Google Professional Data Engineer Question 112
Select 3Google Cloud PlatformYou are designing a data pipeline in Google Cloud to process batch data and deliver analytics results. The pipeline includes multiple stages, such as data ingestion, transformation, and storage. During deployment, you need to ensure the pipeline is resilient, scalable, and operationally efficient. Which steps should you prioritize to achieve these goals?
- A
Implement automated retry logic for transient failures in the pipeline.
- B
Use BigQuery as a staging area to temporarily store raw data before transformation.
- C
Set up monitoring and alerting using Cloud Monitoring and Cloud Logging.
- D
Deploy all pipeline components in a single Compute Engine instance to simplify operations.
- E
Use Dataflow's autoscaling feature to dynamically adjust resources based on workload.
Show answer and explanation
Correct answers: A, C, E
Explanation
To deploy and operationalize a data pipeline effectively, it's crucial to prioritize resilience, scalability, and operational efficiency. By implementing automated retry logic, monitoring and alerting, and leveraging autoscaling features, you can ensure the pipeline can handle failures, adapt to workload changes, and provide visibility for maintenance and troubleshooting. Avoid single points of failure, like a single Compute Engine instance, and use appropriate tools for staging and processing data.
- A. Correct.
Implementing automated retry logic for transient failures improves the resilience of the pipeline by ensuring it can recover from temporary issues without manual intervention.
- B. Incorrect.
While BigQuery is a powerful analytics tool, it is not typically used as a staging area for raw data. Instead, tools like Cloud Storage are better suited for this purpose.
- C. Correct.
Setting up monitoring and alerting with Cloud Monitoring and Cloud Logging ensures operational visibility into the pipeline, allowing you to detect and respond to issues promptly.
- D. Incorrect.
Deploying all pipeline components on a single Compute Engine instance reduces scalability and fault-tolerance, which are critical for production-grade pipelines.
- E. Correct.
Using Dataflow's autoscaling feature ensures the pipeline can handle varying workloads efficiently by automatically adjusting resources.