The correct answer is Cloud Dataproc with Cloud Composer.

Cloud Dataproc provides a managed Spark and Hadoop environment that supports both Python and Scala development and it can scale across a compute cluster with autoscaling and cluster pooling. Cloud Composer supplies managed Apache Airflow for automated pipeline orchestration and it can submit and manage Dataproc jobs while handling DAGs, retries, and dependencies. Together they allow teams to isolate workloads by using separate or ephemeral clusters and they meet the needs of a shared data engineering and data science platform.

The combination is native to Google Cloud so it integrates with IAM, Cloud Storage, logging, and monitoring. Using Cloud Dataproc with Cloud Composer reduces operational overhead compared with building and maintaining a custom orchestration layer and it fits common enterprise patterns for scalable, multi team analytics.

Databricks on Google Cloud with Cloud Composer could support Python and Scala and Composer can orchestrate jobs but Databricks is a third party managed platform rather than the native Google Cloud managed Spark service. That difference brings additional licensing and integration considerations that make it a less likely correct choice on a GCP focused architecture exam.

Cloud Dataflow with Cloud Scheduler is not the best fit because Cloud Dataflow is an Apache Beam service optimized for streaming and batch transforms and it does not provide first class Scala based Spark runtimes. Also Cloud Scheduler is a simple cron style trigger and it does not offer the DAG based orchestration, dependency handling, and operational features that Composer provides.

Cloud Dataproc with Google Kubernetes Engine for orchestration would work in principle but using Google Kubernetes Engine for orchestration requires you to build and operate a custom orchestration layer and it adds significant operational overhead. The managed Airflow in Cloud Composer is a better fit for DAG oriented pipelines and integrates directly with Dataproc job APIs.

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