Question: Which of these statements best describes the kinds of transforms a Cloud Dataflow pipeline can do?
- Cloud Dataflow takes a query and runs information from the database through it to produce tables of data organized according to the requirements of the original query.
- Cloud Dataflow relies on a large database to store and analyze data processing pipelines, performing transforms resulting in predictive analytics that can be leveraged to optimize business decisions.
- Cloud Dataflow relies on training data to enable machine learning that can then read multiple streams of data and perform transforms that produce resulting output data.
- Cloud Dataflow reads data in and can apply filtering, grouping, comparing, joining, or aggregation
Explanation
Cloud Dataflow is a managed service for building and running stream and batch data processing pipelines. It uses the Apache Beam programming model, where a pipeline applies transforms to collections of data. These transforms support common data preparation and processing patterns before writing results to a destination. This makes Cloud Dataflow suitable for ETL, real-time analytics, and large-scale data processing workflows.
Why the other options are incorrect
Query-based table output describes database querying, not Cloud Dataflow pipeline processing.
Predictive analytics database confuses data processing with analytics and machine learning outcomes.
Training-data machine learning describes ML model behavior, not Cloud Dataflow transforms.
Source for verification
https://cloud.google.com/dataflow/docs/concepts/beam-programming-model
https://cloud.google.com/dataflow/docs/overview
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