Free Amazon Web Services DOP-C02 Practice Exam with Questions & Answers | Set: 4

To prevent developers from unintentionally attaching an Elastic IP address to an Amazon EC2 instance in production, the best approach is to use IAM policies and AWS Config rules. By attaching an IAM policy that denies the associate-address permission to the developers’ IAM group, you ensure that developers cannot perform this action. Additionally, creating a custom AWS Config rule to check for Elastic IP addresses associated with instances tagged as production provides ongoing monitoring. If the rule detects an Elastic IP address, it can trigger an alert to notify the security team. This method is proactive and enforces the necessary permissions while also providing a mechanism for detection and notification. References: from Amazon DevOps sources

When using the awslogs log driver with Amazon ECS on EC2, CloudWatch Logs permissions must be granted to the ECS container instance IAM role , not the task definition or infrastructure role. The ECS agent running on the EC2 instances is responsible for creating log streams and pushing log events to Amazon CloudWatch Logs on behalf of the containers.

In this scenario, the task definition is correctly configured to automatically create the log group (awslogs-create-group: true) and send logs to the specified Region. However, the error occurs because the EC2 container instances do not have sufficient IAM permissions to perform the required CloudWatch Logs API calls. As a result, ECS cannot place the task, and it reports that no container instance meets the requirements.

According to AWS documentation, the container instance IAM role must include the following permissions when using the awslogs driver:

logs:CreateLogStream

logs:PutLogEvents

(and, when creating log groups automatically) logs:CreateLogGroup

Option C correctly addresses the root cause by updating the container instance IAM role . Option A is incorrect because the ECS infrastructure or service role is not used to write logs. Option B is unrelated to permissions and does not resolve the issue. Option D would fail because the log group does not already exist, causing task startup to fail.

Therefore, modifying the container instance IAM role is the correct solution.

The correct answer is B and E. Configuring a higher provisioned concurrency for the Lambda functions will ensure that the functions are ready to respond to the initial burst of requests without any cold start latency. Using Amazon RDS Proxy to create a proxy for the Aurora database will enable the Lambda functions to reuse existing database connections and reduce the overhead of establishing new ones. This will also improve the scalability and availability of the database by managing the connection pool size and handling failovers. Option A is incorrect because reserved concurrency only limits the number of concurrent executions for a function, not pre-warms them. Option C is incorrect because converting the DB cluster to an Aurora global database will not address the issue of database connection latency, and may introduce additional costs and complexity. Option D is incorrect because moving the code blocks that initialize database connections into the function handlers will not improve the performance or scalability of the Lambda functions, and may actually worsen the cold start latency. References:

AWS Lambda Provisioned Concurrency

Using Amazon RDS Proxy with AWS Lambda

Certified DevOps Engineer - Professional (DOP-C02) Study Guide (page 173)

The engineer should make the following changes to achieve a policy of least permission:

A: Add a condition to ensure that the principal making the request is an AWS Lambda function. This ensures that only Lambda functions can execute this policy.

B: Narrow down the resources by specifying the ARN of EC2 instances instead of allowing all resources. This ensures that the policy only affects EC2 instances.

D: Add a condition to ensure that this policy only applies to EC2 instances tagged with “Environment: NonProduction”. This ensures that production environments are not affected by this policy.

AWS Identity and Access Management (IAM) - AWS Documentation

Certified DevOps Engineer - Professional (DOP-C02) Study Guide (page 179)

Systems Manager State Manager with Automation documents allows running scripts sequentially and reliably with built-in retry and status tracking.

Using EventBridge with SNS for notifications leverages managed services with minimal custom development.

Using SES (Options B, D) requires more setup and custom logic for email formatting and sending.

Lambda (Options C, D) can run scripts but might have limitations on execution time and complexity compared to Systems Manager Automation.

These solutions will meet the requirement because they will prevent the loss of notification messages in the future. An Amazon SQS queue is a service that provides a reliable, scalable, and secure message queue for asynchronous communication between distributed components. You can use an SQS queue to buffer messages from an SNS topic and ensure that they are delivered and processed by a Lambda function, even if the function or the database is temporarily unavailable.

Option C will configure an SQS dead-letter queue for the SNS topic. A dead-letter queue is a queue that receives messages that could not be delivered to any subscriber after a specified number of retries. You can use a dead-letter queue to store and analyze failed messages, or to reprocess them later. This way, you can avoid losing messages that could not be delivered to the Lambda function due to network errors, throttling, or other issues.

Option D will subscribe an SQS queue to the SNS topic and configure the Lambda function to process messages from the SQS queue. This will decouple the SNS topic from the Lambda function and provide more flexibility and control over the message delivery and processing. You can use an SQS queue to store messages from the SNS topic until they are ready to be processed by the Lambda function, and also to retry processing in case of failures. This way, you can avoid losing messages that could not be processed by the Lambda function due to database errors, timeouts, or other issues.

The most cost-effective solution to meet the RTO and RPO requirements is option B. This option involves copying the CloudFormation templates to an Amazon S3 bucket in the DR Region, configuring Aurora automated backup Cross-Region Replication, and configuring ECR Cross-Region Replication. In the event of a disaster, the CloudFormation template with the most recent Aurora snapshot and the Docker image from the local ECR repository can be used to launch a new CloudFormation stack in the DR Region. This approach avoids the need to build Docker images from the Dockerfile, which can sometimes take more than 2 hours, thus meeting the RTO requirement. Additionally, the use of automated backups and replication ensures that the RPO of 8 hours is met.

AWS Documentation on Disaster Recovery: Plan for Disaster Recovery (DR) - Reliability Pillar

AWS Blog on Establishing RPO and RTO Targets: Establishing RPO and RTO Targets for Cloud Applications

AWS Documentation on ECR Cross-Region Replication: Amazon ECR Cross-Region Replication

AWS Documentation on Aurora Cross-Region Replication: Replicating Amazon Aurora DB Clusters Across AWS Regions

https://docs.aws.amazon.com/codedeploy/latest/userguide/troubleshooting-deployments.html#troubleshooting-skipped-lifecycle-events

https://aws.amazon.com/blogs/security/automatically-block-suspicious-traffic-with-aws-network-firewall-and-amazon-guardduty/