CompTIA CAS-005 Exam With Confidence Using Practice Dumps

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.

Multicloud provider solutionsinvolve using services from more than one cloud provider to ensure resiliency and redundancy. In the event of a failure or SLA breach by one CSP, another provider can maintain service continuity. An on-premises backup could help, but does not address CSP-specific SLA concerns directly. Round-robin load balancing and active-active within the same tenant still depend on a single provider, thus posing risks if the CSP fails.

Phishing simulations are a proven method for reinforcing security awareness, meeting compliance training requirements, and improving user incident reporting. In CAS-005, social engineering testing is a recommended component of organizational security culture programs.

DoS attacks (A) and penetration tests (B) assess technical security, not user awareness.

Fake ransomware (D) can cause unnecessary alarm and operational disruption.