DOP-C02 Exam Dumps : AWS Certified DevOps Engineer - Professional

 Use AWS Step Functions to Orchestrate Processing:

AWS Step Functions allow you to build distributed applications by combining AWS Lambda functions or other AWS services into workflows.

Decoupling the processing into Step Functions tasks enables you to retry individual steps without reprocessing the entire record.

 Architectural Steps:

Create a web application to pass records to AWS Step Functions:

The web application can be a simple frontend that receives input and triggers the Step Functions workflow.

Define a Step Functions state machine:

Each step in the state machine represents a processing stage. If a step fails, Step Functions can retry the step based on defined conditions.

Use AWS Lambda functions:

Lambda functions can be used to handle each processing step. These functions can be stateless and handle specific tasks, reducing the complexity of error handling and reprocessing logic.

 Operational Efficiency:

Using Step Functions and Lambda improves operational efficiency by providing built-in error handling, retries, and state management.

This architecture scales automatically and isolates failures to individual steps, ensuring only failed steps are retried.

CloudWatch metric filters can parse logs directly to create metrics without additional infrastructure.

Metric filters combined with CloudWatch dashboards provide the simplest and most operationally efficient solution.

Options A, B, and D add complexity with additional services (OpenSearch, Lambda, Kinesis).

The best solution to log and review all stopped tasks for errors is to use Amazon EventBridge and Amazon CloudWatch Logs. Amazon EventBridge allows the DevOps engineer to create a rule that matches task state change events from Amazon ECS. The rule can then send the event data to Amazon CloudWatch Logs as the target. Amazon CloudWatch Logs can store and monitor the log data, and also provide CloudWatch Logs Insights, a feature that enables the DevOps engineer to interactively search and analyze the log data. Using CloudWatch Logs Insights, the DevOps engineer can filter and aggregate the log data based on various fields, such as cluster, task, container, and reason. This way, the DevOps engineer can easily identify and investigate the stopped tasks and their errors.

The other options are not as effective or efficient as the solution in option A. Option B is not suitable because the embedded metric format is designed for custom metrics, not for logging task state changes. Option C is not feasible because the EC2 instances do not store the task state change events in their logs. Option D is not relevant because the EC2_INSTANCE_TERMINATING lifecycle hook is triggered when an EC2 instance is terminated by the Auto Scaling group, not when a task is stopped by Amazon ECS.

Creating a CloudWatch Events Rule That Triggers on an Event - Amazon Elastic Container Service

Sending and Receiving Events Between AWS Accounts - Amazon EventBridge

Working with Log Data - Amazon CloudWatch Logs

Analyzing Log Data with CloudWatch Logs Insights - Amazon CloudWatch Logs

Embedded Metric Format - Amazon CloudWatch

Amazon EC2 Auto Scaling Lifecycle Hooks - Amazon EC2 Auto Scaling