Google Professional Data Engineer Question 254
Select 3Google Cloud PlatformYou are a data engineer responsible for managing a critical data pipeline in Google Cloud that processes streaming data into BigQuery. Recently, some pipeline jobs have been failing intermittently, and latency has increased. Your team has asked you to quickly identify the root cause and monitor the pipeline's health. Which of the following actions should you take to effectively monitor and troubleshoot the issue?
- A
Enable and review Cloud Logging logs for the pipeline components to identify errors or warnings.
- B
Use the BigQuery Admin Panel to analyze query performance and review job execution details for potential bottlenecks.
- C
Set up custom metrics in Cloud Monitoring to track pipeline latency and trigger alerts for high latency.
- D
Manually inspect raw data in storage to find potential anomalies causing the pipeline to fail.
- E
Enable Stackdriver Debugger to analyze the application code of the pipeline in real-time.
Show answer and explanation
Correct answers: A, B, C
Explanation
To effectively troubleshoot and monitor a data pipeline in Google Cloud, you should leverage tools designed for observability, such as Cloud Logging for reviewing errors, the BigQuery Admin Panel for execution details, and Cloud Monitoring for tracking metrics like latency. These tools provide detailed insights and scalable solutions to identify and resolve issues quickly, compared to manual inspection or unrelated debugging tools.
- A. Correct.
Cloud Logging provides detailed logs for monitoring and troubleshooting pipeline components, such as identifying errors or warnings in data processing jobs.
- B. Correct.
The BigQuery Admin Panel allows you to monitor query performance, view execution details, and identify performance bottlenecks or failed jobs that could impact the pipeline.
- C. Correct.
Custom metrics in Cloud Monitoring can help track critical pipeline health indicators, such as latency, and automatically trigger alerts when thresholds are breached.
- D. Incorrect.
Manually inspecting raw data in storage is not an efficient or scalable approach for troubleshooting pipeline issues, especially when tools like logging and monitoring are available.
- E. Incorrect.
Stackdriver Debugger is used for debugging application code, but it is not relevant in this scenario as the issue is related to pipeline monitoring and data process observability.