Question 3: You are asked to build a recommendation engine for an off…

You are asked to build a recommendation engine for an offline store at the checkout counter, using a combination of cloud and point-of-sale (PoS) terminal resources. Consider the following pipeline choices for effecting the same outcome, where Kubernetes is an open-source system for automating deployment, scaling and management of containerized applications, Google Datastore and HBase are both highly-scalable NoSQL database systems for interactive, real-time applications, and Google Vertex AI is a hosted platform that lets you train and deploy ML models
(i) Recommender model training on Python VM on GCP; Data publisher on VM in PoS → Kafka VM on GCP → Spark Streaming on Hadoop VMs on GCP -> Recommender hosted on Python VM on GCP → Relay recommendation to PoS (ii) Recommender model training on Vertex AI; Data publisher on VM in PoS → Kafka VM on GCP → Dataflow -> Recommender hosted on Vertex AI → Relay recommendation to PoS (iii) Recommender model training on Python VM on GCP; Data publisher on VM in PoS → Pub/Sub → Spark Streaming on Google Dataproc → Recommender hosted on Python VM on GCP -> Relay recommendation to PoS (iv) Recommender model training on Python VM on GCP; Data publisher on VM in PoS → Pub/Sub → Dataflow → Recommender hosted on Python VM on GCP → Relay recommendation to PoS (v) Recommender model training on Python VM on GCP; Data publisher on VM in PoS → Pub/Sub → Spark Streaming on Hadoop VMs on GCP → Recommender hosted on Python VM on GCP → Relay recommendation to PoS
Which option below represents the correct order of pipeline options that has the “most IaaS” entry to the left and the “most PaaS” entry to the right?