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Answer: Use a load testing tool to simulate the expected number of concurrent users and total requests to your application, and inspect the results.
The correct answer is A: 'Use a load testing tool to simulate the expected number of concurrent users and total requests to your application, and inspect the results.' This approach directly tests the current deployment under the expected load conditions, allowing you to verify the application's latency and overall performance. While enabling autoscaling and sending curl requests (option B) could help in managing load, using a load testing tool provides a more comprehensive and realistic simulation of user traffic, helping you to observe how your application handles the load and identify any potential performance bottlenecks.
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Your team is developing a web application that will be deployed on Google Kubernetes Engine (GKE). Your CTO expects a successful launch with the application needing to handle the expected load of tens of thousands of users efficiently. The goal is to ensure the application's latency stays below a certain threshold during high traffic periods. What should you do to test the current deployment's ability to maintain low latency under these conditions?
A
Use a load testing tool to simulate the expected number of concurrent users and total requests to your application, and inspect the results.
B
Enable autoscaling on the GKE cluster and enable horizontal pod autoscaling on your application deployments. Send curl requests to your application, and validate if the autoscaling works.
C
Replicate the application over multiple GKE clusters in every Google Cloud region. Configure a global HTTP(S) load balancer to expose the different clusters over a single global IP address.
D
Use Cloud Debugger in the development environment to understand the latency between the different microservices.
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