Pass the Amazon Web Services AWS Certified Professional DOP-C02 Questions and answers with CertsForce

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.

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/

Action-level states in events

Action state Description

STARTED The action is currently running.

SUCCEEDED The action was completed successfully.

FAILED For Approval actions, the FAILED state means the action was either rejected by the reviewer or failed due to an incorrect action configuration.

CANCELED The action was canceled because the pipeline structure was updated.

EKS control plane nodes are fully managed by AWS and are not accessible for configuration, SSH, SSM commands, or custom workloads. Therefore, any prefetching of container images must occur on the worker nodes (EC2 instances in the node groups) because those are the machines that pull and cache container images before running pods.

Option C is the only solution that aligns with AWS architecture:

It creates an IAM role allowing Systems Manager Run Command to execute on EC2 worker nodes.

It uses node group tags to target the correct nodes dynamically.

State Manager runs the prefetch script whenever triggered by the EventBridge automation — ensuring newly added nodes pre-pull images and reduce cold-start latency to seconds.

Options A and D are invalid because you cannot run Systems Manager commands on EKS control plane nodes; AWS manages them and does not expose them. Option B incorrectly targets node groups based on machine size, which does not provide reliable node identification or filtering.

Thus, Option C provides the correct, scalable, and supported method for prefetching images across all worker nodes.

The root cause of the problem is that the RDS PostgreSQL instance is exceeding its database connection limit due to high volumes of concurrent GET requests. The most efficient and lowest-effort remediation is to reduce the number of connections that reach the database and serve as many reads as possible from a cache layer. Option B accomplishes both goals with minimal changes to the existing architecture.

First, RDS Proxy manages database connections efficiently by pooling and reusing them. This reduces connection churn and prevents the Lambda function from opening excessive concurrent connections during traffic spikes. RDS Proxy is natively integrated with RDS and requires only minor configuration changes without altering application logic.

Second, enabling API Gateway caching allows caching GET responses directly at the API layer. Because over 90% of requests are GET operations, enabling caching drastically reduces traffic hitting both Lambda and RDS, significantly improving performance and lowering connection pressure.

Option C also improves read scaling, but adding ElastiCache introduces additional infrastructure and requires modifying application code to query Redis before querying the database. This is more complex than enabling API Gateway caching. Options A and D require full database migration, which is far more labor-intensive.

Option B is the simplest, most effective, and most aligned with AWS best practices for reducing RDS connection saturation.

The requirements describe a classic attribute-based access control (ABAC) model using AWS IAM. The company already has consistent tagging across users and resources, which is ideal for ABAC and least-privilege enforcement at scale.

First, Option A is required because IAM roles should represent job functions (developer vs. database administrator) and be scoped per project. Creating separate roles per project and job function allows permissions to be isolated cleanly and prevents cross-project access.

Second, Option B enforces project-level isolation at the policy level . By modifying the existing job-function policies to include a StringEquals condition that compares aws:ResourceTag/Project with aws:PrincipalTag/Project, access is automatically limited to only those resources that belong to the same project as the user. This is an AWS-recommended ABAC pattern and avoids policy sprawl.

Third, Option C is required so users can assume roles only when their tags match the role’s tags . An IAM policy attached to users that allows sts:AssumeRole only if both project and job function tags match ensures users cannot assume roles outside their assigned scope.

Options D, E, and F either misapply policies to the wrong entities, misuse tagging on policies (which IAM does not evaluate for authorization), or introduce unnecessary group-based management that conflicts with the ABAC design.

Therefore, A, B, and C together provide least privilege, project isolation, and scalable access control with minimal ongoing administration.

The constructs are TypeScript-based CDK libraries, so publishing production-qualified versions as npm packages is the clean release model. CodeArtifact is designed for storing and sharing software packages, including npm packages, and supports package version events. AWS documentation states that CodeArtifact creates EventBridge events when package versions are created, modified, or deleted, and AWS provides a pattern to start CodePipeline when a CodeArtifact package version changes. This satisfies semantic versioning and immediately triggers dependent application pipelines when a qualified release is published. Option A tracks a branch instead of production-qualified semantic releases. Option C introduces Git submodule overhead and delayed scheduled execution. Option D authenticates to CodeArtifact during builds but does not automatically notify or trigger all consuming application pipelines.

================

Amazon FSx for NetApp ONTAP provides NetApp ONTAP features in AWS, including SnapMirror replication and storage efficiencies like deduplication and compression. Create FSx for ONTAP in each Region and use SnapMirror from on-prem ONTAP to each Region for efficient, incremental replication. Regions can serve data independently of on-prem availability once replicated.

The operating system (Amazon Linux) uses yum for package management. To use both the default and a custom repository, the simplest method is to add the custom repository configuration file under /etc/yum.repos.d/ using the yum-config-manager CLI, and enable it. This allows the system to access both repositories during patching automatically.

This approach requires minimal setup and effort and leverages the existing yum patching mechanisms.

Option A and D involve managing multiple patch baselines in Systems Manager Patch Manager, which does not natively support custom repositories — it relies on the underlying OS package manager.

Option B (Direct Connect) is not necessary unless network connectivity is an issue.

Thus, option C meets the requirement with the least effort.