Google Professional Data Engineer Question 26
Select 3Google Cloud PlatformYour organization has implemented a data pipeline using Apache Beam on Cloud Dataflow to process streaming data from IoT devices. The pipeline occasionally experiences delays and errors due to increased load during peak hours. You have been tasked with monitoring and orchestrating the pipeline to ensure reliable performance. Which of the following actions should you take to address these issues?
- A
Set up Cloud Monitoring alerts to track pipeline job metrics such as system lag and failed processing elements.
- B
Use Dataflow's Autoscaling feature to dynamically allocate workers based on load variations.
- C
Schedule the pipeline to run at fixed intervals using Cloud Scheduler to avoid processing delays.
- D
Implement retries and dead-letter queues in the pipeline to handle transient errors and undeliverable data.
- E
Disable pipeline logging to reduce overhead and improve processing speed.
Show answer and explanation
Correct answers: A, B, D
Explanation
To ensure reliable performance of a Dataflow pipeline, it is essential to monitor metrics like system lag and failures, dynamically scale resources with Autoscaling, and handle errors gracefully using retries and dead-letter queues. These measures help address the issues caused by increased load during peak hours and improve the pipeline's overall resilience and efficiency.
- A. Correct.
Setting up Cloud Monitoring alerts helps you proactively identify and address performance issues like increased system lag or failed elements in the data pipeline.
- B. Correct.
Dataflow's Autoscaling feature allows the pipeline to dynamically adjust the number of workers based on the workload, which is critical for handling peak load scenarios.
- C. Incorrect.
Scheduling the pipeline at fixed intervals does not address the streaming nature of the pipeline and may cause further delays or data loss during peak loads.
- D. Correct.
Retries and dead-letter queues help ensure that transient errors are retried and undeliverable data is captured for further analysis, improving the pipeline's reliability.
- E. Incorrect.
Disabling pipeline logging would hinder troubleshooting and monitoring efforts, making it harder to identify and resolve issues.