Exam AWS Certified DevOps Engineer - Professional DOP-C02 topic 1 question 267 discussion

Exam question from Amazon's AWS Certified DevOps Engineer - Professional DOP-C02

Question #: 267
Topic #: 1

[All AWS Certified DevOps Engineer - Professional DOP-C02 Questions]

A company's application uses a fleet of Amazon EC2 On-Demand Instances to analyze and process data. The EC2 instances are in an Auto Scaling group. The Auto Scaling group is a target group for an Application Load Balancer (ALB). The application analyzes critical data that cannot tolerate interruption. The application also analyzes noncritical data that can withstand interruption.

The critical data analysis requires quick scalability in response to real-time application demand. The noncritical data analysis involves memory consumption. A DevOps engineer must implement a solution that reduces scale-out latency for the critical data. The solution also must process the noncritical data.

Which combination of steps will meet these requirements? (Choose two.)

A. For the critical data, modify the existing Auto Scaling group. Create a warm pool instance in the stopped state. Define the warm pool size. Create a new version of the launch template that has detailed monitoring enabled. Use Spot Instances.
B. For the critical data, modify the existing Auto Scaling group. Create a warm pool instance in the stopped state. Define the warm pool size. Create a new version of the launch template that has detailed monitoring enabled. Use On-Demand Instances.
C. For the critical data, modify the existing Auto Scaling group. Create a lifecycle hook to ensure that bootstrap scripts are completed successfully. Ensure that the application on the instances is ready to accept traffic before the instances are registered. Create a new version of the launch template that has detailed monitoring enabled.
D. For the noncritical data, create a second Auto Scaling group that uses a launch template. Configure the launch template to install the unified Amazon CloudWatch agent and to configure the CloudWatch agent with a custom memory utilization metric. Use Spot Instances. Add the new Auto Scaling group as the target group for the ALB. Modify the application to use two target groups for critical data and noncritical data.
E. For the noncritical data, create a second Auto Scaling group. Choose the predefined memory utilization metric type for the target tracking scaling policy. Use Spot Instances. Add the new Auto Scaling group as the target group for the ALB. Modify the application to use two target groups for critical data and noncritical data.

Show Suggested Answer

Suggested Answer: BD 🗳️

by xdkonorek2 at July 6, 2024, 10:41 a.m.

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Comments

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trungtd

Highly Voted 11 months, 2 weeks ago

Selected Answer: BD

AWS Auto Scaling does not provide a predefined memory utilization metric type

upvoted 5 times

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Rs123x

Most Recent 2 months ago

Selected Answer: CD

This combination ensures quick scaling and uninterrupted processing for critical data and cost-efficient, memory-optimized scaling for noncritical data.

upvoted 1 times

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jamesf

11 months ago

Selected Answer: BD

Option B (For critical data): Creates a warm pool and ensures quick scaling with On-Demand Instances, addressing the need for low latency in scaling. Option D (For noncritical data): Uses Spot Instances with memory-based scaling policies to handle noncritical data efficiently.

upvoted 4 times

jamesf

10 months, 3 weeks ago

For D, Spot Instance, Using Cloudwatch with Custom Memory Utilization Metric https://aws.amazon.com/blogs/mt/create-amazon-ec2-auto-scaling-policy-memory-utilization-metric-linux/ Not E as Auto Scaling does not provide predefined memory utilization.

upvoted 1 times

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tgv

11 months, 2 weeks ago

Selected Answer: BD

---> B D

upvoted 4 times

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TEC1

11 months, 3 weeks ago

Selected Answer: BE

On-Demand Instances: For critical data that cannot tolerate interruption, On-Demand Instances are reliable and provide the required stability without the risk of termination Spot Instances: Utilising Spot Instances for noncritical data processing can significantly reduce costs since these workloads can tolerate interruptions. This combination ensures that the critical data analysis benefits from reduced scale-out latency and reliability, while noncritical data processing leverages cost-effective Spot Instances and is scaled based on memory usage.

upvoted 1 times

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