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Google Cloud Certified Professional Cloud Architect · Google Cloud
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1. Managed Hadoop and Spark: Dataproc is specifically designed for running and managing Apache Spark and Hadoop clusters, which directly addresses your company's needs. 2. Scalability: Dataproc allows you to easily scale your clusters to handle the increasing number and size of jobs. You can add or remove nodes as needed to accommodate the workload. 3. Minimal Operations Work: Dataproc automates cluster creation, configuration, and management, minimizing the operational overhead. This is crucial since you want to reduce operations work. 4. Code Compatibility: Dataproc is compatible with existing Spark and Hadoop code, so you can migrate your jobs with minimal or no code changes.
B is correct because Dataproc is uded for Apache Hadoop and Spark
Dataflow for data stream and batch. Dataproc for data process with Apache Spark and Hadoop. Compute Engine for VM. Kubernetes Engine for Kubernetes Cluster with Compute Engine under the hood.