication, it does not inherently prevent replay attacks on captured tokens unless combined with short token expiration and proper validation.
Why B is Wrong: SSH certificate authentication uses cryptographic certificates for strong authentication.
While more secure than password-based SSH, certificates alone do not prevent replay attacks unless they include timestamps, nonces, or other anti-replay mechanisms that invalidate captured credentials.
Why D is Wrong: IAM access keys are long-lived credentials that provide programmatic access to services.
They are typically static and do not expire automatically, making them vulnerable to replay attacks if intercepted. They are less suitable for replay attack prevention than expiring session tokens.
NEW QUESTION # 164
A disgruntled employee changed the company policies that a chatbot references in order to create confusion and disrupt the business.
Which of the following AI-generated vulnerabilities is the employee exploiting?
Answer: B
Explanation:
Basic Concept: AI systems that rely on knowledge bases, vector databases, or reference documents are vulnerable to attacks that corrupt or manipulate that source data. When an adversary deliberately modifies the data an AI uses, this is a form of data poisoning. CompTIA SecAI+ Study Guide covers data poisoning as a core AI vulnerability.
Why C is Correct: Data poisoning is an attack where an adversary intentionally corrupts or manipulates the data that an AI system uses for training, inference, or reference. In this scenario, the employee modified the company policies document that the chatbot uses as its knowledge base, causing the chatbot to provide incorrect, misleading, or confusing information to users. This is a classic indirect data poisoning attack targeting the AI ' s reference data rather than its model weights.
Why A is Wrong: Data reduction refers to techniques that decrease the volume or dimensionality of data for processing efficiency. It is a data engineering concept, not an attack vector or vulnerability classification.
Why B is Wrong: Data masking replaces sensitive data values with anonymized equivalents to protect privacy. It is a data protection control used legitimately, not an attack that an employee would exploit to cause disruption.
Why D is Wrong: Data leaking involves unauthorized disclosure of sensitive information from an AI system or its associated data stores. The employee ' s action of manipulating data is an integrity attack, not a confidentiality violation involving leakage of data to unauthorized parties.
NEW QUESTION # 165
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