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Google Cloud Certified Professional Data Engineer · Google Cloud
Select one answer.
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C for me
using Cloud Dataflow for transformations with monitoring via Stackdriver and leveraging its default autoscaling settings, is the best choice. Cloud Dataflow is purpose-built for this type of workload, providing seamless scalability and efficient processing capabilities for streaming data. Its autoscaling feature minimizes manual intervention and helps manage costs by dynamically adjusting resources based on the actual processing needs, which is crucial for handling fluctuating data volumes efficiently and cost-effectively.
Option C suggests using Cloud Dataflow to run the transformations and monitoring the job system lag with Stackdriver while using the default autoscaling setting for worker instances. While using Cloud Dataflow is a suitable choice for processing data from Cloud Pub/Sub to BigQuery, and monitoring with Stackdriver provides valuable insights, the specific emphasis on configuring non-default Compute Engine machine types (as mentioned in option D) gives you more control over cost optimization and performance tuning. By configuring non-default machine types, you can precisely tailor the computational resources to match the specific requirements of your workload. This fine-grained control allows you to optimize costs further by avoiding over-provisioning of resources, especially if your workload is memory-intensive, CPU-bound, or requires specific configurations that are not met by the default settings.