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Google Professional Data Engineer Exam - Topic 4 Question 13 Discussion

You work for an advertising company, and you've developed a Spark ML model to predict click-through rates at advertisement blocks. You've been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?
A) Use Cloud ML Engine for training existing Spark ML models
B) Rewrite your models on TensorFlow, and start using Cloud ML Engine
C) Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
D) Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery

Google Professional Data Engineer Exam - Topic 4 Question 13 Discussion

Actual exam question for Google's Professional Data Engineer exam
Question #: 13
Topic #: 4
[All Professional Data Engineer Questions]

You work for an advertising company, and you've developed a Spark ML model to predict click-through rates at advertisement blocks. You've been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?

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Suggested Answer: A

by Erick at May 04, 2022, 03:36 AM

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Rosendo
9 months ago
Can we really trust BigQuery for all our data needs?
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Elenora
9 months ago
Definitely go with option C, it makes the most sense for Spark.
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Vicente
9 months ago
Surprised that Cloud ML Engine isn't the go-to choice for this.
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Clare
9 months ago
I disagree, rewriting everything in TensorFlow seems like a lot of work!
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Georgeanna
9 months ago
I think using Cloud Dataproc is the best option here.
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Tonja
10 months ago
I have a feeling option A might not be the right fit since Cloud ML Engine is more for TensorFlow models. I think we need to stick with Spark for our existing models.
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Annmarie
10 months ago
I remember practicing a question about migrating models, and I feel like option D could be a good choice since it allows us to keep using Spark. But isn't there a cost concern with spinning up a cluster?
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Refugia
10 months ago
I think option C sounds familiar since we discussed using Cloud Dataproc for Spark jobs in class. But I'm not entirely sure if reading directly from BigQuery is the best approach.
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Alonso
10 months ago
I’m a bit confused about option B. I know TensorFlow is powerful, but rewriting everything seems like a lot of work. Would it really be worth it?
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Filiberto
10 months ago
Hmm, this is a tricky one. I think the key is figuring out what's causing the issue with the minutes option not being visible to the user. Maybe it has something to do with the permissions or the license type.
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Alba
10 months ago
This seems like a straightforward question about the UiPath Robotic Enterprise Framework. I'm pretty confident I can figure this out.
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Marica
10 months ago
I practiced a similar question on Control Plane Policing last week. If I recall correctly, the right configuration typically specifies the class-map correctly.
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Yong
10 months ago
This looks like a data classification matrix, so I'll go with option C.
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