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A company needs a strategy for failover and disaster recovery of its data and application. The application uses a MySQL database and Amazon EC2 instances. The company requires a maximum RPO of 2 hours and a maximum RTO of 10 minutes for its data and application at all times.
Which combination of deployment strategies will meet these requirements? (Select TWO.)
A. 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.
B. 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.
C. Create an Amazon Aurora Single-AZ cluster in multiple AWS Regions as the data store. Use Aurora's automatic recovery capabilities in the event of a disaster.
D. Set up the application in two AWS Regions. Use Amazon Route 53 failover routing that points to Application Load Balancers in both Regions. Use health checks and Auto Scaling groups in each Region.
E. Create an Amazon Aurora cluster in multiple AWS Regions as the data store. Use a Network Load Balancer to balance the database traffic in different Regions.
Answer: A,B
Explanation:
Verified answer: B and E
Short 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.
NEW QUESTION # 404
An AWS CodePipeline pipeline has implemented a code release process. The pipeline is integrated with AWS CodeDeploy to deploy versions of an application to multiple Amazon EC2 instances for each CodePipeline stage.
During a recent deployment the pipeline failed due to a CodeDeploy issue. The DevOps team wants to improve monitoring and notifications during deployment to decrease resolution times.
What should the DevOps engineer do to create notifications. When issues are discovered?
A. Implement Amazon EventBridge for CodePipeline and CodeDeploy create an AWS Lambda function to evaluate code deployment issues, and create an Amazon Simple Notification Service (Amazon SNS) topic to notify stakeholders of deployment issues.
B. Implement Amazon EventBridge for CodePipeline and CodeDeploy create an Amazon. Inspector assessment target to evaluate code deployment issues and create an Amazon Simple. Notification Service (Amazon SNS) topic to notify stakeholders of deployment issues.
C. Implement AWS CloudTrail to record CodePipeline and CodeDeploy API call information create an AWS Lambda function to evaluate code deployment issues and create an Amazon Simple Notification Service (Amazon SNS) topic to notify stakeholders of deployment issues.
D. Implement Amazon CloudWatch Logs for CodePipeline and CodeDeploy create an AWS Config rule to evaluate code deployment issues, and create an Amazon Simple Notification Service (Amazon SNS) topic to notify stakeholders of deployment issues.
Answer: A
Explanation:
Explanation
AWS CloudWatch Events can be used to monitor events across different AWS resources, and a CloudWatch Event Rule can be created to trigger an AWS Lambda function when a deployment issue is detected in the pipeline. The Lambda function can then evaluate the issue and send a notification to the appropriate stakeholders through an Amazon SNS topic. This approach allows for real-time notifications and faster resolution times.
NEW QUESTION # 405
A company is using an Amazon Aurora cluster as the data store for its application. The Aurora cluster is configured with a single DB instance. The application performs read and write operations on the database by using the cluster's instance endpoint.
The company has scheduled an update to be applied to the cluster during an upcoming maintenance window.
The cluster must remain available with the least possible interruption during the maintenance window.
What should a DevOps engineer do to meet these requirements?
A. Add a reader instance to the Aurora cluster. Create a custom ANY endpoint for the cluster. Update the application to use the Aurora cluster's custom ANY endpoint for read and write operations.
B. Add a reader instance to the Aurora cluster. Update the application to use the Aurora cluster endpoint for write operations. Update the Aurora cluster's reader endpoint for reads.
C. Turn on the Multi-AZ option on the Aurora cluster. Update the application to use the Aurora cluster endpoint for write operations. Update the Aurora cluster's reader endpoint for reads.
D. Turn on the Multi-AZ option on the Aurora cluster. Create a custom ANY endpoint for the cluster.Update the application to use the Aurora cluster's custom ANY endpoint for read and write operations.
Answer: C
Explanation:
Explanation
To meet the requirements, the DevOps engineer should do the following:
Turn on the Multi-AZ option on the Aurora cluster.
Update the application to use the Aurora cluster endpoint for write operations.
Update the Aurora cluster's reader endpoint for reads.
Turning on the Multi-AZ option will create a replica of the database in a different Availability Zone. This will ensure that the database remains available even if one of the Availability Zones is unavailable.
Updating the application to use the Aurora cluster endpoint for write operations will ensure that all writes are sent to both the primary and replica databases. This will ensure that the data is always consistent.
Updating the Aurora cluster's reader endpoint for reads will allow the application to read data from the replica database. This will improve the performance of the application during the maintenance window.
NEW QUESTION # 406
A company recently migrated its application to an Amazon Elastic Kubernetes Service (Amazon EKS) cluster that uses Amazon EC2 instances. The company configured the application to automatically scale based on CPU utilization.
The application produces memory errors when it experiences heavy loads. The application also does not scale out enough to handle the increased load. The company needs to collect and analyze memory metrics for the application over time.
