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

This solution will meet the requirements because it will use Amazon S3 as a common data lake for both the application logs and the API logs. Amazon S3 is a service that provides scalable, durable, and secure object storage for any type of data. You can use the Amazon CloudWatch agent to send logs from your EC2 instances to S3 buckets, and use AWS CloudTrail to deliver the API logs to S3 buckets as well. You can also use Amazon Athena to query both sets of logs in S3 using standard SQL, without loading or transforming them. Athena is a serverless interactive query service that allows you to analyze data in S3 using a variety of data formats, such as JSON, CSV, Parquet, and ORC.

To prevent ApplicationA from being installed on the additional instance, the deployment group configuration needs to be more specific. By changing the current single tag group to include only the Environment=Production tag and adding another single tag group that includes only the Name=ApplicationA tag, the deployment process will target only the instances that match both tag groups. This ensures that only instances intended for ApplicationA with the correct environment and name tags will receive the deployment, thus excluding the additional instance with the Department=Marketing and Name=ApplicationB tags.

AWS CodeDeploy Documentation: Working with instances for CodeDeploy

AWS CodeDeploy Documentation: Stop a deployment with CodeDeploy

Stack Overflow Discussion: CodeDeploy Deployment failed to stop Application

Enabling EKS control plane logs to CloudWatch captures API requests. CloudWatch Container Insights collects node and pod-level performance data with no additional infrastructure. AWS recommends this integrated observability solution for minimal management overhead.

AWS Organizations is a service that helps to manage multiple AWS accounts. AWS Control Tower is a service that makes it easy to set up and govern secure, compliant multi-account AWS environments. Account Factory for Terraform (AFT) is an AWS Control Tower feature that provisions new accounts using Terraform templates. To provision new accounts with the Enterprise Support plan, the DevOps engineer can set the aft_feature_enterprise_support feature flag to True in the AFT deployment input configuration. This flag enables the Enterprise Support plan for newly provisioned accounts.

https://docs.aws.amazon.com/controltower/latest/userguide/aft-feature-options.html

Verify No SCP Blocking Access:

Ensure that no Service Control Policy (SCP) is blocking access for developers to the S3 bucket. SCPs are applied at the organization or organizational unit (OU) level in AWS Organizations and can restrict what actions users and roles in the affected accounts can perform.

Verify No IAM Policy Permissions Boundaries Blocking Access:

IAM permissions boundaries can limit the maximum permissions that a user or role can have. Verify that these boundaries are not restricting access to the S3 bucket.

Make Necessary Changes to SCP and IAM Policy Permissions Boundaries:

Adjust the SCPs and IAM permissions boundaries if they are found to be the cause of the access issue. Make sure these changes are reflected in the code maintained in the AWS CodeCommit repository.

Invoke Deployment Through CloudFormation:

Commit the updated policies to the CodeCommit repository.

Use AWS CloudFormation to deploy the changes across the relevant accounts and resources to ensure that the updated permissions are applied consistently.

By ensuring no SCPs or IAM policy permissions boundaries are blocking access and making necessary changes if they are, the DevOps engineer can resolve the access issue for developers trying to access the S3 bucket.

ï‚· Ensure that Amazon GuardDuty is Enabled:

Amazon GuardDuty is a threat detection service that continuously monitors for malicious activity and unauthorized behavior.

It can detect port scans and generate findings for these events.

ï‚· Create an Amazon CloudWatch Alarm for Detected EC2 and Port Scan Findings:

Configure GuardDuty to monitor for port scans and other threats.

Create a CloudWatch alarm that triggers when GuardDuty detects port scan activities.

ï‚· Connect the Alarm to the SNS Topic:

The CloudWatch alarm should be configured to send notifications to the SNS topic subscribed by the security team.

This setup ensures that the security team receives near-real-time notifications when a port scan is detected on the EC2 instances.

Example configuration steps:

Enable GuardDuty and ensure it is monitoring the relevant AWS accounts.

Create a CloudWatch alarm:

{

" AlarmName " : " GuardDutyPortScanAlarm " ,

" MetricName " : " ThreatIntelIndicator " ,

" Namespace " : " AWS/GuardDuty " ,

" Statistic " : " Sum " ,

" Dimensions " : [

{

" Name " : " FindingType " ,

" Value " : " Recon:EC2/Portscan "

}

],

" Period " : 300,

" EvaluationPeriods " : 1,

" Threshold " : 1,

" ComparisonOperator " : " GreaterThanOrEqualToThreshold " ,

" AlarmActions " : [ " arn:aws:sns:region:account-id:SecurityAlerts " ]

}