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Question 181 - Professional Machine Learning Engineer discussion

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You need to develop a custom TensorRow model that will be used for online predictions. The training data is stored in BigQuery. You need to apply instance-level data transformations to the data for model training and serving. You want to use the same preprocessing routine during model training and serving. How should you configure the preprocessing routine?

A.
Create a BigQuery script to preprocess the data, and write the result to another BigQuery table.
Answers
A.
Create a BigQuery script to preprocess the data, and write the result to another BigQuery table.
B.
Create a pipeline in Vertex Al Pipelines to read the data from BigQuery and preprocess it using a custom preprocessing component.
Answers
B.
Create a pipeline in Vertex Al Pipelines to read the data from BigQuery and preprocess it using a custom preprocessing component.
C.
Create a preprocessing function that reads and transforms the data from BigQuery Create a Vertex Al custom prediction routine that calls the preprocessing function at serving time.
Answers
C.
Create a preprocessing function that reads and transforms the data from BigQuery Create a Vertex Al custom prediction routine that calls the preprocessing function at serving time.
D.
Create an Apache Beam pipeline to read the data from BigQuery and preprocess it by using TensorFlow Transform and Dataflow.
Answers
D.
Create an Apache Beam pipeline to read the data from BigQuery and preprocess it by using TensorFlow Transform and Dataflow.
Suggested answer: D

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''.TensorFlow Transform2is a library for preprocessing data with TensorFlow. TensorFlow Transform enables you to define and execute distributed pre-processing or feature engineering functions on large data sets, and then export the same functions as a TensorFlow graph for re-use during training or serving. TensorFlow Transform can handle both instance-level and full-pass data transformations.Apache Beam3is an open source framework for building scalable and portable data pipelines. Apache Beam supports both batch and streaming data processing.Dataflow4is a fully managed service for running Apache Beam pipelines on Google Cloud. Dataflow handles the provisioning and management of the compute resources, as well as the optimization and execution of the pipelines. Therefore, option D is the best way to configure the preprocessing routine for the given use case, as it allows you to use the same preprocessing logic during model training and serving, and leverage the scalability and performance of Dataflow. The other options are not relevant or optimal for this scenario.Reference:

Professional ML Engineer Exam Guide

TensorFlow Transform

Apache Beam

Dataflow

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
DAVID ALCARAZ AZNAR
27 questions
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