Google Professional Data Engineer exam dumps

Google Professional Data Engineer practice question 255 of 279

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

Google Professional Data Engineer Question 255

Select 3Google Cloud Platform

Your team manages a data pipeline that ingests data into BigQuery from multiple sources. Recently, there have been intermittent job failures in the pipeline, causing inconsistencies in the ingested data. You need to identify the root cause of the failures and monitor the pipeline's health. Which combination of tools should you use to achieve this effectively?

  1. A

    Cloud Logging to review detailed logs of the pipeline's execution and errors.

  2. B

    Cloud Monitoring to set up alerts and dashboards for pipeline health metrics.

  3. C

    BigQuery Admin Panel to monitor query execution and dataset activity.

  4. D

    Cloud Spanner to store detailed logs and analyze pipeline performance.

  5. E

    Cloud Pub/Sub to track the health of the pipeline's messaging system.

Show answer and explanation

Correct answers: A, B, C

Explanation

To effectively identify and monitor the issues in a data pipeline, you need a combination of tools that provide observability into the system. Cloud Logging helps with detailed error analysis, Cloud Monitoring provides proactive monitoring and alerting, and BigQuery Admin Panel offers insights into query and dataset activity. Together, these tools create a comprehensive observability framework for troubleshooting and monitoring the pipeline.

  • A. Correct.

    Cloud Logging provides detailed logs that can help identify errors and failures in the data pipeline. This is crucial for root cause analysis.

  • B. Correct.

    Cloud Monitoring allows you to create dashboards and alerts to monitor the health and performance of your pipeline. This helps in proactive issue detection.

  • C. Correct.

    BigQuery Admin Panel helps you monitor query execution times, errors, and dataset activity, which is important for diagnosing issues in data ingestion.

  • D. Incorrect.

    Cloud Spanner is a globally distributed database and is not intended for logging or monitoring purposes. It is irrelevant to this use case.

  • E. Incorrect.

    Cloud Pub/Sub is a messaging service, and while it can be a part of your pipeline, it does not directly provide observability for pipeline health or failures.

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