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| Section | Weight | Objectives |
|---|---|---|
| AI-Assisted Security | 24% | - Security Operations Enhancement
|
| Securing AI Systems | 40% | - Adversarial Defense
|
| AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI and Machine Learning Fundamentals
|
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NEW QUESTION # 155
Which of the following describes the number of training cycles used in an AI model for threat detection?
Answer: A
Explanation:
Basic Concept: Training an AI model involves repeatedly exposing it to training data so it can learn optimal parameters. The terminology for training cycles is fundamental to understanding AI model training processes.
CompTIA SecAI+ Study Guide covers core AI training concepts under basic AI concepts.
Why D is Correct: An epoch refers to one complete pass through the entire training dataset. When a model trains for multiple epochs, it sees each training example multiple times, allowing it to refine its parameters progressively. The number of training epochs is a key hyperparameter that directly affects model performance
- too few and the model underfits; too many and it may overfit. For a threat detection model, specifying epochs controls how thoroughly the model has learned from the training data.
Why A is Wrong: k-means clustering is an unsupervised machine learning algorithm that groups data points into k clusters based on feature similarity. It is a data clustering algorithm, not a term describing training cycles or iterations.
Why B is Wrong: Tokens are the discrete units that LLMs use to process text inputs and outputs. Token count measures text processing volume and LLM utilization, not the number of times a model has processed its training dataset.
Why C is Wrong: Temperature is an inference parameter that controls the randomness or creativity of an LLM ' s output generation. Higher temperature produces more varied outputs; lower temperature produces more deterministic responses. It is not related to training cycles or iterations.
NEW QUESTION # 156
A global security operations center (SOC) wants to adapt and leverage the strength of AI in order to enhance its security operations.
Which of the following is the best way to enhance the global SOC functions?
Answer: C
Explanation:
Basic Concept: AI can augment SOC operations in various ways, but the most appropriate uses maintain human oversight and leverage AI ' s natural language understanding to reduce cognitive load on analysts.
CompTIA SecAI+ Study Guide identifies alert summarization as a high-value, low-risk AI application for SOC enhancement.
Why D is Correct: AI-powered alert summarization consolidates complex, high-volume security alerts into concise, actionable insights, helping analysts rapidly understand threats without reading extensive raw log data. This is a safe, bounded AI application that enhances analyst efficiency while preserving human decision- making authority, directly addressing the volume and complexity challenges SOCs face.
Why A is Wrong: Generating and executing code directly in production without human review introduces serious risk. AI-generated code may contain errors, security vulnerabilities, or unintended side effects that could disrupt or compromise production systems.
Why B is Wrong: Enabling an AI assistant to act autonomously with no human intervention violates the human-in-the-loop principle. Autonomous AI in a SOC without oversight could incorrectly contain legitimate systems, miss actual threats, or make consequential decisions without accountability.
Why C is Wrong: Deploying open-source models directly in production without proper vetting, security hardening, and compliance review introduces supply chain risk, model reliability concerns, and potential intellectual property issues into sensitive security operations.
NEW QUESTION # 157
A short AI-generated video shows a celebrity's likeness talking about a fake public security event.
Which of the following was used to create this video?
Answer: C
Explanation:
Convolutional neural networks (CNNs) are commonly used in generating deepfake videos, where a person's likeness is realistically mapped and animated to create fake but convincing audiovisual content.
NEW QUESTION # 158
A company develops an AI model to diagnose patients. Hospitals access the model through an integrated application programming interface (API). The security team performs a denial-of- service (DoS) attack via brute force on the model. Which of the following controls would have prevented this issue?
Answer: A
Explanation:
Rate limiting restricts the number of API requests within a specific timeframe, preventing brute- force attempts that can overwhelm the AI model and cause denial-of-service conditions.
NEW QUESTION # 159
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: C
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 # 160
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