Amazon Web Services DOP-C02 Exam With Confidence Using Practice Dumps

The correct approach to meet the requirements for separate environment pipelines and automatic deployment for test environments is to create two AWS CodePipeline configurations, one for each environment. The production pipeline should have a manual approval step to ensure that changes are reviewed before being deployed to production. A single AWS CodeCommit repository with separate branches for each environment allows for organized and efficient code management. Each CodePipeline retrieves the source code from the appropriate branch in the repository. The deployment step utilizes AWS CloudFormation to deploy the Lambda functions, ensuring that the infrastructure as code is maintained and version-controlled.

AWS Lambda with Amazon API Gateway: Using AWS Lambda with Amazon API Gateway

Tutorial on using Lambda with API Gateway: Tutorial: Using Lambda with API Gateway

AWS CodePipeline automatic deployment: Set Up a Continuous Deployment Pipeline Using AWS CodePipeline

Building a pipeline for test and production stacks: Walkthrough: Building a pipeline for test and production stacks

To meet the requirements, the DevOps team needs to configure a monitoring solution for the VPC flow logs that can detect anomalies in network traffic over time and initiate a response to the anomaly. The DevOps team can use Amazon Kinesis Data Streams to ingest and process streaming data from CloudWatch Logs. The DevOps team can subscribe the log group to a Kinesis data stream, which will deliver log events from CloudWatch Logs to Kinesis Data Streams in near real-time. The DevOps team can then create an AWS Lambda function to detect log anomalies using machine learning or statistical methods. The Lambda function can be set as a processor for the data stream, which means that it will process each record from the stream before sending it to downstream applications or destinations. The Lambda function can also write to the default Amazon EventBridge event bus if it detects an anomaly, which will allow other AWS services or custom applications to respond to the anomaly event.

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.