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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Securing AI Systems | 40% | - AI System Protection
|
| Topic 2: Basic AI Concepts Related to Cybersecurity | 17% | - Generative AI Concepts
|
| Topic 3: AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
| Topic 4: AI-Assisted Security | 24% | - Operational Use of AI
|
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NEW QUESTION # 41
A malware sample alters itself slightly each time it runs to evade signature detection. What technique is this?
Answer: C
Explanation:
Polymorphic malware changes its code structure while maintaining functionality.
NEW QUESTION # 42
During a model validation procedure, an engineer notices that a model performs well during training but poorly during testing.
Which of the following best describes the reason?
Answer: D
Explanation:
Basic Concept: The gap between training performance and test performance is a classic indicator of a specific model quality problem. Understanding this phenomenon and its causes is fundamental to AI model development. CompTIA SecAI+ Study Guide covers overfitting under basic AI concepts and model quality.
Why B is Correct: Overfitting occurs when a model learns the training data too specifically - memorizing noise, outliers, and specific patterns in the training set rather than learning generalizable underlying patterns.
The model achieves high accuracy on training data but fails to generalize to new, unseen test data. This produces exactly the scenario described: excellent training performance combined with poor test performance.
Overfitting is the quintessential cause of this training-testing performance gap.
Why A is Wrong: Fine-tuning is a training technique that adapts a pre-trained model to a new task or domain using additional training data. It is a deliberate training process, not a description of why a model ' s performance degrades from training to testing.
Why C is Wrong: Regularization is a training technique specifically used to prevent overfitting by adding penalties to large model weights, encouraging the model to learn simpler, more generalizable patterns. It is the solution to overfitting, not its cause.
Why D is Wrong: Inference is the process of using a trained model to make predictions on new data. It describes the operational use of a model, not a quality characteristic that explains why performance differs between training and testing phases.
NEW QUESTION # 43
Which of the following helps in managing potential security issues related to model training?
Answer: B
Explanation:
Basic Concept: Managing security risks in AI model training requires a comprehensive framework specifically designed for AI risk identification, assessment, and mitigation across the entire AI lifecycle including data collection, training, and deployment. CompTIA SecAI+ Study Guide identifies NIST AI RMF as the primary resource for AI-specific risk management.
Why A is Correct: The NIST AI Risk Management Framework is purpose-built for managing risks throughout the AI lifecycle. It provides structured guidance for identifying, assessing, and mitigating risks specific to AI systems including training data quality, model bias, data poisoning, and training pipeline vulnerabilities. Its AI-specific scope makes it the most appropriate framework for managing model training security issues.
Why B is Wrong: ISO 27001 is an information security management system standard focused on general IT security controls and risk management. It does not specifically address AI model training risks, data pipeline integrity, or ML-specific vulnerabilities.
Why C is Wrong: The OECD provides high-level AI governance principles and policy recommendations at an international level. It offers ethical and policy guidance but does not provide operational risk management guidance for securing AI model training processes.
Why D is Wrong: GDPR is a European data protection regulation focused on personal data privacy, consent, and individual rights. While relevant to training data governance, it does not address the technical security risks of model training pipelines or ML system vulnerabilities.
NEW QUESTION # 44
An internal user enters a client credit card number into an internal generative machine learning (ML) model:
#User prompt: Customer Jane Doe has a new credit card that she wants to add to her account. The number is 5555-5555-5555-5555 Which of the following is the most effective way to prevent prompt injection attacks against a large language model (LLM)?
Answer: D
Explanation:
Guardrails are the primary security control for LLMs to prevent prompt injection attacks. They enforce rules on what inputs are accepted and how the model responds, blocking malicious or sensitive prompts (such as credit card numbers) before they can manipulate or exploit the model.
NEW QUESTION # 45
A security operations center (SOC) has a very high volume of logs and alerts. The manager proposes the implementation of a machine learning (ML) system to help with triage.
Which of the following tasks is most suitable?
Answer: D
Explanation:
Basic Concept: ML models excel at classification tasks, learning to assign incoming data points to predefined categories based on patterns in training data. In a SOC context, alert classification is the highest-value triage function ML can perform. CompTIA SecAI+ Exam Objectives address AI-assisted security operations under Domain 3.
Why C is Correct: ML-based alert classification automatically analyzes characteristics of each alert and assigns it to a severity category such as critical, high, medium, or low, or to a threat type such as malware or intrusion attempt. This dramatically reduces analyst workload and speeds triage by prioritizing which alerts demand immediate human attention, directly solving the high-volume problem.
Why A is Wrong: Applying filters on specific alerts is a rule-based operation achievable without ML using simple log management tools. It requires no learning capability and does not adapt to new or evolving threats.
Why B is Wrong: Automatically patching systems is a remediation action requiring validated, controlled processes. Having an ML system autonomously patch production systems without human oversight poses unacceptable operational and security risk.
Why D is Wrong: Summarizing alert content is a useful generative AI function but does not provide prioritization value for triage. Classification tells analysts what to act on first; summarization only rephrases existing information.
NEW QUESTION # 46
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