Google Professional Cloud DevOps Engineer exam dumps

Google Professional Cloud DevOps Engineer practice question 135 of 268

Professional Cloud DevOps Engineer. Associate level, Google Cloud. Free question with the correct answer and a full explanation.

Google Professional Cloud DevOps Engineer Question 135

Select 3Google Cloud Platform

Your team has a custom application running on Google Kubernetes Engine (GKE) that writes important operational data to Cloud Logging. You need to create a custom metric to monitor error rates from these logs and set up an alerting policy. Which steps should you take to achieve this goal?

  1. A

    Create a logs-based metric in Cloud Monitoring using a filter that matches error-level entries in the logs.

  2. B

    Export the logs to BigQuery and then use SQL queries to calculate error rates.

  3. C

    Use Cloud Monitoring to define an alerting policy based on the logs-based metric you created.

  4. D

    Enable Cloud Trace in your project to automatically generate error metrics from your logs.

  5. E

    Ensure your logging filter is specific enough to capture only the relevant error logs for the custom metric.

Show answer and explanation

Correct answers: A, C, E

Explanation

To monitor error rates from logs, you should create a logs-based metric in Cloud Monitoring using a filter to capture the relevant data. Once the metric is created, you can define an alerting policy to notify the team when the error rate exceeds a threshold. A specific filter ensures accurate data for the custom metric. Exporting logs to BigQuery or using Cloud Trace is not applicable in this scenario.

  • A. Correct.

    Creating a logs-based metric in Cloud Monitoring using a filter is the correct first step to capture metrics from logs.

  • B. Incorrect.

    Exporting logs to BigQuery is unnecessary in this case. Logs-based metrics can directly analyze logs without exporting them to BigQuery.

  • C. Correct.

    Defining an alerting policy in Cloud Monitoring is essential for notifying your team when the error rate exceeds a defined threshold.

  • D. Incorrect.

    Enabling Cloud Trace does not help in this scenario as it is used for distributed tracing, not generating custom metrics from logs.

  • E. Correct.

    Ensuring the logging filter is specific avoids capturing irrelevant data and ensures the custom metric accuracy.

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