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A company is adopting AWS CodeDeploy to automate its application deployments for a Java-Apache Tomcat application with an Apache Webserver. The development team started with a proof of concept, created a deployment group for a developer environment, and performed functional tests within the application. After completion, the team will create additional deployment groups for staging and production.
The current log level is configured within the Apache settings, but the team wants to change this configuration dynamically when the deployment occurs, so that they can set different log level configurations depending on the deployment group without having a different application revision for each group.
How can these requirements be met with the LEAST management overhead and without requiring different script versions for each deployment group?
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 variableDEPLOYMENT_GROUP_NAMEto identify which deployment group the instanceis part of.
Use this information to configure the log level settings.
Reference this script as part of theBeforeInstalllifecycle hook in theappspec.ymlfile.
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 theDEPLOYMENT_GROUP_IDenvironment 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.
A company is launching an application. The application must use only approved AWS services. The account that runs the application was created less than 1 year ago and is assigned to an AWS Organizations OU.
The company needs to create a new Organizations account structure. The account structure must have an appropriate SCP that supports the use of only services that are currently active in the AWS account.
The company will use AWS Identity and Access Management (IAM) Access Analyzer in the solution.
Which solution will meet these requirements?
To meet the requirements of creating a new Organizations account structure with an appropriate SCP that supports the use of only services that are currently active in the AWS account, the company should use the following solution:
The other options are not correct because they do not meet the requirements or follow best practices. Creating an SCP that denies the services that IAM Access Analyzer identifies is not a good option because it might not cover all possible services that are not approved or required for the application. A deny policy is also more difficult to maintain and update than an allow policy. Creating an SCP that allows the services that IAM Access Analyzer identifies and attaching it to the organization's root is not a good option because it might affect other accounts and OUs in the organization that have different service requirements or approvals. Creating an SCP that allows the services that IAM Access Analyzer identifies and attaching it to the management account is not a valid option because SCPs cannot be attached directly to accounts, only to OUs or roots.
References:
1: Using AWS Identity and Access Management Access Analyzer - AWS Identity and Access Management
2: Generate a policy based on access activity - AWS Identity and Access Management
3: Organizing your accounts into OUs - AWS Organizations
4: Service control policies - AWS Organizations
5: How SCPs work - AWS Organizations
A company has a new AWS account that teams will use to deploy various applications. The teams will create many Amazon S3 buckets for application- specific purposes and to store AWS CloudTrail logs. The company has enabled Amazon Macie for the account.
A DevOps engineer needs to optimize the Macie costs for the account without compromising the account's functionality.
Which solutions will meet these requirements? (Select TWO.)
To optimize the Macie costs for the account without compromising the account's functionality, the DevOps engineer needs to exclude S3 buckets that do not contain sensitive data from automated discovery. S3 buckets that contain CloudTrail logs are unlikely to have sensitive data, and Macie charges for scanning and monitoring data in S3 buckets. Therefore, excluding S3 buckets that contain CloudTrail logs from automated discovery can reduce Macie costs. Similarly, configuring discovery jobs to include S3 objects based on the last modified criterion can also reduce Macie costs, as it will only scan and monitor new or updated objects, rather than all objects in the bucket.
A company recently created a new AWS Control Tower landing zone in a new organization in AWS Organizations. The landing zone must be able to demonstrate compliance with the Center tor Internet Security (CIS) Benchmarks tor AWS Foundations.
The company's security team wants to use AWS Security Hub to view compliance across all accounts Only the security team can be allowed to view aggregated Security Hub Findings. In addition specific users must be able to view findings from their own accounts within the organization All accounts must be enrolled m Security Hub after the accounts are created.
Which combination of steps will meet these requirements in the MOST automated way? (Select THREE.)
A DevOps engineer is researching the least expensive way to implement an image batch processing cluster on AWS. The application cannot run in Docker containers and must run on Amazon EC2. The batch job stores checkpoint data on an NFS volume and can tolerate interruptions. Configuring the cluster software from a generic EC2 Linux image takes 30 minutes.
What is the MOST cost-effective solution?
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