Amazon Web Services Updated DOP-C02 Exam Questions and Answers by zayna

When using the awslogs log driver with Amazon ECS on EC2, CloudWatch Logs permissions must be granted to the ECS container instance IAM role , not the task definition or infrastructure role. The ECS agent running on the EC2 instances is responsible for creating log streams and pushing log events to Amazon CloudWatch Logs on behalf of the containers.

In this scenario, the task definition is correctly configured to automatically create the log group (awslogs-create-group: true) and send logs to the specified Region. However, the error occurs because the EC2 container instances do not have sufficient IAM permissions to perform the required CloudWatch Logs API calls. As a result, ECS cannot place the task, and it reports that no container instance meets the requirements.

According to AWS documentation, the container instance IAM role must include the following permissions when using the awslogs driver:

logs:CreateLogStream

logs:PutLogEvents

(and, when creating log groups automatically) logs:CreateLogGroup

Option C correctly addresses the root cause by updating the container instance IAM role . Option A is incorrect because the ECS infrastructure or service role is not used to write logs. Option B is unrelated to permissions and does not resolve the issue. Option D would fail because the log group does not already exist, causing task startup to fail.

Therefore, modifying the container instance IAM role is the correct solution.

Step 1: Creating an Identity Provider in IAMYou first need to configure AWS to trust the external identity provider (IdP), which in this case supports OpenID Connect (OIDC). The IdP will handle the authentication, and AWS will handle the authorization based on the IdP ' s token.

Action: Create an IAM Identity Provider (IdP) in AWS using the existing provider ' s URL, audience, and signature. This step is essential for establishing trust between AWS and the external IdP.

Why: This allows AWS to accept tokens from your external IdP (auth.company.com) for authentication.

The recommended architecture to audit, analyze, and visualize application workload metrics at scale involves:

Using Amazon EventBridge to schedule AWS Lambda invocations that call the application API and fetch metrics (Option A). The data is stored in Amazon S3, which is ideal for scalable, cost-effective storage of large datasets.

Cataloging the stored data with AWS Glue crawlers, enabling schema discovery and making data queryable via Amazon Athena (Option C).

Visualizing the data by creating Amazon QuickSight datasets from Athena views and building dashboards for analysis (Option E). Option B and D introduce DynamoDB, which is less suitable for large-scale analytics and Athena querying. Option F suggests querying Athena via Lambda widgets in CloudWatch, which adds complexity without significant benefit over QuickSight.

 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.