1. Why create custom metrics and alerts in Google Cloud SQL?

2. What is the primary purpose of creating custom metrics?

3. Which Google Cloud service enables custom metrics and alerts for Cloud SQL?

4. Which programming language can be used to create custom metrics for Google Cloud SQL?

5. What type of alerting policy in Google Cloud Monitoring allows custom conditions?

6. How can you enable Google Cloud Monitoring for your GCP project?

7. Which programming language is used in the provided Python code to create a custom metric?

8. What does the custom metric descriptor’s “type” field represent in the code?

9. What is the value type of the custom metric in the code?

10. How is the custom metric data pushed to Google Cloud Monitoring in Python?

11. In the code example, what is the resource type set to for the custom metric?

12. What is the primary benefit of setting up custom alerts for Google Cloud SQL?

13. How can you create alerting policies in Google Cloud Monitoring?

14. What should you do after setting up custom metrics and alerts?

15. Which of the following is NOT a suitable notification channel for alerts?

16. What can you track using custom metrics?

17. What’s the first step to create custom metrics for Google Cloud SQL?

18. What are custom metrics used for in Google Cloud SQL?

19. How can you simulate metric data for testing custom alerts?

20. Which built-in Google Cloud SQL metric covers query latency?

21. What’s the key advantage of creating custom metrics and alerts in Google Cloud SQL?

22. How often should you refine your alerting policies?

23. What should be the threshold for setting up alert conditions?

24. What is the purpose of defining custom metrics in Google Cloud SQL?

25. Which of the following is NOT a notification channel for alerts in Google Cloud Monitoring?