CS0-002

CS0-002 practice questions, page 8 of 17, with answer review and explanations.

Question 36

A software development team asked a security analyst to review some code for security vulnerabilities. Which of the following would BEST assist the security analyst while performing this task?

  1. Static analysis
  2. Dynamic analysis
  3. Regression testing
  4. User acceptance testing

Question 37

A security analyst inspects the header of an email that is presumed to be malicious and sees the following: Which of the following is inconsistent with the rest of the header and should be treated as suspicious?

  1. The use of a TLS cipher
  2. The sender's email address
  3. The destination email server
  4. The subject line

Question 38

A team of network security analysts is examining network traffic to determine if sensitive data was exfiltrated. Upon further investigation, the analysts believe confidential data was compromised. Which of the following capabilities would BEST defend against this type of sensitive data exfiltration?

  1. Deploy an edge firewall.
  2. Implement DLP.
  3. Deploy EDR.
  4. Encrypt the hard drives.

Question 39

After a series of Group Policy Object updates, multiple services stopped functioning. The systems administrator believes the issue resulted from a Group Policy Object update but cannot validate which update caused the issue. Which of the following security solutions would resolve this issue?

  1. Privilege management
  2. Group Policy Object management
  3. Change management
  4. Asset management

Question 40

Which of the following describes the main difference between supervised and unsupervised machine-learning algorithms that are used in cybersecurity applications?

  1. Supervised algorithms can be used to block attacks, while unsupervised algorithms cannot.
  2. Supervised algorithms require security analyst feedback, while unsupervised algorithms do not.
  3. Unsupervised algorithms are not suitable for IDS systems, while supervised algorithms are.
  4. Unsupervised algorithms produce more false positives than supervised algorithms.