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Google Professional Machine Learning Engineer Exam - Topic 2 Question 30 Discussion

You have a demand forecasting pipeline in production that uses Dataflow to preprocess raw data prior to model training and prediction. During preprocessing, you employ Z-score normalization on data stored in BigQuery and write it back to BigQuery. New training data is added every week. You want to make the process more efficient by minimizing computation time and manual intervention. What should you do?
B) Translate the normalization algorithm into SQL for use with BigQuery
A) Normalize the data using Google Kubernetes Engine
C) Use the normalizer_fn argument in TensorFlow's Feature Column API
D) Normalize the data with Apache Spark using the Dataproc connector for BigQuery

Google Professional Machine Learning Engineer Exam - Topic 2 Question 30 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 30
Topic #: 2
[All Professional Machine Learning Engineer Questions]

You have a demand forecasting pipeline in production that uses Dataflow to preprocess raw data prior to model training and prediction. During preprocessing, you employ Z-score normalization on data stored in BigQuery and write it back to BigQuery. New training data is added every week. You want to make the process more efficient by minimizing computation time and manual intervention. What should you do?

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Suggested Answer: B

by Lawrence at May 09, 2022, 05:33 AM

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Cordelia
9 months ago
C is cool, but isn't it more for model training than preprocessing?
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Mammie
9 months ago
D might be overkill for just normalization, right?
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Clare
9 months ago
Wait, can you really do normalization in SQL? Sounds interesting!
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Lucille
9 months ago
I disagree, A could offer more flexibility with Kubernetes.
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Georgeanna
9 months ago
B seems like a solid choice, keeps it all in BigQuery.
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Yaeko
10 months ago
I feel like normalizing in Kubernetes could add unnecessary complexity, but I can't recall the exact reasons why.
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Micah
10 months ago
I practiced a similar question where using Apache Spark with Dataproc was highlighted as a way to handle large datasets efficiently.
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Dorsey
10 months ago
I'm not entirely sure, but I think using TensorFlow's Feature Column API might be more suited for model training rather than preprocessing in this case.
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Vanesa
10 months ago
I remember we discussed how using SQL for normalization could streamline the process since it's directly integrated with BigQuery.
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Latosha
10 months ago
Wait, is this asking about paraphrasing in a customer service context? I was thinking more generally. Let me re-read the question and options carefully to make sure I'm on the right track.
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Dacia
10 months ago
Implementing Windows AutoPilot and deploying policies through Intune seem like the most efficient ways to standardize the device deployments and minimize costs. I'll focus on those two options.
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Micah
10 months ago
Okay, let me see. The Session layer is all about managing the communication session, so an application-level firewall that inspects the actual application data seems like the right choice here. I'm feeling good about B.
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