Pass the Amazon Web Services AWS Certified Professional DOP-C02 Questions and answers with CertsForce

https://docs.aws.amazon.com/systems-manager/latest/userguide/sysman-managed-instance-activation.html

This is a classic AWS Organizations governance design:

Restrict Regions and allowed services across all accounts

The correct Organizations mechanism for account-wide guardrails is a Service Control Policy (SCP) .

SCPs set the maximum permissions that accounts/OUs can use, making them the right tool to enforce “only these Regions” and “only these services” consistently across the org.

Authentication from Active Directory + consistent job-function roles in every account

With AD as the identity source, the standard approach is federation to AWS using an IAM identity provider (SAML) and role assumption based on job function.

AWS CloudFormation StackSets is the operationally efficient way to deploy identical IAM roles/policies into multiple accounts/OUs so job-function permissions are consistent everywhere.

The roles’ trust policies can be configured to allow federated identities (from the AD-backed IdP) to assume the correct job role in each account.

Why the other options don’t fit:

A “OU with group policies” isn’t an AWS Organizations control mechanism for restricting Regions/services. The AWS-native control for this is SCPs , not “group policies.”

B Permissions boundaries are useful to limit what an IAM principal/role can do within an account , but they are not the org-wide enforcement mechanism for “all accounts must stay in these Regions / use only these services.” That’s what SCPs do.

C AWS RAM is for sharing resources (like subnets, Transit Gateway, etc.), not for “sharing roles” across accounts as a governance baseline. Also, the question emphasizes identical permissions in each account, which is best achieved by provisioning roles in each account (StackSets), not attempting to “share” roles.

 Ensure that the Amazon CloudWatch agent is installed on all EC2 instances:

The Amazon CloudWatch agent collects and logs metrics and sends them to Amazon CloudWatch.

To install the CloudWatch agent:

Download the CloudWatch agent package.

Install the agent on your EC2 instances.

Configure the agent to collect disk space metrics.

 Create an Amazon CloudWatch alarm to monitor available disk space on all EC2 instances Add the alarm as a safety control to the Systems Manager Automation task:

Create CloudWatch alarms to monitor the available disk space and trigger notifications or actions when the disk space falls below a defined threshold.

Add the CloudWatch alarm to the Systems Manager Automation task to halt or fail the task if disk space is insufficient.

To create the alarm:

Navigate to the CloudWatch console and create a new alarm.

Set the metric to monitor (e.g., disk space utilization).

Define the threshold and notification actions.

 Modify the Build Stage to Add a Test Action with a RunOrder Value of 2:

The build stage in AWS CodePipeline can have multiple actions. By adding a test action with a runOrder value of 2, the test action will execute after the initial build action completes.

 Use AWS CodeBuild as the Action Provider to Run Unit Tests:

AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces software packages.

Using CodeBuild to run unit tests ensures that the tests are executed in a controlled environment and that only the code changes that pass the unit tests proceed to the deploy stage.

Example configuration in CodePipeline:

{

" name " : " BuildStage " ,

" actions " : [

{

" name " : " Build " ,

" actionTypeId " : {

" category " : " Build " ,

" owner " : " AWS " ,

" provider " : " CodeBuild " ,

" version " : " 1 "

},

" runOrder " : 1

},

{

" name " : " Test " ,

" actionTypeId " : {

" category " : " Test " ,

" owner " : " AWS " ,

" provider " : " CodeBuild " ,

" version " : " 1 "

},

" runOrder " : 2

}

]

}

By integrating the unit tests into the build stage and ensuring they run after the build process, the pipeline guarantees that only code changes passing all unit tests are deployed.

https://aws.amazon.com/blogs/mt/org-aggregator-delegated-admin/ https://docs.aws.amazon.com/organizations/latest/userguide/services-that-can-integrate-config.html

Comprehensive and Detailed Explanation From Exact Extract of DevOps Engineer documents only:

The requirement is to shift traffic automatically in equal increments over a defined total deployment time with no manual intervention. In AWS CodeDeploy blue/green deployments for Amazon ECS, the TimeBasedLinear traffic routing option is specifically designed for this purpose.

