Google Cloud Professional Machine Learning Engineer — Question 202

You work for a startup that has multiple data science workloads. Your compute infrastructure is currently on-premises, and the data science workloads are native to PySpark. Your team plans to migrate their data science workloads to Google Cloud. You need to build a proof of concept to migrate one data science job to Google Cloud. You want to propose a migration process that requires minimal cost and effort. What should you do first?

Answer options

• A. Create a n2-standard-4 VM instance and install Java, Scala, and Apache Spark dependencies on it.
• B. Create a Google Kubernetes Engine cluster with a basic node pool configuration, install Java, Scala, and Apache Spark dependencies on it.
• C. Create a Standard (1 master, 3 workers) Dataproc cluster, and run a Vertex AI Workbench notebook instance on it.
• D. Create a Vertex AI Workbench notebook with instance type n2-standard-4.

Correct answer: C

Explanation

The correct answer is C because using a Standard Dataproc cluster is specifically designed for running big data processing tasks like those using PySpark, and it integrates well with Vertex AI for machine learning workflows. The other options involve setting up infrastructure that may require more management and configuration, resulting in higher costs and complexity, which does not align with the goal of minimizing effort and expense.