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Exam Professional Cloud Developer topic 1 question 137 discussion

Actual exam question from Google's Professional Cloud Developer
Question #: 137
Topic #: 1
[All Professional Cloud Developer Questions]

You recently developed a new service on Cloud Run. The new service authenticates using a custom service and then writes transactional information to a Cloud
Spanner database. You need to verify that your application can support up to 5,000 read and 1,000 write transactions per second while identifying any bottlenecks that occur. Your test infrastructure must be able to autoscale. What should you do?

  • A. Build a test harness to generate requests and deploy it to Cloud Run. Analyze the VPC Flow Logs using Cloud Logging.
  • B. Create a Google Kubernetes Engine cluster running the Locust or JMeter images to dynamically generate load tests. Analyze the results using Cloud Trace.
  • C. Create a Cloud Task to generate a test load. Use Cloud Scheduler to run 60,000 Cloud Task transactions per minute for 10 minutes. Analyze the results using Cloud Monitoring.
  • D. Create a Compute Engine instance that uses a LAMP stack image from the Marketplace, and use Apache Bench to generate load tests against the service. Analyze the results using Cloud Trace.
Show Suggested Answer Hide Answer
Suggested Answer: B 🗳️
by scaenruy at Jan. 9, 2022, 3:24 p.m.

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scaenruy
Highly Voted 2 years, 3 months ago
I vote B https://cloud.google.com/architecture/distributed-load-testing-using-gke
upvoted 7 times
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hongminhcbg
Most Recent 2 weeks, 3 days ago
Selected Answer: B
B is correct.
upvoted 1 times
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__rajan__
7 months, 1 week ago
Selected Answer: B
B is correct.
upvoted 1 times
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purushi
8 months, 4 weeks ago
Selected Answer: B
The key here is "Your test infrastructure must be able to autoscale" and load testing.
upvoted 1 times
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omermahgoub
1 year, 3 months ago
Selected Answer: B
B. Create a Google Kubernetes Engine cluster running the Locust or JMeter images to dynamically generate load tests. Analyze the results using Cloud Trace. To verify that your application can support up to 5,000 read and 1,000 write transactions per second and to identify any bottlenecks that occur, you can use a load testing tool such as Locust or JMeter to generate load tests on your Cloud Run service. These tools allow you to simulate a high number of concurrent requests and help you determine the maximum number of requests your service can handle. You can run the load testing tool on a Google Kubernetes Engine (GKE) cluster which will support autoscale feature, this way you can handle the high number of requests, and use Cloud Trace to analyze the results, which will give you insights into the performance and any bottlenecks.
upvoted 1 times
omermahgoub
1 year, 3 months ago
A. Build a test harness to generate requests and deploy it to Cloud Run. Analyze the VPC Flow Logs using Cloud Logging. VPC flow logs would not provide the transaction details, it's more useful to troubleshoot issues at the network level. C. Create a Cloud Task to generate a test load. Use Cloud Scheduler to run 60,000 Cloud Task transactions per minute for 10 minutes. Analyze the results using Cloud Monitoring. Although cloud task is a good solution for scheduling the test loads, it's not the best solution for load testing since it doesn't support dynamic loading and it would be hard to get the fine-grained details about the performance. D. Create a Compute Engine instance that uses a LAMP stack image from the Marketplace, and use Apache Bench to
upvoted 1 times
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zellck
1 year, 4 months ago
Selected Answer: B
B is the answer. https://cloud.google.com/architecture/distributed-load-testing-using-gke
upvoted 1 times
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tomato123
1 year, 8 months ago
Selected Answer: B
B is correct
upvoted 2 times
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nehaxlpb
1 year, 10 months ago
Selected Answer: B
This tutorial explains how to use Google Kubernetes Engine (GKE) to deploy a distributed load testing framework that uses multiple containers to create traffic for a simple REST-based API. This tutorial load-tests a web application deployed to App Engine that exposes REST-style endpoints to respond to incoming HTTP POST requests. You can use this same pattern to create load testing frameworks for a variety of scenarios and applications, such as messaging systems, data stream management systems, and database systems.
upvoted 1 times
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dishum
2 years ago
Correct answer is B
upvoted 1 times
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