Google Professional Machine Learning Engineer Exam - Topic 6 Question 102 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam

Question #: 102
Topic #: 6

[All Professional Machine Learning Engineer Questions]

You are responsible for building a unified analytics environment across a variety of on-premises data marts. Your company is experiencing data quality and security challenges when integrating data across the servers, caused by the use of a wide range of disconnected tools and temporary solutions. You need a fully managed, cloud-native data integration service that will lower the total cost of work and reduce repetitive work. Some members on your team prefer a codeless interface for building Extract, Transform, Load (ETL) process. Which service should you use?

ADataflow

BDataprep

CApache Flink

DCloud Data Fusion

Show Suggested Answer

Suggested Answer: D

Cloud Data Fusion is a fully managed, cloud-native data integration service that helps users efficiently build and manage ETL/ELT data pipelines. It provides a graphical interface to increase time efficiency and reduce complexity, and allows users to easily create and explore data pipelines using a code-free, point and click visual interface. Cloud Data Fusion also supports a broad range of data sources and formats, including on-premises data marts, and ensures data quality and security by using built-in transformation capabilities and Cloud Data Loss Prevention. Cloud Data Fusion lowers the total cost of ownership by handling performance, scalability, availability, security, and compliance needs automatically.Reference:

Cloud Data Fusion documentation

Cloud Data Fusion overview

by Jennifer at Jun 15, 2025, 06:42 PM

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Winifred

4 months ago

Dataflow might work too, but I prefer codeless options.

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Gaynell

4 months ago

Totally agree, it’s user-friendly and cloud-native!

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Twanna

4 months ago

I’m surprised they want a codeless interface. Isn’t that limiting?

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Winfred

4 months ago

Wait, isn’t Apache Flink more for stream processing?

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Kayleigh

5 months ago

I think Cloud Data Fusion is the best choice here.

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Aimee

5 months ago

Cloud Data Fusion sounds familiar; I think it was mentioned in a practice question about integrating multiple data sources effectively.

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Kina

5 months ago

I feel like Dataprep could be a good option too, especially for data cleaning, but I'm not sure if it covers the full ETL process.

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Francene

5 months ago

I'm not entirely sure, but I think Dataflow is more about stream processing, while we need something for batch ETL.

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Kate

6 months ago

I remember we discussed the importance of a codeless interface for ETL processes in our last study session. I think that points towards Cloud Data Fusion.

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Ruth

6 months ago

I think Dataflow is the way to go here. It's a fully managed service that can handle the data integration challenges, and it seems to fit the team's needs pretty well. I'm confident that's the best choice for this scenario.

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Stefania

6 months ago

Hmm, this is a tricky one. I'm leaning towards Dataprep since it sounds like it has the codeless ETL interface that the team prefers. But I'm not sure if it can handle the full range of data integration needs. I'll have to research the options more.

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Nina

6 months ago

I'm a bit confused by the options. Dataflow and Cloud Data Fusion both sound like they could work, but I'm not sure which one would be better for a codeless ETL interface. I'll have to think this through carefully.

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Ivette

6 months ago

This seems like a straightforward question. I think Dataflow is the best choice here since it's a fully managed, cloud-native data integration service that can handle the data quality and security challenges mentioned.

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Val

9 months ago

Haha, I bet the developers of Cloud Data Fusion had a field day coming up with that name. But it does sound like the perfect solution for our needs.

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Catalina

9 months ago

I was leaning towards B) Dataprep, but D) Cloud Data Fusion makes more sense given the requirement for a codeless interface for ETL processes.

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Jaleesa

8 months ago

I agree, it will help us lower costs and reduce repetitive work.

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Sylvie

8 months ago

I think D) Cloud Data Fusion is the best choice for our needs.

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Selma

9 months ago

I agree with Dorothea, Cloud Data Fusion seems like the best option to lower costs and reduce repetitive work.

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Gail

9 months ago

I agree, D) Cloud Data Fusion is the way to go. It's designed to handle data quality and security challenges across multiple data sources.

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Tula

8 months ago

D) Cloud Data Fusion offers a codeless interface for ETL processes.

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Pamella

8 months ago

B) Dataprep is more focused on data preparation and cleaning.

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Freeman

9 months ago

A) Dataflow is a good option for real-time data processing.

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Dorothea

9 months ago

I prefer Cloud Data Fusion because it offers a codeless interface for ETL processes.

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Leota

10 months ago

D) Cloud Data Fusion seems like the best option to me. It's a fully managed, cloud-native data integration service that can help us lower costs and reduce repetitive work.

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Johnetta

8 months ago

A) Dataflow and Cloud Data Fusion both seem like strong contenders for our needs.

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Matilda

9 months ago

D) Cloud Data Fusion is definitely the way to go for a fully managed, cloud-native data integration service.

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Rima

9 months ago

D) Cloud Data Fusion is definitely worth considering for a fully managed, cloud-native data integration service.

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Stephen

9 months ago

A) Dataflow could be a good option for a codeless interface for building ETL processes.

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Cristen

9 months ago

B) Dataprep could also be a good choice for simplifying the data preparation process.

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Kristine

10 months ago

A) Dataflow sounds like a good option for a codeless interface for building ETL processes.

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Levi

10 months ago

I think we should use Dataflow for building a unified analytics environment.

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