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

To ensure that CloudTrail cannot be disabled in any member account, the control must be enforced above the individual accounts, at the organization level , and must not be overridable by local administrators. AWS Service Control Policies (SCPs) provide exactly this capability: they define permission guardrails that apply to all IAM users, roles, and even the root user in accounts within an AWS Organizations OU.

By applying an SCP that explicitly denies cloudtrail:StopLogging and cloudtrail:DeleteTrail , the organization prevents any principal in the affected accounts from disabling logging or deleting the trails, regardless of their IAM permissions. This provides strong, centralized enforcement of audit logging.

Option B uses IAM policies in each account. Local administrators with sufficient privileges could modify or detach those policies, defeating the control. Option C only sends notifications and does not prevent the action. Option D with AWS Config auto-remediation still allows a window where CloudTrail is disabled and also can be disabled or modified itself.

Therefore, the most robust and least bypassable approach is to use an SCP at the OU level to deny CloudTrail stop and delete actions.

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