You work for an advertising company, and you've developed a Spark ML model to predict click-through rates at advertisement blocks. You've been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be closing soon, so a rapid lift-and-shift migration is necessary. However, the data you've been using will be migrated to migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?
A) Use Cloud ML Engine for training existing Spark ML models
B) Rewrite your models on TensorFlow, and start using Cloud ML Engine
C) Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
D) Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery

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The Answer of You work for an advertising company, and you've developed a Spark ML model to predict click-through rates at advertisement blocks. You've been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be closing soon, so a rapid lift-and-shift migration is necessary. However, the data you've been using will be migrated to migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?
A) Use Cloud ML Engine for training existing Spark ML models
B) Rewrite your models on TensorFlow, and start using Cloud ML Engine
C) Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
D) Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery

You Work for an Advertising Company, and You've Developed a Spark