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Google Professional-Machine-Learning-Engineer Exam With Confidence Using Practice Dumps

Exam Code:
Professional-Machine-Learning-Engineer
Exam Name:
Google Professional Machine Learning Engineer
Certification:
Machine Learning Engineer
Vendor:
Google
Questions:
296
Last Updated:
Mar 25, 2026
Exam Status:
Stable
Google Professional-Machine-Learning-Engineer

Professional-Machine-Learning-Engineer: Machine Learning Engineer Exam 2025 Study Guide Pdf and Test Engine

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Google Professional Machine Learning Engineer Questions and Answers

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Question 1

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?

Options:

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.

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.

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Question 2

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?

Options:

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.

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.

Question 3

You are developing an ML pipeline using Vertex Al Pipelines. You want your pipeline to upload a new version of the XGBoost model to Vertex Al Model Registry and deploy it to Vertex Al End points for online inference. You want to use the simplest approach. What should you do?

Options:

A.

Use the Vertex Al REST API within a custom component based on a vertex-ai/prediction/xgboost-cpu image.

B.

Use the Vertex Al ModelEvaluationOp component to evaluate the model.

C.

Use the Vertex Al SDK for Python within a custom component based on a python: 3.10 Image.

D.

Chain the Vertex Al ModelUploadOp and ModelDeployop components together.

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