Question 1

At the onset of every festive season, there is a surge in quick commerce orders (i.e. orders delivered within 15 minutes) on Swiggy. The supply chain head at Swiggy for a city is interested in a real-time view of the inventory of her dark stores (i.e. stores without nameboards where the supplies are kept and used to fulfil app orders). She wants to see this be presented in a monitor mounted in her office wall that refreshes with the latest info on a city map every 1 minute. Along with the info, there also needs to be the current time so that she gets a visual confirmation that this is the latest data. This dashboard allows her to plan for new orders of specific supplies that she is running out of so that no customer is left unsatisfied. What solution option below best solves for the need? You are given a Spark program that runs on a Google Dataproc cluster on a daily schedule starting execution at 7AM running typically for 2 hours, to produce as output the total amount spent on purchases made by every customer the previous day. The input data is coming into GCS every minute from a variety of sources as standalone files. Therefore, the business leader now feels that having to wait till 9AM the next day is no longer acceptable and instead ideally wants purchase information for each customer at least every 5 minutes during the day itself. What’s more, she wants to be able to change this time configuration later without involving you.\ Which amongst the below represents the best option to achieve the above? 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?