Free Amazon Web Services DOP-C02 Practice Exam with Questions & Answers | Set: 6

A: If the ApproximateAgeOfOldestMessages indicate that orders are remaining in the SQS queue for longer than expected, the reserved concurrency limit may be set too small to keep up with the number of orders entering the queue and is being throttled. D: The DynamoDB table is using Auto Scaling. With Auto Scaling, you create a scaling policy that specifies whether you want to scale read capacity or write capacity (or both), and the minimum and maximum provisioned capacity unit settings for the table. The ThottledWriteRequests metric will indicate if there is a throttling issue on the DynamoDB table, which can be resolved by increasing the maximum write capacity units for the table ' s Auto Scaling policy. https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/AutoScaling.html

ECS container instances must have an IAM role that trusts ECS (the ecs-tasks.amazonaws.com principal) to assume permissions for CloudWatch logging. Without this trust relationship, ECS cannot push logs even if the permissions are present.

(A) When Docker communicates with an Amazon Elastic Container Registry (ECR) repository, it requires authentication. You can authenticate your Docker client to the Amazon ECR registry with the help of the AWS CLI (Command Line Interface). Specifically, you can use the " aws ecr get-login-password " command to get an authorization token and then use Docker ' s " docker login " command with that token to authenticate to the registry. You would need to perform these steps in your buildspec.yml file before attempting to push or pull images from/to the ECR repository.

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/dynamic-references.html

AWS CodeDeploy supports deployment of both Lambda functions and ECS services with native integration.

It provides deployment strategies such as canary deployments for Lambda and blue/green deployments for ECS, which minimize downtime and risk.

CodeDeploy automates the deployment process, which reduces manual effort and improves consistency.

AWS Systems Manager Parameter Store is a simple, managed solution for storing configuration data securely and at scale.

Option A uses CloudFormation stack updates with resource replacement, which can cause downtime and is less flexible for minimizing downtime compared to CodeDeploy’s deployment strategies.

Option C adds unnecessary orchestration complexity with Step Functions and cross-region deployments, increasing deployment effort.

Option D’s all-at-once deployments for Lambda and rolling updates for ECS do not minimize downtime as effectively as canary and blue/green strategies. Hence, option B meets the requirement with the least deployment effort and maximizes automation and availability.

Reference from AWS Official Documentation:

Deploying Lambda Functions with CodeDeploy: " AWS CodeDeploy supports canary and linear deployment strategies for AWS Lambda functions to reduce deployment risk. " (CodeDeploy Lambda Deployment)

Deploying ECS Services with CodeDeploy Blue/Green: " CodeDeploy supports blue/green deployments for ECS to minimize downtime during service updates. " (CodeDeploy ECS Blue/Green)

AWS Systems Manager Parameter Store: " Parameter Store provides secure, hierarchical storage for configuration data management. " (Systems Manager Parameter Store)

CodeCommit generates events in CloudWatch, CloudWatch triggers the CodeBuild https://aws.amazon.com/es/blogs/devops/complete-ci-cd-with-aws-codecommit-aws-codebuild-aws-codedeploy-and-aws-codepipeline/

Exporting logs using CloudWatch subscription filters + Firehose → S3 enables long-term archival. S3 lifecycle transitions optimize cost with Standard-IA (low-latency) and Deep Archive (long-term retention). This matches AWS best practice for cost-effective log retention.

To meet the requirements of failover and disaster recovery, the company should use the following deployment strategies:

Create an Amazon Aurora global database in two AWS Regions as the data store. In the event of a failure, promote the secondary Region to the primary for the application. Update the application to use the Aurora cluster endpoint in the secondary Region. This strategy can provide a low RPO and RTO for the data, as Aurora global database replicates data with minimal latency across Regions and allows fast and easy failover12. The company can use the Amazon Aurora cluster endpoint to connect to the current primary DB cluster without needing to change any application code1.

Set up the application in two AWS Regions. Configure AWS Global Accelerator to point to Application Load Balancers (ALBs) in both Regions. Add both ALBs to a single endpoint group. Use health checks and Auto Scaling groups in each Region. This strategy can provide high availability and performance for the application, as AWS Global Accelerator uses the AWS global network to route traffic to the closest healthy endpoint3. The company can also use static IP addresses that are assigned by Global Accelerator as a fixed entry point for their application1. By using health checks and Auto Scaling groups, the company can ensure that their application can scale up or down based on demand and handle any instance failures4.

The other options are incorrect because:

Creating an Amazon Aurora Single-AZ cluster in multiple AWS Regions as the data store would not provide a fast failover or disaster recovery solution, as the company would need to manually restore data from backups or snapshots in another Region in case of a failure.

Creating an Amazon Aurora cluster in multiple AWS Regions as the data store and using a Network Load Balancer to balance the database traffic in different Regions would not work, as Network Load Balancers do not support cross-Region routing. Moreover, this strategy would not provide a consistent view of the data across Regions, as Aurora clusters do not replicate data automatically between Regions unless they are part of a global database.

Setting up the application in two AWS Regions and using Amazon Route 53 failover routing that points to Application Load Balancers in both Regions would not provide a low RTO, as Route 53 failover routing relies on DNS resolution, which can take time to propagate changes across different DNS servers and clients. Moreover, this strategy would not provide deterministic routing, as Route 53 failover routing depends on DNS caching behavior, which can vary depending on different factors.

The correct answer is C.

Option A is incorrect because creating a new SCP that has two statements, one that allows access to the new range of IP addresses for all the S3 buckets and one that denies access to the old range of IP addresses for all the S3 buckets, is not a valid solution. SCPs are not resource-based policies, and they cannot specify the S3 buckets or the IP addresses as resources or conditions. SCPs can only control the actions that can be performed by the principals in the organization, not the access to specific resources. Moreover, setting a permissions boundary for the OrganizationAccountAccessRole role in the two OUs to deny access to the S3 buckets is not sufficient to revoke the permissions of the two OUs, as there might be other roles or users in those OUs that can still access the S3 buckets.

Option B is incorrect because creating a new SCP that has a statement that allows only the new range of IP addresses to access the S3 buckets is not a valid solution, for the same reason as option A. SCPs are not resource-based policies, and they cannot specify the S3 buckets or the IP addresses as resources or conditions. Creating another SCP that denies access to the S3 buckets and attaching it to the two OUs is also not a valid solution, as SCPs cannot specify the S3 buckets as resources either.

Option C is correct because it meets both requirements of changing the range of IP addresses that have permission to access the contents of the S3 buckets and revoking the permissions of two OUs in the company. On all the S3 buckets, configuring resource-based policies that allow only the new range of IP addresses to access the S3 buckets is a valid way to update the IP address ranges, as resource-based policies can specify both resources and conditions. Creating a new SCP that denies access to the S3 buckets and attaching it to the two OUs is also a valid way to revoke the permissions of those OUs, as SCPs can deny actions such as s3:PutObject or s3:GetObject on any resource.

Option D is incorrect because setting a permissions boundary for the OrganizationAccountAccessRole role in the two OUs to deny access to the S3 buckets is not sufficient to revoke the permissions of the two OUs, as there might be other roles or users in those OUs that can still access the S3 buckets. A permissions boundary is a policy that defines the maximum permissions that an IAM entity can have. However, it does not revoke any existing permissions that are granted by other policies.