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

The requirement is not just to perform a one-time upgrade, but to ensure that Kubernetes and related components stay continuously within the AWS standard support window, with minimal ongoing effort. EKS Auto Mode is designed to reduce operational burden by managing cluster infrastructure, including control plane and data plane lifecycle, in a more automated fashion. When EKS Auto Mode is enabled and existing managed node groups are removed, EKS can manage compute capacity and associated upgrades in a more integrated, hands-off way, tracking supported versions automatically.

Option A (aws eks update-cluster-version) and Option C (eksctl upgrade) are both manual upgrade mechanisms. They can update clusters to a specific version but do not continuously ensure future upgrades as new supported versions are released; they require the DevOps team to schedule and execute each upgrade. Option D (IaC refactor) is generally a good practice but does not inherently provide automatic version lifecycle management; it also introduces significant upfront effort compared to enabling Auto Mode.

Thus, enabling EKS Auto Mode and deprecating manual managed node groups provides the least operational overhead for staying within the EKS-supported version schedule.

 Configure AWS Systems Manager on Each Instance:

AWS Systems Manager provides a unified interface for managing AWS resources. Install the Systems Manager agent on each EC2 instance to enable inventory management and other features.

 Use AWS Systems Manager Inventory:

Systems Manager Inventory collects metadata about your instances and the software installed on them. This data includes information about applications, network configurations, and more.

Enable Systems Manager Inventory on all EC2 instances to gather detailed information about installed applications.

 Use Systems Manager Resource Data Sync to Synchronize and Store Findings in an Amazon S3 Bucket:

Resource Data Sync aggregates inventory data from multiple accounts and regions into a single S3 bucket, making it easier to query and analyze the data.

Configure Resource Data Sync to automatically transfer inventory data to an S3 bucket for centralized storage.

 Create an AWS Lambda Function that Runs When New Objects are Added to the S3 Bucket:

Use an S3 event to trigger a Lambda function whenever new inventory data is added to the S3 bucket.

The Lambda function can parse the inventory data and check for the presence of prohibited applications.

 Configure the Lambda Function to Identify Prohibited Applications:

The Lambda function should be programmed to scan the inventory data for any known prohibited applications and generate alerts or take appropriate actions if such applications are found.

Example Lambda function in Python

import json

import boto3

def lambda_handler(event, context):

s3 = boto3.client('s3')

bucket = event['Records'][0]['s3']['bucket']['name']

key = event['Records'][0]['s3']['object']['key']

response = s3.get_object(Bucket=bucket, Key=key)

inventory_data = json.loads(response['Body'].read().decode('utf-8'))

prohibited_apps = ['app1', 'app2']

for instance in inventory_data['Instances']:

for app in instance['Applications']:

if app['Name'] in prohibited_apps:

# Send notification or take action

print(f"Prohibited application found: {app['Name']} on instance {instance['InstanceId']}")

return {'statusCode': 200, 'body': json.dumps('Check completed')}

By leveraging AWS Systems Manager Inventory, Resource Data Sync, and Lambda, this solution provides an efficient and automated way to audit EC2 instances for prohibited applications.

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.