A Company's Developers Plan to Migrate Their On-Premises Applications to Amazon

question 76

Multiple Choice

A company's Developers plan to migrate their on-premises applications to Amazon EC2 instances running Amazon Linux AMIs. The applications are accessed by a group of partner companies. The Security Engineer needs to implement the following host-based security measures for these instances: Block traffic from documented known bad IP addresses. Detect known software vulnerabilities and CIS Benchmarks compliance. Which solution addresses these requirements?

• A) Launch the EC2 instances with an IAM role attached. Include a user data script that uses the AWS CLI to retrieve the list of bad IP addresses from AWS Secrets Manager, and uploads it as a threat list in Amazon GuardDuty. Use Amazon Inspector to scan the instances for known software vulnerabilities and CIS Benchmarks compliance.
• B) Launch the EC2 instances with an IAM role attached. Include a user data script that uses the AWS CLI to create NACLs blocking ingress traffic from the known bad IP addresses in the EC2 instance's subnets. Use AWS Systems Manager to scan the instances for known software vulnerabilities, and AWS Trusted Advisor to check instances for CIS Benchmarks compliance.
• C) Launch the EC2 instances with an IAM role attached. Include a user data script that uses the AWS CLI to create and attach security groups that only allow an allow listed source IP address range inbound. Use Amazon Inspector to scan the instances for known software vulnerabilities, and AWS Trusted Advisor to check instances for CIS Benchmarks compliance.
• D) Launch the EC2 instances with an IAM role attached. Include a user data script that creates a cron job to periodically retrieve the list of bad IP addresses from Amazon S3, and configures iptables on the instances blocking the list of bad IP addresses. Use Amazon Inspector to scan the instances for known software vulnerabilities and CIS Benchmarks compliance.

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Definitions:

Predicted Values

Predicted values are the outcomes forecasted by a statistical model or algorithm based on existing data inputs.

Heteroscedasticity

A condition in regression analysis where the variance of the residuals or errors is not constant across all levels of an independent variable, violating one of the key assumptions of linear regression.

Errors

Differences between observed values and the values predicted by a model.

Standardized Residuals

Residuals that have been adjusted to have a mean of zero and a standard deviation of one, useful for comparing residuals across different datasets.