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

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The correct answer is B because AWS Config can continuously evaluate the account-level S3 Block Public Access configuration by using a managed rule. When the configuration becomes noncompliant, AWS Config can trigger automatic remediation through an AWS Systems Manager Automation runbook to restore the required settings.

This solution directly satisfies both requirements:

Detect changes to account-level S3 public access settings.

Automatically revert those changes so that all public access is blocked again.

Why the other options are incorrect:

A. Security Hub is primarily for aggregated security findings and controls visibility. It is not the most direct or standard service for continuous configuration evaluation with automatic remediation of this specific setting.

C. An SCP can restrict permissions, but it does not automatically detect and revert changes to S3 Block Public Access settings.

D. Amazon Macie is for discovering and protecting sensitive data, not for continuously enforcing and remediating S3 Block Public Access account settings.

AWS recommends centralizing CodeDeploy agent logs in Amazon CloudWatch Logs for efficient analysis. By configuring the CloudWatch agent or using the CodeDeploy console integration, you can stream logs in near-real time and query them with CloudWatch Logs Insights. This enables filtering by deployment lifecycle events, saving time versus manually connecting to instances. Refer to AWS documentation section “Monitoring and troubleshooting deployments with CodeDeploy and CloudWatch.”