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Answer: Integrate the monitoring data into Cloud Monitoring and establish an Alerting policy for immediate failure notifications.
**Correct Answer: D** Integrating monitoring data into Cloud Monitoring and setting up an Alerting policy is the most efficient and scalable approach for overseeing the health of data pipelines across BigQuery, Dataflow, and Dataproc. Cloud Monitoring, as a managed service, simplifies the monitoring process and enables the definition of specific conditions for alerts, ensuring that the team is promptly notified of any issues. **Why other options are less optimal:** - **A:** Creating a custom App Engine solution demands additional development and maintenance efforts, making it less efficient than leveraging Cloud Monitoring's built-in capabilities. - **B:** While feasible, this method introduces unnecessary complexity by involving BigQuery and App Engine for a task that Cloud Monitoring can handle more straightforwardly. - **C:** This approach not only requires managing a VM and Airflow but also misses the opportunity to fully utilize GCP's managed monitoring services, leading to higher operational overhead.
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You are responsible for monitoring the health and performance of data pipelines across BigQuery, Dataflow, and Dataproc, with a focus on detecting failures and ensuring timely notifications. Your solution must span multiple projects and prioritize the use of managed services or platform features. Which approach best meets these requirements?
A
Develop a custom App Engine application to fetch logs via GCP API calls and dispatch email alerts upon detecting failures.
B
Transfer logs to BigQuery, configure App Engine to analyze this data, and trigger email alerts for any identified failures.
C
Deploy a Virtual Machine in Compute Engine equipped with Airflow, then funnel the monitoring data into Cloud Monitoring.
D
Integrate the monitoring data into Cloud Monitoring and establish an Alerting policy for immediate failure notifications.