To meet the requirements, the company needs to create a solution that alerts the Senior Manager when the creation of resources approaches the service limits for the account with the least amount of development effort. The company can use AWS Trusted Advisor, which is a service that provides best practice recommendations for cost optimization, performance, security, and service limits. The company can deploy an AWS Lambda function that refreshes Trusted Advisor checks, and configure an Amazon CloudWatch Events rule to run the Lambda function periodically. This will ensure that Trusted Advisor checks are up to date and reflect the current state of the account. The company can then create another CloudWatch Events rule with an event pattern matching Trusted Advisor events and a target Lambda function. The event pattern can filter for events related to service limit checks and their status. The target Lambda function can notify the Senior Manager via a third-party API call if the event indicates that the account is approaching or exceeding a service limit.

The company requires an organization-wide, centralized, and automated solution to detect sensitive data in Amazon S3, aggregate findings in one location, and quarantine affected objects with minimal operational overhead. AWS provides a native service specifically designed for this purpose: Amazon Macie .
Option A is essential because Amazon Macie automatically discovers and classifies sensitive data such as PII in S3 buckets using managed machine learning models. When enabled at the organization level , Macie scans buckets across all accounts and Regions. Integrating Macie with AWS Security Hub centralizes all findings in a single dashboard, allowing the company's security officer to review and manage sensitive data alerts across the organization without building custom aggregation pipelines.
Detection alone is not sufficient; remediation is also required. Option C completes the solution by using Amazon EventBridge , which natively receives Macie findings in near real time. An EventBridge rule can trigger a Lambda function whenever Macie identifies sensitive data. The Lambda function can then copy the affected object to a quarantine S3 bucket and delete the original object, meeting the remediation requirement automatically and consistently.
Option D requires custom sensitive data detection logic, which is complex, error-prone, and unnecessary given Macie's capabilities. Option E is invalid because SCPs cannot inspect or move data. Option B does not detect sensitive information.
Therefore, A and C together provide the most efficient, scalable, and AWS-recommended solution.

The correct answer is C. Using AWS Config to identify and audit all EC2 instances based on their host placement configuration is the most efficient and scalable solution to ensure compliance with the software licensing requirement. AWS Config is a service that enables you to assess, audit, and evaluate the configurations of your AWS resources. By creating a custom AWS Config rule that triggers a Lambda function to verify host placement, the DevOps engineer can automate the process of checking whether the instances are running on EC2 Dedicated Hosts or not. The Lambda function can return a NON_COMPLIANT result if the instance is not running on an EC2 Dedicated Host, and the AWS Config report can provide a summary of the compliance status of the instances. This solution requires the least administrative overhead compared to the other options.
Option A is incorrect because using AWS Systems Manager Configuration Compliance to scan and build a database of noncompliant EC2 instances based on their host placement configuration is a more complex and costly solution than using AWS Config. AWS Systems Manager Configuration Compliance is a feature of AWS Systems Manager that enables you to scan your managed instances for patch compliance and configuration inconsistencies. To use this feature, the DevOps engineer would need to install the Systems Manager Agent on each EC2 instance, create a State Manager association to run the put-compliance-items API action periodically, and use a DynamoDB table to store the instance IDs of noncompliant resources. This solution would also require more API calls and storage costs than using AWS Config.
Option B is incorrect because using custom Java code running on an EC2 instance to check and terminate noncompliant EC2 instances is a more cumbersome and error-prone solution than using AWS Config. This solution would require the DevOps engineer to write and maintain the Java code, set up EC2 Auto Scaling for the instance, use an SQS queue and another worker instance to process the instance IDs, use a Lambda function and an SNS topic to terminate and notify the noncompliant instances, and handle any potential failures or exceptions in the workflow. This solution would also incur more compute, storage, and messaging costs than using AWS Config.
Option D is incorrect because using AWS CloudTrail to identify and audit EC2 instances by analyzing the EC2 RunCommand API action is a less reliable and accurate solution than using AWS Config. AWS CloudTrail is a service that enables you to monitor and log the API activity in your AWS account. The EC2 RunCommand API action is used to execute commands on one or more EC2 instances. However, this API action does not necessarily indicate the host placement of the instance, and it may not capture all the instances that are running on EC2 Dedicated Hosts or not. Therefore, option D would not provide a comprehensive and consistent audit of the EC2 instances.

EC2 Image Builder is the recommended AWS-native service for automating the creation, validation, and distribution of AMIs. It integrates directly with AWS Organizations, allowing AMIs to be automatically shared with all accounts. The service also supports scheduled builds (e.g., monthly), significantly reducing operational overhead. This design is referenced in AWS documentation: "Automate OS image creation and distribution securely across multiple accounts using EC2 Image Builder."

For gradual traffic shifting in Lambda deployments, AWS Lambda aliases support the RoutingConfig property, which specifies the percentage of traffic routed to different versions of the Lambda function. This enables weighted traffic shifting between versions as part of deployment strategies.
* The AWS::Lambda::Alias resource's RoutingConfig can specify multiple versions with weights, enabling canary or linear deployment strategies without needing CodeDeploy resources explicitly.
* AWS CodeDeploy resources like DeploymentConfig and DeploymentGroup are used primarily for blue
/green deployments and managing deployment strategies outside Lambda aliases.
* Lambda versions themselves do not have a VersionWeight property; instead, weighted routing is managed via aliases.
Reference:
AWS::Lambda::Alias - RoutingConfig: " Specifies the versions of the function and the percentage of traffic to send to each version. " (AWS CloudFormation Lambda Alias) AWS Lambda Deployment Preferences: " Weighted aliases enable gradual traffic shifting between Lambda function versions. " (AWS Lambda Deployment Preferences)