Amazon Web Services Updated DOP-C02 Exam Questions and Answers by miriam

Enable AWS Config Across the Organization:

AWS Config provides a detailed view of the configuration of AWS resources in your AWS account. It can be used to assess, audit, and evaluate the configurations of your resources.

Enabling AWS Config across the organization ensures that all accounts are monitored for compliance.

Create a Conformance Pack Using the approved-amis-by-id AWS Config Managed Rule:

A conformance pack is a collection of AWS Config rules and remediation actions that can be easily deployed across an organization.

The approved-amis-by-id managed rule checks whether running instances are using approved AMIs.

Deploy the Conformance Pack Across the Organization:

Deploying the conformance pack across the organization ensures that all accounts adhere to the policy of using only approved AMIs.

The conformance pack can be deployed via the AWS Management Console, CLI, or SDKs.

Configure the Rule to Run the AWS-StopEC2Instance AWS Systems Manager Automation Runbook for Non-Compliant EC2 Instances:

The AWS-StopEC2Instance runbook can be configured to automatically stop any EC2 instances that are found to be non-compliant (i.e., not using approved AMIs).

This remediation action ensures that any unauthorized instances are promptly stopped, enforcing the policy without manual intervention.

By following these steps, the solution ensures that all EC2 instances across the organization use approved AMIs, and any non-compliant instances are remediated automatically.

Step 1: Storing Docker Images in Amazon ECRDocker images can be large, and storing them in a centralized, scalable location can greatly reduce build times. Amazon Elastic Container Registry (ECR) is a fully managed container registry that stores, manages, and deploys Docker container images.

Action: Store the Docker images in an ECR repository.

Why: Storing Docker images in ECR ensures that Docker images can be reused across multiple builds, improving build performance by avoiding the need to rebuild the images from scratch.

https://docs.aws.amazon.com/config/latest/developerguide/ec2-instance-profile-attached.html

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