Google Professional Cloud DevOps Engineer Question 245
Select 3Google Cloud PlatformYour organization has recently deployed a new microservices-based application on Google Kubernetes Engine (GKE). The DevOps team needs to monitor key application metrics, such as request latency, error rates, and CPU usage, to ensure Service Level Objectives (SLOs) are met. Which of the following actions should you take to manage and monitor these metrics effectively?
- A
Instrument the application code to export custom metrics using OpenTelemetry and send them to Google Cloud Monitoring.
- B
Use Kubernetes built-in metrics like node resource utilization and container restart counts to monitor application performance.
- C
Set up uptime checks and alerting policies in Google Cloud Monitoring to track the availability of the application endpoints.
- D
Configure a Cloud Function to periodically query the metrics and write them to a BigQuery dataset for analysis.
- E
Define SLOs and SLIs in Google Cloud Monitoring to measure and track the application's performance against business objectives.
Show answer and explanation
Correct answers: A, C, E
Explanation
To manage metrics effectively in a microservices-based application, you need to monitor both application-specific and infrastructure-level metrics. Instrumenting the application with OpenTelemetry allows for detailed custom metrics collection, while uptime checks and SLOs in Google Cloud Monitoring ensure comprehensive monitoring of both availability and business-critical objectives. Kubernetes built-in metrics and BigQuery analysis, while useful in some scenarios, are not sufficient for the requirements specified in this scenario.
- A. Correct.
Instrumenting the application code to export custom metrics using OpenTelemetry ensures that you can gather detailed metrics such as request latency and error rates, which are critical for application performance monitoring.
- B. Incorrect.
While Kubernetes built-in metrics provide useful insights on infrastructure-level performance, they do not directly address application-specific metrics like request latency or error rates.
- C. Correct.
Uptime checks and alerting policies in Google Cloud Monitoring are essential for tracking the availability and health of application endpoints, which is a key aspect of managing application metrics.
- D. Incorrect.
Using a Cloud Function to query metrics and write them to BigQuery is not an efficient approach for real-time monitoring and alerting, as it adds unnecessary complexity and latency.
- E. Correct.
Defining SLOs and SLIs in Google Cloud Monitoring allows you to align monitoring and alerting with business objectives, enabling you to measure performance effectively and take corrective actions as needed.