Question: What are some business challenges that Cloud Dataproc addresses?
- High PUC cores and GPUs
- Ease of use and speed
- Idle clusters and scaling inflexibility
- Integration and customization
Explanation
Dataproc addresses ease of use and speed by providing a managed service for running Apache Spark and Hadoop workloads. It helps create clusters quickly and manage them with less administrative effort. Fast cluster startup, scaling, and shutdown reduce delays in big data processing work. This supports faster data processing outcomes while lowering the operational burden of managing open source data tools.
Why the other options are incorrect
High PUC cores and GPUs is not the business challenge identified for Dataproc.
Idle clusters and scaling inflexibility is a challenge Dataproc can reduce, but it is not the selected business-value pairing here.
Integration and customization describes capability flexibility, not the main challenge pairing of ease and speed.
Source for verification
https://docs.cloud.google.com/dataproc/docs/concepts/overview
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