Option A is correct because:

TimeBasedLinear shifts traffic in equal percentages.

The deployment proceeds automatically at each interval.

linearPercentage defines how much traffic is shifted each time.

linearInterval defines how often the traffic shift happens.

This supports gradual traffic shifting and zero-downtime blue/green deployment behavior.

Why the other options are incorrect:

B. AllAtOnce shifts all traffic immediately, not in equal increments over time.

C. TimeBasedCanary shifts traffic in an initial portion and then the remainder later. That is not equal incremental shifting across the full deployment period.

D. This option is too generic and does not specifically identify the CodeDeploy configuration that provides equal incremental automatic traffic shifting. The exact feature required is TimeBasedLinear.

The key requirement is to shift a small percentage of traffic to a new version first to validate changes before rolling out to the entire ECS service, thereby reducing downtime and deployment risk. For ECS services behind an Application Load Balancer, the AWS-recommended and most reliable approach is to use AWS CodeDeploy with blue/green or canary deployments .

Option B directly addresses this requirement. By configuring the ECS service to use CodeDeploy as the deployment controller , CodeDeploy can manage traffic shifting between two ALB target groups: one for the current (stable) version and one for the new version. The CodeDeployDefault.ECSCanary10Percent15Minutes deployment configuration initially routes 10% of production traffic to the new task set, monitors the deployment for issues, and then shifts the remaining traffic after the defined interval. This enables early detection of application errors while limiting user impact.

Option A and C rely on rolling updates , which replace tasks incrementally but do not provide precise traffic control or automated rollback based on health checks. Option D’s slow start setting only affects how quickly targets receive traffic after registration and does not implement true canary testing.

Therefore, using AWS CodeDeploy with ECS canary deployments is the most robust, AWS-supported solution to test changes safely before full rollout.

For cross-account deployments, CodePipeline must assume a role in the target account. Configure S3 bucket policy to allow the target account (CodeDeployAccount) access, trust relationship in DevOps_Role to allow assumption by the pipeline’s account (PipelineAccount), and update CodePipeline_Service_Role with sts:AssumeRole permissions. This cross-account pipeline setup is documented in “Cross-Account Access for AWS CodePipeline.”

The requirement is simply: review changes before production deployment , with the least operational overhead .

B is the lightest change: adding a Manual approval (review) action in CodePipeline creates a controlled gate before the deploy stage. It requires no new repositories, no new services, and no custom code—just pipeline configuration.

Why not the others:

A introduces additional moving parts (Git repo integration, PR workflow management, and CloudFormation Git sync). That’s useful, but it’s more operational overhead than necessary to satisfy “review before deploy.”

C requires custom Lambda logic to inspect templates and decide whether to proceed—more code to write, run, secure, and maintain.

D adds both Git integration and a manual approval step—again more overhead than just adding the approval gate.

So B best meets the requirement with the least operational effort: a simple manual approval stage in the pipeline before production deployment.

When using the awslogs log driver with ECS on EC2, the ECS agent running on the container instance uses the instance’s IAM role (container instance role or task execution role, depending on configuration) to write logs to CloudWatch Logs. The policy already grants logs:CreateLogGroup , logs:CreateLogStream , and logs:PutLogEvents , which are the required CloudWatch Logs actions. However, for the role to be usable by ECS, the role’s trust policy must allow the appropriate service principal to assume it.

In this question, the error message indicates “missing permission” during ECS task deployment. If the IAM role is not trusted by the ECS service (for example, ecs-tasks.amazonaws.com for a task execution role or the proper principal for container instances), ECS cannot assume that role and therefore cannot use the granted CloudWatch permissions, causing deployment failures.

Option A addresses this by adding a trust relationship so that Amazon ECS can assume the IAM role. Options B and C mutate the permissions but do not fix the underlying problem: the missing trust. Option D incorrectly attempts to trust CloudWatch, which does not assume roles in this context.

Thus, adding a trust policy that establishes ECS as a trusted service is the correct fix.