Which combination of steps will meet these requirements? (Select THREE.)
A. Analyze the node_memory_utilization Amazon CloudWatch metric in the Containerlnsights namespace by using the ClusterName dimension.
B. Collect performance metrics by deploying the unified Amazon CloudWatch agent to the existing EC2 instances in the cluster. Add the agent to the AMI for any new EC2 instances that are added to the cluster.
C. Attach the Cloud WatchAgentServer Pol icy managed 1AM policy to the 1AM instance profile that the cluster uses.
D. Analyze the pod_memory_utilization Amazon CloudWatch metric in the Containerlnsights namespace by using the Service dimension.
E. Attach the Cloud WatchAgentServer Pol icy managed 1AM policy to a service account role for the cluster.
F. Collect performance logs by deploying the AWS Distro for OpenTelemetry collector as a DaemonSet.
Answer: B,C,D
Explanation:
* Step 1: Attaching the CloudWatchAgentServerPolicy to the IAM Role
The CloudWatch agent needs permissions to collect and send metrics, including memory metrics, to Amazon CloudWatch. You can attach the CloudWatchAgentServerPolicy managed IAM policy to the IAM instance profile or service account role to grant these permissions.
Action: Attach the CloudWatchAgentServerPolicy managed IAM policy to the IAM instance profile that the EKS cluster uses.
Why: This ensures the CloudWatch agent has the necessary permissions to collect memory metrics.
Reference:
This corresponds to Option A: Attach the CloudWatchAgentServerPolicy managed IAM policy to the IAM instance profile that the cluster uses.
* Step 2: Deploying the CloudWatch Agent to EC2 Instances
To collect memory metrics from the EC2 instances running in the EKS cluster, the CloudWatch agent needs to be deployed on these instances. The agent collects system-level metrics, including memory usage.
Action: Deploy the unified Amazon CloudWatch agent to the existing EC2 instances in the EKS cluster. Update the Amazon Machine Image (AMI) for future instances to include the CloudWatch agent.
Why: The CloudWatch agent allows you to collect detailed memory metrics from the EC2 instances, which is not enabled by default.
This corresponds to Option C: Collect performance metrics by deploying the unified Amazon CloudWatch agent to the existing EC2 instances in the cluster. Add the agent to the AMI for any new EC2 instances that are added to the cluster.
* Step 3: Analyzing Memory Metrics Using Container Insights
After collecting the memory metrics, you can analyze them using the pod_memory_utilization metric in Amazon CloudWatch Container Insights. This metric provides visibility into the memory usage of the containers (pods) in the EKS cluster.
Action: Analyze the pod_memory_utilization CloudWatch metric in the Container Insights namespace by using the Service dimension.
Why: This provides detailed insights into memory usage at the container level, which helps diagnose memory-related issues.
This corresponds to Option E: Analyze the pod_memory_utilization Amazon CloudWatch metric in the Container Insights namespace by using the Service dimension.
NEW QUESTION # 407
A company is migrating its web application to AWS. The application uses WebSocket connections for real- time updates and requires sticky sessions.
A DevOps engineer must implement a highly available architecture for the application. The application must be accessible to users worldwide with the least possible latency.
Which solution will meet these requirements with the LEAST operational overhead?
A. Deploy an Application Load Balancer (ALB) for HTTP traffic. Deploy a Network Load Balancer (NLB) in each of the company's AWS Regions for WebSocket connections. Enable sticky sessions on the ALB. Configure the ALB to forward requests to the NLB.
B. Deploy an Application Load Balancer (ALB). Deploy another ALB in a different AWS Region. Enable cross-zone load balancing and sticky sessions on the ALBs. Integrate the ALBs with Amazon Route 53 latency-based routing.
C. Deploy a Network Load Balancer (NLB). Deploy another NLB in a different AWS Region. Enable cross-zone load balancing and sticky sessions on the NLBs. Integrate the NLBs with Amazon Route 53 geolocation routing.
D. Deploy a Network Load Balancer (NLB) with cross-zone load balancing enabled. Configure the NLB with IP-based targets in multiple Availability Zones. Use Amazon CloudFront for global content delivery. Implement sticky sessions by using source IP address preservation on the NLB.
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
ALB natively supportsWebSocket protocolsandsticky sessionsvia target group session affinity. Deploying ALBs in multiple regions with cross-zone load balancing ensures high availability and fault tolerance.
UsingRoute 53 latency-based routingallows users worldwide to connect to the lowest latency region, minimizing delay.
NLBs do not support sticky sessions and WebSocket protocol as well as ALBs do. Combining ALBs and NLBs (Option D) increases complexity. CloudFront (Option C) does not natively support WebSocket sticky sessions.
Option A is the simplest, most effective solution meeting all requirements with least operational overhead.
References:
ALB WebSocket Support
Route 53 Latency Based Routing
NEW QUESTION # 408
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