DOP-C02 Exam Dumps - Amazon Web Services AWS Certified Professional Questions and Answers

(A) Use ECR replication to keep images synchronized between Regions, minimizing CI/CD pipeline changes.

(C) DynamoDB global tables allow multi-Region replication and provide local read/write access, so tasks should interact with the DynamoDB replica in their Region.

(D) Use S3 cross-Region replication or separate buckets with replication; tasks access the bucket in the same Region for latency and data sovereignty.

(B) Using DynamoDB global table but pointing tasks to only one Region reduces resilience.

(E) S3 Multi-Region Access Points are newer but add complexity and are not required for minimal changes.

(F) Managing CI/CD pipeline for multiple Regions and failover routing adds complexity beyond minimal changes.

https://docs.aws.amazon.com/cli/latest/reference/cloudformation/continue-update-rollback.html For a specified stack that is in the UPDATE_ROLLBACK_FAILED state, continues rolling it back to the UPDATE_ROLLBACK_COMPLETE state. Depending on the cause of the failure, you can manually fix the error and continue the rollback. By continuing the rollback, you can return your stack to a working state (the UPDATE_ROLLBACK_COMPLETE state), and then try to update the stack again.

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 startup delay occurs because large container images must be pulled from Amazon ECR when pods are scheduled, especially on newly added nodes that do not have cached images. To consistently reduce pod startup time to seconds, container images must be prefetched directly onto the worker nodes that run the pods, not the EKS control plane.

Amazon EKS control plane nodes are fully managed by AWS and cannot be accessed or modified using AWS Systems Manager. Therefore, any solution that attempts to run Systems Manager commands on control plane nodes is invalid. Prefetching must target the EKS worker nodes (EC2 instances in managed node groups) where container images are actually stored and used.

Option C correctly creates an IAM role that allows Amazon EventBridge to invoke AWS Systems Manager to run commands on the EKS worker nodes. By using Systems Manager State Manager associations with node tags , the automation dynamically targets both existing nodes and newly added nodes that inherit the same tags. This ensures that container images are pulled immediately when a new image is pushed to ECR or when new nodes join the cluster.

Option B incorrectly targets nodes based on machine size, which is not reliable for lifecycle automation. Options A and D incorrectly reference control plane nodes, which cannot be managed with Systems Manager.

Therefore, Option C is the correct and AWS-aligned solution to ensure fast pod startup times across all nodes.

Amazon S3 can generate server access logs that record detailed information about each request, including requester, bucket, key, operation, time, and status. These logs are written as objects to an S3 bucket. To analyze access patterns, the simplest and most serverless approach is to use Amazon Athena directly on those logs without building ingestion pipelines or databases.

Option B enables S3 server access logging and then creates an Athena external table over the log bucket. AWS provides standard log formats and even example schemas for S3 access logs. The analytics team can run ad hoc SQL queries to count the number of accesses per object per time period, filter by user, and perform aggregations, all without provisioning compute or managing databases.

Option A requires ingesting logs into Aurora, which adds ETL complexity and ongoing database management. Option C requires a Lambda function for every access event plus DB writes, which is more complex and potentially expensive at scale. Option D uses CloudWatch Logs and Managed Flink, which is more suited for streaming analytics and is significantly more complex than necessary for monthly summary reports.

Therefore, Option B provides the required analysis with the least development and operational effort.

To allow developers to create IAM users without granting excessive permissions, the correct solution is to use permissions boundaries, which AWS specifically recommends for restricting delegated administrators such as developers. A permissions boundary defines the maximum permissions that an IAM user or role can delegate.

Step C ensures that each AWS account contains a PermissionBoundaries policy defining the maximum allowed permissions that any developer-created user may receive. This prevents privilege escalation, even if the developer attaches a more powerful policy. This aligns with AWS guidance for restricting privilege escalation within multi-account environments.

Step E ensures that developers can create IAM users but only if they attach the PermissionBoundaries policy as the permissions boundary. By attaching the DeveloperBoundary policy to the CreateAndManageUsers role, developers gain the ability to create users, but they are cryptographically prevented from assigning permissions outside the boundary policy.

Meanwhile, the DevOps team (who are not restricted by the boundary) can still create IAM users with full permissions.

This combination satisfies all constraints:

DevOps team: unrestricted IAM creation

Developers: restricted IAM creation enforced by boundaries

Other users: still blocked from IAM creation by existing SCP

To meet the requirements of creating a new Organizations account structure with an appropriate SCP that supports the use of only services that are currently active in the AWS account, the company should use the following solution:

Create an SCP that allows the services that IAM Access Analyzer identifies. IAM Access Analyzer is a service that helps identify potential resource-access risks by analyzing resource-based policies in the AWS environment. IAM Access Analyzer can also generate IAM policies based on access activity in the AWS CloudTrail logs. By using IAM Access Analyzer, the company can create an SCP that grants only the permissions that are required for the application to run, and denies all other services. This way, the company can enforce the use of only approved AWS services and reduce the risk of unauthorized access12

Create an OU for the account. Move the account into the new OU. An OU is a container for accounts within an organization that enables you to group accounts that have similar business or security requirements. By creating an OU for the account, the company can apply policies and manage settings for the account as a group. The company should move the account into the new OU to make it subject to the policies attached to the OU3

Attach the new SCP to the new OU. Detach the default FullAWSAccess SCP from the new OU. An SCP is a type of policy that specifies the maximum permissions for an organization or organizational unit (OU). By attaching the new SCP to the new OU, the company can restrict the services that are available to all accounts in that OU, including the account that runs the application. The company should also detach the default FullAWSAccess SCP from the new OU, because this policy allows all actions on all AWS services and might override or conflict with the new SCP45

The other options are not correct because they do not meet the requirements or follow best practices. Creating an SCP that denies the services that IAM Access Analyzer identifies is not a good option because it might not cover all possible services that are not approved or required for the application. A deny policy is also more difficult to maintain and update than an allow policy. Creating an SCP that allows the services that IAM Access Analyzer identifies and attaching it to the organization’s root is not a good option because it might affect other accounts and OUs in the organization that have different service requirements or approvals. Creating an SCP that allows the services that IAM Access Analyzer identifies and attaching it to the management account is not a valid option because SCPs cannot be attached directly to accounts, only to OUs or roots.

1: Using AWS Identity and Access Management Access Analyzer - AWS Identity and Access Management

2: Generate a policy based on access activity - AWS Identity and Access Management

3: Organizing your accounts into OUs - AWS Organizations

4: Service control policies - AWS Organizations

5: How SCPs work - AWS Organizations

service catalog uses stacksets and can enforce tag and restrict resources AWS Customer case with tag enforcement https://aws.amazon.com/ko/blogs/apn/enforce-centralized-tag-compliance-using-aws-service-catalog-amazon-dynamodb-aws-lambda-and-amazon-cloudwatch-events/ And Youtube video showing how to restrict resources per user with portfolio https://www.youtube.com/watch?v=LzvhTcqqyog