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

Step 1: Reacting to RDS Storage Autoscaling Events Using Amazon EventBridgeAmazon RDS emits events when storage autoscaling occurs. To visualize these events in a CloudWatch dashboard, you can create an EventBridge rule that listens for these specific autoscaling events.

Action: Create an EventBridge rule that reacts to RDS storage autoscaling events from the RDS event stream.

Why: EventBridge allows you to listen to RDS events and route them to specific AWS services for processing.

Step 2: Creating a Custom CloudWatch Metric via LambdaOnce the EventBridge rule detects a storage autoscaling event, you can use a Lambda function to publish a custom metric to CloudWatch. This metric can then be visualized in a CloudWatch dashboard.

Action: Use a Lambda function to publish custom metrics to CloudWatch based on the RDS storage autoscaling events.

Why: Custom metrics allow you to track specific events like autoscaling and visualize them easily on a CloudWatch dashboard.

The latency in processing and ingesting logs can be caused by several factors, such as the throughput of the Kinesis data stream, the concurrency of the Lambda function, and the configuration of the event source mapping. To reduce the latency, the following steps can be taken:

Create a data stream consumer with enhanced fan-out. Set the Lambda function that processes the logs as the consumer. This will allow the Lambda function to receive records from the data stream with dedicated throughput of up to 2 MB per second per shard, independent of other consumers1. This will reduce the contention and delay in accessing the data stream.

Increase the ParallelizationFactor setting in the Lambda event source mapping. This will allow the Lambda service to invoke more instances of the function concurrently to process the records from the data stream2. This will increase the processing capacity and reduce the backlog of records in the data stream.

Configure reserved concurrency for the Lambda function that processes the logs. This will ensure that the function has enough concurrency available to handle the increased load from the data stream3. This will prevent the function from being throttled by the account-level concurrency limit.

The other options are not effective or may have negative impacts on the latency. Option D is not suitable because increasing the batch size in the Kinesis data stream will increase the amount of data that the Lambda function has to process in each invocation, which may increase the execution time and latency4. Option E is not advisable because turning off the ReportBatchItemFailures setting in the Lambda event source mapping will prevent the Lambda service from retrying the failed records, which may result in data loss. Option F is not necessary because increasing the number of shards in the Kinesis data stream will increase the throughput of the data stream, but it will not affect the processing speed of the Lambda function, which is the bottleneck in this scenario.

1: Using AWS Lambda with Amazon Kinesis Data Streams - AWS Lambda

2: AWS Lambda event source mappings - AWS Lambda

3: Managing concurrency for a Lambda function - AWS Lambda

4: AWS Lambda function scaling - AWS Lambda

AWS Lambda event source mappings - AWS Lambda

Scaling Amazon Kinesis Data Streams with AWS CloudFormation - Amazon Kinesis Data Streams

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

During an Amazon ECS service deployment, if tasks fail the Elastic Load Balancing target group health checks, Amazon ECS considers those tasks unhealthy and stops and replaces them. This behavior can cause the deployment to remain incomplete because healthy replacement tasks never become available.

Also, if an essential container in the task exits, the entire task stops. Since the question states that the new tasks enter a stopped state soon after launch, an essential container failure is a direct and highly likely cause.

Why the other options are incorrect:

B. The IAM role used by the DevOps engineer to update the ECS service does not need RunTask permission for the tasks to remain running after launch. A permission issue here would more likely affect the update action itself rather than cause launched tasks to stop shortly afterward.

C. Although CloudWatch Logs configuration problems can affect logging, the missing log group is not the most likely direct reason for ECS tasks to repeatedly stop in this deployment scenario.

D. The task role provides permissions to the application code running inside the container. It is not the role primarily responsible for launching the task. Task startup is typically related to the task execution role, ECS service behavior, container health, and load balancer checks.