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CompTIA SecAI+ Certification Exam Sample Questions (Q67-Q72):

NEW QUESTION # 67
Which tool prevents unauthorized system file modifications?

  • A. NIDS
  • B. Containerization
  • C. File Integrity Monitoring (FIM)
  • D. WAF

Answer: C

Explanation:
FIM detects changes to critical system files.


NEW QUESTION # 68
An organization recently developed an AI-powered product and discovers that it is vulnerable to attacks in which malicious actors can alter the input, causing the system to recommend inappropriate information. Which of the following techniques is the most effective way to secure the system against manipulation attacks?

  • A. Feature scaling
  • B. Feature regularization
  • C. Guardrails
  • D. Cross-validation

Answer: C

Explanation:
Guardrails restrict and control how an AI model processes and responds to inputs, making them the most effective defense against manipulation attacks such as prompt injection or malicious input alteration.


NEW QUESTION # 69
Which of the following explains the reason a cybersecurity analyst prefers a machine learning (ML) model over a statistical model for attack classification?

  • A. The ability to learn complex problems and adapt to new information
  • B. A simplified development pipeline and deployment process
  • C. Improved performance with a small data set and high durability
  • D. Large community support and availability of global experts

Answer: A

Explanation:
Basic Concept: Cybersecurity threats evolve continuously, with new attack variants emerging regularly. The choice between traditional statistical models and ML models for attack classification depends on which better handles the complexity and dynamism of the threat landscape. CompTIA SecAI+ covers ML model advantages for cybersecurity under basic AI concepts.
Why A is Correct: ML models can learn arbitrarily complex, non-linear relationships from training data and adapt to new patterns when retrained with updated data. For attack classification, this means ML can recognize sophisticated, multi-feature attack patterns that exceed the capabilities of simple statistical models and can be updated to detect new attack variants as the threat landscape evolves. This adaptability to complex and changing problems is the primary reason analysts prefer ML over static statistical approaches.
Why B is Wrong: ML model development pipelines are generally more complex than statistical models, requiring data preparation, feature engineering, model selection, training, validation, and deployment steps.
Simplicity of development is not a characteristic advantage of ML over statistical models.
Why C is Wrong: ML models typically require large amounts of training data to perform well. Statistical models often perform better than ML with small datasets. Performance with small datasets is actually an advantage of statistical models over ML, not ML over statistical.
Why D is Wrong: Community support and expert availability are ecosystem considerations rather than technical reasons to prefer ML for cybersecurity classification tasks. These factors might influence tool selection but do not explain the fundamental technical preference for ML ' s superior handling of complex attack patterns.


NEW QUESTION # 70
Which of the following technologies is used in deepfake?

  • A. Multi-shot prompting
  • B. Prompt engineering
  • C. Generative adversarial network (GAN)
  • D. Transfer learning

Answer: C

Explanation:
Deepfakes are primarily created using GANs, where two neural networks (a generator and a discriminator) compete to produce highly realistic synthetic media, such as manipulated videos or images.


NEW QUESTION # 71
Which of the following attacks is most enabled by AI-generated content?

  • A. Ransomware
  • B. Phishing
  • C. Model poisoning
  • D. Remote code execution

Answer: B

Explanation:
Basic Concept: AI-generated content including personalized text, synthetic voice, and deepfake video has dramatically enhanced the effectiveness and scalability of social engineering attacks. Understanding how AI amplifies specific attack types is key to CompTIA SecAI+ basic AI concepts in the cybersecurity context.
Why B is Correct: Phishing attacks are most dramatically enabled by AI-generated content. AI can generate highly personalized, grammatically perfect phishing emails tailored to individual targets using publicly available information. It can create convincing deepfake audio and video for voice phishing (vishing) and video phishing, replicate executive communication styles for business email compromise, and generate phishing campaigns at massive scale. The quality and personalization that previously required skilled human social engineers can now be automated with AI.
Why A is Wrong: Model poisoning is a specific attack against AI systems that corrupts training data to manipulate model behavior. While sophisticated, it is a targeted AI security attack rather than a broad cybercrime enabled by AI-generated content at scale.
Why C is Wrong: Ransomware is malware that encrypts victim data and demands payment for decryption keys. While AI can assist in ransomware development, ransomware deployment relies on code execution and network propagation techniques more than AI-generated content.
Why D is Wrong: Remote code execution involves exploiting vulnerabilities to run arbitrary code on a target system. It relies on technical vulnerability exploitation rather than AI-generated content. AI might assist in finding vulnerabilities, but RCE is not primarily enabled by content generation.


NEW QUESTION # 72
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