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Question 172 - Professional Machine Learning Engineer discussion

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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?

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

Explanation:

According to the official exam guide1, one of the skills assessed in the exam is to ''design, build, and productionalize ML models to solve business challenges using Google Cloud technologies''.Dataproc2is a fully managed, fast, and easy-to-use service for running Apache Spark and Apache Hadoop clusters on Google Cloud. Dataproc supports PySpark workloads and provides a simple way to migrate your existing Spark jobs to the cloud. You can create a Dataproc cluster with a few clicks or commands, and run your PySpark jobs on it.You can also use Vertex AI Workbench3, a managed notebook service, to create and run PySpark notebooks on Dataproc clusters. This way, you can interactively develop and test your PySpark code on the cloud. Therefore, option C is the best way to build a proof of concept to migrate one data science job to Google Cloud with minimal cost and effort. The other options are not relevant or optimal for this scenario.Reference:

Professional ML Engineer Exam Guide

Dataproc

Vertex AI Workbench

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

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asked 18/09/2024
Mr. Michael Mettam
29 questions
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