DOP-C02 Amazon Web Services AWS Certified DevOps Engineer - Professional Free Practice Exam Questions (2025 Updated)

 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.

Step 1: Understanding Lambda Permissions and AWS Config

The custom AWS Config rule evaluates resources and invokes an AWS Lambda function when a compliance check is triggered. For AWS Config to invoke the Lambda function, it requires permission to do so.

Issue: The Lambda function fails to execute because AWS Config doesn't have permission to invoke it.

Action: Modify the resource-based policy of the Lambda function to grant AWS Config permission to invoke the Lambda function.

Why: Without this permission, AWS Config cannot trigger the Lambda function, which is why the evaluation fails.

https://aws.amazon.com/blogs/mt/how-to-centrally-manage-aws-iot-greengrass-devices-using-aws-systems-manager/?force_isolation=true

AWS Config’sec2-instance-profile-attachedmanaged rule checks for attached instance profiles.

Config supportsautomatic remediationvia Systems Manager Automation runbooks.

This provides continuous compliance with minimal operational overhead.

EventBridge and Lambda (A) require custom coding and risk missing existing instances.

StartInstances (C) does not cover RunInstances and new instances.

IAM-role managed policy check (D) does not check instance profile attachments.

https://docs.aws.amazon.com/codedeploy/latest/userguide/reference-appspec-file-structure-hooks.html

You need to understand how SCP inheritance works in AWS. The way it works for Deny policies is different that allow policies.

Allow polices are passing down to children ONLY if they don't have an allow policy.

Deny policies always pass down to children.

That's why there is always an SCP set to the Root to allow everything by default. If you limit this policy, the whole organization will be limited, not matter what other policies are saying for the other OUs. So it's not A. It's not D because it restricts the wrong OU.

The correct answer is A.

A comprehensive and detailed explanation is:

Option A is correct because it meets the requirements with the most operational efficiency. Updating the ecommerce application to emit a JSON object to a CloudWatch log group for each processed transaction is a simple and cost-effective way to collect the data needed for the dashboard. Using CloudWatch Logs Insights to query the log group and to visualize the results in a pie chart format is also a convenient and integrated solution that leverages the existing CloudWatch dashboards. Attaching the results to the desired CloudWatch dashboard is straightforward and does not require any additional steps or services.

Option B is incorrect because it introduces unnecessary complexity and cost. Updating the ecommerce application to emit a JSON object to an Amazon S3 bucket for each processed transaction is a valid way to store the data, but it requires creating and managing an S3 bucket and its permissions. Using Amazon Athena to query the S3 bucket and to visualize the results in a pie chart format is also a valid way to analyze the data, but it incurs charges based on the amount of data scanned by each query. Exporting the results from Athena and attaching them to the desired CloudWatch dashboard is also an extra step that adds more overhead and latency.

Option C is incorrect because it uses AWS X-Ray for an inappropriate purpose. Updating the ecommerce application to use AWS X-Ray for instrumentation is a good practice for monitoring and tracing distributed applications, but it is not designed for aggregating product transaction details. Creating a new X-Ray subsegment and adding an annotation for each processed transaction is possible, but it would clutter the X-Ray service map and make it harder to debug performance issues. Using X-Ray traces to query the data and to visualize the results in a pie chart format is also possible, but it would require custom code and logic that are not supported by X-Ray natively. Attaching the results to the desired CloudWatch dashboard is also not supported by X-Ray directly, and would require additional steps or services.

Option D is incorrect because it introduces unnecessary complexity and cost. Updating the ecommerce application to emit a JSON object to a CloudWatch log group for each processed transaction is a simple and cost-effective way to collect the data needed for the dashboard, as in option A. However, creating an AWS Lambda function to aggregate and write the results to Amazon DynamoDB is redundant, as CloudWatch Logs Insights can already perform aggregation queries on log data. Creating a Lambda subscription filter for the log file is also redundant, as CloudWatch Logs Insights can already access log data directly. Attaching the results to the desired CloudWatch dashboard would also require additional steps or services, as DynamoDB does not support native integration with CloudWatch dashboards.

The best solution is to use S3 Object Lambda1, which allows you to add your own code to S3 GET, LIST, and HEAD requests to modify and process data as it is returned to an application2. This way, you can redact the data differently for each application without creating and storing multiple copies of the data or running proxies.

The other solutions are less efficient or scalable because they require replicating the data to multiple buckets, streaming the data through Kinesis, or storing the data in S3 access points.

 1: Amazon S3 Features | Object Lambda | AWS 2: Transforming objects with S3 Object Lambda - Amazon Simple Storage Service