You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue. What should you do?
A.
Use Cloud Logging to identify error groups in the pipeline's logs. Use Cloud Monitoring to create a dashboard that tracks the number of errors in each group.
B.
Use Cloud Logging to create a chart displaying the pipeline’s error logs. Use Metrics Explorer to validate the findings from the chart.
C.
Use Cloud Logging to view error messages in the pipeline's logs. Use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization and memory usage.
D.
Use the Dataflow job monitoring interface to check the pipeline's status every hour. Use Cloud Profiler to analyze the pipeline’s metrics, such as CPU utilization and memory usage.
The best approach for troubleshooting intermittent Dataflow pipeline failures is C. Use Cloud Logging to view error messages and Cloud Monitoring to analyze pipeline metrics. This is most effective because Cloud Logging error messages pinpoint what is failing, while Cloud Monitoring metrics reveal why, often due to resource issues. Option A is less detailed and comprehensive. Option B is less targeted and its validation approach is unclear. Option D is inefficient and Cloud Profiler is less suited for general failure diagnosis compared to pipeline metrics and logs.
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