DOP-C02 · Question #282
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…
The correct answer is B. For the critical data, modify the existing Auto Scaling group. Create a warm pool instance in the D. For the noncritical data, create a second Auto Scaling group that uses a launch template. 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.
Question
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.)
Options
- AFor the critical data, modify the existing Auto Scaling group. Create a warm pool instance in the
- BFor the critical data, modify the existing Auto Scaling group. Create a warm pool instance in the
- CFor the critical data, modify the existing Auto Scaling group. Create a lifecycle hook to ensure that
- DFor the noncritical data, create a second Auto Scaling group that uses a launch template.
- EFor the noncritical data, create a second Auto Scaling group. Choose the predefined memory
How the community answered
(47 responses)- A11% (5)
- B60% (28)
- C4% (2)
- E26% (12)
Explanation
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.
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