CAS-005 Exam Dumps : CompTIA SecurityX Certification Exam

For a disaster recovery (DR) plan requiring immediate data availability, the first step is understanding what needs to be protected and recovered. Identifying critical business processes and their associated software and hardware requirements establishes the foundation for the DR plan. This ensures that backups and recovery mechanisms align with business priorities, meeting the "moment's notice" requirement.

Option A:A change management plan is important for system consistency but doesn’t directly address immediate data availability in a DR context.

Option B:Hiring staff supports execution but doesn’t define what needs to be recovered or how. It’s a later step.

Option C:A warm site (a partially operational backup site) is a good DR solution, but designing it comes after identifying critical processes and resources.

Option D:This is the first step in any DR planning process—knowing what’s critical ensures the plan meets availability goals efficiently.

The question states that security scanning and quality assurance (QA) in the CI/CD pipeline have been completed with no issues, indicating that the code in the test branch is ready for production. According to the CompTIA SecurityX CAS-005 study guide (Domain 2: Security Operations, 2.3), in a secure CI/CD pipeline, once code passes automated security scans, QA, and other checks (e.g., unit testing, peer reviews), the next step is to merge the tested branch into the main branch for deployment to production.

Option B:Threat modeling is typically performed earlier, during design or development, not after passing CI/CD checks.

Option C:Unit testing is part of the CI/CD pipeline and should already be completed.

Option D:Peer reviews are conducted before or during the test phase, not after QAand security scans are clear.

Option A:Merging the test branch to the main branch is the logical next step to prepare for production deployment.

When using AI to fully automate the process of deciding client loan rates, the primary concern from a privacy perspective is model explainability.

Why Model Explainability is Critical:

Transparency: It ensures that the decision-making process of the AI model can be understood and explained to stakeholders, including clients.

Accountability: Helps in identifying biases and errors in the model, ensuring that the AI is making fair and unbiased decisions.

Regulatory Compliance: Various regulations require that decisions, especially those affecting individuals' financial status, can be explained and justified.

Trust: Builds trust among users and stakeholders by demonstrating that the AI decisions are transparent and justifiable.

Other options, such ascredential theft, prompt injections, and social engineering, are significant concerns but do not directly address the privacy and fairness implications of automated decision-making.