Question: Which statement best describes where Cloud Dataproc falls on the big data processing model and the role it plays?
- Cloud Dataproc allows organizations to scale data storage and ensures accessibility without compromising security.
- Cloud Dataproc allows organizations to easily use MapReduce, Pig, Hive, and Spark to process data before storing it, and it helps organizations interactively analyze data with Spark and Hive.
- Cloud Dataproc allows organizations to transform and enrich data in stream and batch modes.
- Cloud Dataproc allows organizations to ingest event streams from anywhere, at any scale, for simple, reliable, real-time stream analytics.
Explanation
Dataproc fits the process stage of the big data processing model by running managed open source data processing workloads. It supports Apache Spark, Hadoop, Hive, and Pig for batch processing and interactive analysis. This helps organizations use familiar tools without managing clusters manually. Its value is faster setup, scalable processing, and reduced operational overhead for open source big data workloads.
Why the other options are incorrect
Scale data storage describes storage services such as Cloud Storage, not Dataproc.
Stream and batch enrichment describes Cloud Dataflow, not Dataproc.
Event stream ingestion describes Pub/Sub, not Dataproc.
Source for verification
https://cloud.google.com/dataproc/docs/concepts/overview
The answer(s) to the question is highlighted in the BOLD text above. You can also find more questions and answers related to the exams on the "Google Cloud Platform Business Professional" page.