DOP-C02 Exam Dumps - Amazon Web Services AWS Certified Professional Questions and Answers

The following are the steps that the company can take to change the log level dynamically when the deployment occurs:

Create a script that uses the CodeDeploy environment variable DEPLOYMENT_GROUP_NAME to identify which deployment group the instance is part of.

Use this information to configure the log level settings.

Reference this script as part of the BeforeInstall lifecycle hook in the appspec.yml file.

The DEPLOYMENT_GROUP_NAME environment variable is automatically set by CodeDeploy when the deployment is triggered. This means that the script does not need to call the metadata service or the EC2 API to identify the deployment group.

This solution is the least complex and requires the least management overhead. It also does not require different script versions for each deployment group.

The following are the reasons why the other options are not correct:

Option A is incorrect because it would require tagging the Amazon EC2 instances, which would be a manual and time-consuming process.

Option C is incorrect because it would require creating a custom environment variable for each environment. This would be a complex and error-prone process.

Option D is incorrect because it would use the DEPLOYMENT_GROUP_ID environment variable. However, this variable is not automatically set by CodeDeploy, so the script would need to call the metadata service or the EC2 API to get the deployment group ID. This would add complexity and overhead to the solution.

 Step 1: Deploy the Automation Solution using CloudFormation StackSets

To automate the process across multiple AWS accounts within an organization, you can use AWS CloudFormation StackSets. StackSets allow you to deploy CloudFormation templates to multiple accounts within an organization, ensuring consistent infrastructure and automation.

Action: Use AWS CloudFormation StackSets to deploy the necessary resources across all AWS accounts. This includes deploying the Lambda function and security groups that will isolate compromised EC2 instances.

Why: StackSets make it easy to deploy and manage resources across multiple AWS accounts, reducing the operational overhead.

AWS CodeDeploy natively supports automatic rollback based on CloudWatch alarms . To use this capability, you define an alarm that indicates unhealthy behavior (for example, increasing application error rate) and attach that alarm to the deployment group in CodeDeploy. If the alarm goes into ALARM state during or after a deployment, CodeDeploy automatically rolls back to the last known good revision when auto rollback is enabled.

Option A correctly implements this pattern:

Derive a CloudWatch metric from application logs (e.g., count of error-level log entries or HTTP 5xx responses).

Create a CloudWatch alarm on that metric with appropriate thresholds.

Configure the deployment group to use the alarm as part of its alarm configuration and enable auto rollback .

Option B uses CodeDeploy agent logs, which may not directly reflect true application health and couples rollback logic tightly to deployment internals rather than application behavior. Options C and D implement custom rollback logic with Lambda and EventBridge, which re-create behavior that CodeDeploy already provides natively and add unnecessary operational and code complexity.

Therefore, Option A is the simplest and most aligned with built-in CodeDeploy features for automated rollback based on application errors.

The following are the steps that the DevOps engineer should take to reduce the latency of the Lambda function at all times of the day:

Configure provisioned concurrency on the Lambda function.

Configure AWS Application Auto Scaling on the Lambda function with provisioned concurrency values set to a minimum of 1 and a maximum of 100.

The provisioned concurrency setting ensures that there is always a minimum number of Lambda function instances available to handle requests. The Application Auto Scaling setting will automatically scale the number of Lambda function instances up or down based on the demand for the application.

This solution will ensure that the Lambda function is able to handle the increased load during the middle of the day, while also keeping the cold-start latency low.

The following are the reasons why the other options are not correct:

Option A is incorrect because it will not reduce the cold-start latency of the Lambda function.

Option B is incorrect because it will not scale the number of Lambda function instances up or down based on demand.

Option D is incorrect because it will only configure reserved concurrency on the API Gateway API, which will not affect the Lambda function.

The company’s requirements span preventive governance , account baseline configuration , and continuous compliance monitoring , all with minimal operational overhead. AWS-native, organization-level services are the most efficient way to meet these goals.

To ensure that new accounts do not retain default VPCs and that development is restricted to specific Regions , AWS Control Tower is the correct foundation. However, Control Tower alone does not remove default VPCs. By installing Customizations for AWS Control Tower (CfCT) , the company can automatically deploy OU-scoped CloudFormation templates during account provisioning. A simple CloudFormation template can delete default VPCs in all Regions, while Control Tower-managed Region deny guardrails (SCPs) restrict access to only approved Regions. This approach is declarative, repeatable, and tightly integrated with account creation workflows, resulting in low operational overhead.

For CIS AWS Foundations Benchmark compliance monitoring , AWS Security Hub is the purpose-built service. Enabling Security Hub at the organization level and selecting the CIS benchmark automatically evaluates all member accounts against CIS controls and continuously reports findings. This provides centralized visibility and compliance reporting without custom rule development.

Option C introduces custom Lambda automation and EventBridge logic, increasing maintenance burden. Option E is incorrect because Control Tower does not provide full CIS benchmark monitoring; it only offers related detective guardrails.

Therefore, Option B (Control Tower + CfCT) and Option D (Security Hub with CIS benchmark) together provide the most efficient, scalable, and AWS-recommended solution.

Amazon GuardDuty provides built-in, fully managed threat detection for AWS accounts and workloads, including automatic detection of cryptocurrency mining activity, compromised EC2 instances, command-and-control traffic patterns, and anomalous behaviors. GuardDuty includes multiple threat identifiers such as EC2:CryptoCurrencyActivity.BitcoinToolDetected and EC2:UnauthorizedAccess.CryptoMining. Because this capability is native and requires no custom detection logic, it provides the lowest development and operational overhead compared to alternatives.

When GuardDuty generates a finding, it publishes the event automatically to Amazon EventBridge. The DevOps engineer can create an EventBridge rule that matches the specific mining-related finding types. The target of the rule is an AWS Lambda function that parses instance details from the finding and calls TerminateInstances on the affected EC2 instance.

This enables real-time automated remediation without needing recurring scans, log parsing, or complex data queries.

Options A and B require building custom detection pipelines and maintaining periodic Lambda polling, increasing long-term complexity. Option D (Security Hub) aggregates findings but still relies on GuardDuty for detection, adding unnecessary overhead.

Therefore, GuardDuty + EventBridge + Lambda termination is the easiest, quickest, and most AWS-recommended solution following incident-response best practices.