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

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

Option B is the correct choice and has the least operational overhead because ECR lifecycle policies are the native, managed feature built specifically to automate image cleanup:

ECR lifecycle policies can expire images based on image age (e.g., images older than X days) and can also enforce retaining a minimum number of images (count-based retention), which directly matches the requirement “retain at least a specified number of images.”

Lifecycle policies work for both tagged and untagged images using rule selection and tagging status filters, so the repository stays clean without custom automation.

Why the other options are not appropriate / higher overhead:

A is invalid: ECR is not S3; S3 lifecycle policies do not manage ECR images .

C and D introduce custom automation (Lambda or SSM scripting), scheduling, permissions, error handling, retries, logging, and ongoing maintenance—higher overhead than a managed lifecycle policy.