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Question 802 - SAA-C03 discussion

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A company has a three-tier web application that processes orders from customers. The web tier consists of Amazon EC2 instances behind an Application Load Balancer. The processing tier consists of EC2 instances. The company decoupled the web tier and processing tier by using Amazon Simple Queue Service (Amazon SQS). The storage layer uses Amazon DynamoDB.

At peak times some users report order processing delays and halts. The company has noticed that during these delays, the EC2 instances are running at 100% CPU usage, and the SQS queue fills up. The peak times are variable and unpredictable.

The company needs to improve the performance of the application

Which solution will meet these requirements?

A.
Use scheduled scaling for Amazon EC2 Auto Scaling to scale out the processing tier instances for the duration of peak usage times. Use the CPU Utilization metric to determine when to scale.
Answers
A.
Use scheduled scaling for Amazon EC2 Auto Scaling to scale out the processing tier instances for the duration of peak usage times. Use the CPU Utilization metric to determine when to scale.
B.
Use Amazon ElastiCache for Redis in front of the DynamoDB backend tier. Use target utilization as a metric to determine when to scale.
Answers
B.
Use Amazon ElastiCache for Redis in front of the DynamoDB backend tier. Use target utilization as a metric to determine when to scale.
C.
Add an Amazon CloudFront distribution to cache the responses for the web tier. Use HTTP latency as a metric to determine when to scale.
Answers
C.
Add an Amazon CloudFront distribution to cache the responses for the web tier. Use HTTP latency as a metric to determine when to scale.
D.
Use an Amazon EC2 Auto Scaling target tracking policy to scale out the processing tier instances. Use the ApproximateNumberOfMessages attribute to determine when to scale.
Answers
D.
Use an Amazon EC2 Auto Scaling target tracking policy to scale out the processing tier instances. Use the ApproximateNumberOfMessages attribute to determine when to scale.
Suggested answer: D

Explanation:

The issue in this case is related to the processing tier, where EC2 instances are overwhelmed at peak times, causing delays. Option D, using an Amazon EC2 Auto Scaling target tracking policy based on the ApproximateNumberOfMessages in the SQS queue, is the best solution.

Detailed Explanation:

Auto Scaling with Target Tracking:

Target tracking policies dynamically scale out or in based on a specific metric. For this use case, you can monitor the ApproximateNumberOfMessages in the SQS queue. When the number of messages (orders) in the queue increases, the Auto Scaling group will scale out more EC2 instances to handle the additional load, ensuring that the queue doesn't build up and cause delays.

This solution is ideal for handling variable and unpredictable peak times, as Auto Scaling can automatically adjust based on real-time load rather than scheduled times.

Why Not the Other Options?:

Option A (Scheduled Scaling): Scheduled scaling works well for predictable peak times, but this company experiences unpredictable peak usage, making scheduled scaling less effective.

Option B (ElastiCache for Redis): Adding a caching layer would help if DynamoDB were the bottleneck, but in this case, the issue is CPU overload on EC2 instances in the processing tier.

Option C (CloudFront): CloudFront would help cache static content from the web tier, but it wouldn't resolve the issue of the processing tier's overloaded EC2 instances.

AWS

Reference:

Amazon EC2 Auto Scaling Target Tracking

Amazon SQS ApproximateNumberOfMessages

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asked 16/09/2024
Martin Mannsbarth
32 questions
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