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Google Professional Cloud Developer: How to Conduct a Load Test on Google Cloud Run Using JMeter, BigQuery, and Looker Studio?

By: Author Alex Lim

Posted on

Categories Exam

Learn the Google-recommended practices for load testing a Cloud Run service using JMeter, BigQuery, and Looker Studio. Follow these steps to effectively orchestrate your load test and analyze the results.

Table of Contents

Question

You are preparing to conduct a load test on your Cloud Run service by using JMeter. You need to orchestrate the steps and services to use for an effective load test and analysis. You want to follow Google-recommended practices. What should you do?

A. Install JMeter on your local machine, create a log sink to BigQuery, and use Looker to analyze the results.
B. Set up a Compute Engine instance, install JMeter on the instance, create a log sink to a Cloud Storage bucket, and use Looker Studio to analyze the results.
C. Set up a Compute Engine instance, install JMeter on the instance, create a log sink to a Cloud Storage bucket, and use Looker to analyze the results.
D. Set up a Compute Engine instance, install JMeter on the instance, create a log sink to BigQuery, and use Looker Studio to analyze the results.

Answer

D. Set up a Compute Engine instance, install JMeter on the instance, create a log sink to BigQuery, and use Looker Studio to analyze the results.

Explanation

To conduct a load test on your Google Cloud Run service using JMeter and analyze the results following Google-recommended practices, you should:

  1. Set up a Compute Engine instance: Create a virtual machine instance on Google Cloud Platform to run your load testing tool.
  2. Install JMeter on the instance: Deploy the open-source load testing tool JMeter on your Compute Engine instance. This allows you to simulate user traffic and test your Cloud Run service’s performance under load.
  3. Create a log sink to BigQuery: Configure a log sink to export your Cloud Run service’s logs to BigQuery, Google’s fully-managed, serverless data warehouse. This enables you to store and analyze your load test data efficiently.
  4. Use Looker Studio to analyze the results: Utilize Google’s Looker Studio (formerly Data Studio) to visualize and analyze the load test results stored in BigQuery. Looker Studio provides interactive dashboards and reports to help you gain insights from your data.

By following these steps, you can effectively orchestrate a load test on your Cloud Run service, capture the necessary data, and analyze the results using Google’s recommended tools and practices. This approach ensures a comprehensive and efficient load testing process.

Google Professional Cloud Developer certification exam assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the Google Professional Cloud Developer exam and earn Google Professional Cloud Developer certification.