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  2. CompTIA Certification
  3. CY0-001 Exam
  4. CompTIA.CY0-001.v2026-03-31.q34 Dumps
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Question 6

Which of the following is the best example of an AI model that is trained to identify multiple points from input using a neural network to provide output for authentication?

Correct Answer: A
Facial recognition uses neural networks to analyze multiple points or features from an input image (such as eyes, nose, mouth, and facial structure) to generate a unique identifier for authentication purposes.
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Question 7

An AI security team must assess the probability of an attack on its new system and the impact associated with such an attack. Which of the following threat-modeling resources best addresses the threat landscape for machine learning (ML)?

Correct Answer: B
MITRE ATLAS is specifically designed to capture adversarial tactics, techniques, and procedures (TTPs) targeting machine learning systems. It helps organizations assess both the probability and impact of AI/ML-related attacks, making it the most relevant threat-modeling resource.
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Question 8

A security engineer needs to monitor an AI-based system for runtime operations. The engineer is mostly concerned about the visibility of internal activity. Which of the following is the most appropriate monitoring solution?

Correct Answer: D
For runtime visibility into internal activity of an AI system, the most suitable control is enabling stack calls and debugging-level traces. This provides granular insights into function-level execution, dependencies, and operations, which directly supports monitoring of runtime behavior.
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Question 9

What control reduces the impact radius when a single host is compromised?

Correct Answer: B
Segmentation isolates systems and limits lateral movement.
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Question 10

An employee wants a consulting company to procure a data set that contains age, ethnicity, and diabetes status. During development, the employer wants to ensure the integrity of the data.
Which of the following is the best strategy to accomplish this task?

Correct Answer: A
Checksums ensure data integrity by detecting any unauthorized or accidental changes to the dataset. This provides a reliable way to confirm that sensitive attributes such as age, ethnicity, and diabetes status remain unaltered during development.
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