Google Exam Professional-Data-Engineer Topic 4 Question 76 Discussion

Actual exam question for Google's Google Cloud Certified Professional Data Engineer exam

Question #: 76
Topic #: 4

[All Google Cloud Certified Professional Data Engineer Questions]

You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a

machine-learning process. You want to support a logistic regression model. You also need to monitor and

adjust for null values, which must remain real-valued and cannot be removed. What should you do?

AUse Cloud Dataprep to find null values in sample source data. Convert all nulls to 'none' using a Cloud
Dataproc job.

BUse Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud
Dataprep job.

CUse Cloud Dataflow to find null values in sample source data. Convert all nulls to 'none' using a Cloud
Dataprep job.

DUse Cloud Dataflow to find null values in sample source data. Convert all nulls to using a custom script.

Show Suggested Answer

Suggested Answer: C

by Lorrie at Jan 23, 2024, 01:07 AM

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Ozell

10 days ago

I'm not sure about using a custom script, though. It could be more error-prone and harder to maintain than using a managed service like Cloud Dataprep or Cloud Dataflow. Maybe we could explore those options further.

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Kristeen

11 days ago

Exactly! Gotta do it right, even if it means a little more effort. I'd rather spend the time getting the data prepped properly than have to troubleshoot issues with the model later on. *nods sagely*

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Walker

12 days ago

Ah, I see what you mean. Converting nulls to 0 could definitely cause issues if 0 is a valid value. Hmm, maybe we should go with the custom script after all, even if it's a bit more work. Can't risk messing up the model, you know?

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Brigette

13 days ago

Haha, yeah, the old 'measure twice, cut once' approach. Wise words, my friends. Alright, let's go with option D and figure out a solid custom script to handle those nulls. Can't be that hard, right? *winks*

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