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

To meet the requirements, the DevOps team needs to configure a monitoring solution for the VPC flow logs that can detect anomalies in network traffic over time and initiate a response to the anomaly. The DevOps team can use Amazon Kinesis Data Streams to ingest and process streaming data from CloudWatch Logs. The DevOps team can subscribe the log group to a Kinesis data stream, which will deliver log events from CloudWatch Logs to Kinesis Data Streams in near real-time. The DevOps team can then create an AWS Lambda function to detect log anomalies using machine learning or statistical methods. The Lambda function can be set as a processor for the data stream, which means that it will process each record from the stream before sending it to downstream applications or destinations. The Lambda function can also write to the default Amazon EventBridge event bus if it detects an anomaly, which will allow other AWS services or custom applications to respond to the anomaly event.

ECR pull-through cache caches images from upstream repositories for resilience.

Private repo with basic scanning ensures vulnerability detection on pushed images.

Enabling pull-through caching on a private repo combines caching and vulnerability scanning seamlessly.

Public repos do not support pull-through caching of upstream images.

Replication rules are for multi-Region replication, not upstream caching.

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. Updating the SCP in the child OU to allow all actions for Amazon EC2 will enable the DevOps engineer to launch the EC2 instance in the vendor-data account. SCPs are applied to OUs and accounts in a hierarchical manner, meaning that the SCPs attached to the parent OU are inherited by the child OU and accounts. Therefore, the SCP in the child OU overrides the SCP in the root OU and denies all actions except for DynamoDB and Lambda. By adding EC2 to the allowed actions in the child OU’s SCP, the DevOps engineer can access EC2 resources in the vendor-data account.

Option A is incorrect because attaching the AmazonEC2FullAccess IAM policy to the IAM user will not grant the user access to EC2 resources. IAM policies are evaluated after SCPs, so even if the IAM policy allows EC2 actions, the SCP will still deny them.

Option B is incorrect because creating a new SCP that allows all actions for EC2 and attaching it to the vendor-data account will not work. SCPs are not cumulative, meaning that only one SCP is applied to an account at a time. The SCP attached to the account will be the SCP attached to the OU that contains the account. Therefore, option B will not change the SCP that is applied to the vendor-data account.

Option D is incorrect because creating a new SCP that allows all actions for EC2 and attaching it to the root OU will not work. As explained earlier, the SCP in the child OU overrides the SCP in the root OU and denies all actions except for DynamoDB and Lambda. Therefore, option D will not affect the SCP that is applied to the vendor-data account.

To meet the requirements, the DevOps engineer should create an Amazon EventBridge event rule that has the default event bus as the source. The rule's event pattern should match EC2 security group creation and modification events, and it should be configured to invoke the Lambda function. This solution will allow for near real-time detection of security group rule changes and will trigger the Lambda function to remove any unrestricted rules and send email notifications to the security team.

https://repost.aws/knowledge-center/monitor-security-group-changes-ec2

https://docs.aws.amazon.com/IAM/latest/UserGuide/tutorial_cross-account-with-roles.html

To scale microservice Pods based on CPU utilization, the Kubernetes Horizontal Pod Autoscaler (HPA) uses the Kubernetes Metrics Server to monitor resource usage and automatically adjusts the number of Pods. However, scaling Pods may require additional nodes if the current node capacity is insufficient.

The Cluster Autoscaler works with EKS managed node groups to add or remove worker nodes based on pending Pod requirements and resource usage.

By deploying both HPA and Cluster Autoscaler, the system can automatically scale Pods and add nodes as necessary, ensuring efficient resource utilization and availability.

Configuring the Cluster Autoscaler with auto-discovery allows it to manage node groups without manual intervention, reducing operational effort.

Option A only scales nodes based on node CPU utilization, not Pods.

Option B uses VPA recommender mode, which only suggests resource changes and does not scale automatically.

Option C involves manual updates and is not automated scaling.Therefore, option D provides the most operationally efficient, fully automated scaling solution.

Reference from AWS Official Documentation:

Kubernetes Horizontal Pod Autoscaler:"HPA automatically scales the number of Pods based on observed CPU utilization or other metrics."(Kubernetes HPA)

Cluster Autoscaler on Amazon EKS:"The Cluster Autoscaler automatically adjusts the size of the Kubernetes cluster when there are Pods that fail to run due to insufficient resources or when nodes in the cluster are underutilized."(AWS EKS Cluster Autoscaler)

CodePipeline V2 with QUEUED mode ensures that new executions wait for the previous execution to finish, preventing overlapping runs.

Adding a trigger filter to listen for Git tags triggers the pipeline only on new tag pushes.

An EventBridge rule can detect new image pushes to ECR and start the deployment pipeline, integrating the build and deploy pipelines effectively.

SUPERSEDED mode cancels the previous run when a new one starts, which is not desired here.

Using branches instead of tags would trigger on all commits, not just releases.