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Google Professional Data Engineer Exam - Topic 1 Question 112 Discussion

Actual exam question for Google's Professional Data Engineer exam
Question #: 112
Topic #: 1
[All Professional Data Engineer Questions]

A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions. You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL 'dataset.model', table user_features). How should you create the ML pipeline?

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Suggested Answer: D

by Lashandra at Aug 14, 2025, 03:56 AM

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Verlene
4 days ago
I practiced a similar question about using Cloud Dataflow, but I can't recall if it's the best option here since we need predictions for a single user.
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Katina
10 days ago
I think creating an Authorized View could be a good approach since it simplifies access control, but I’m not entirely sure how it impacts performance.
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Jesusa
15 days ago
I remember we discussed the importance of latency in serving predictions, but I'm not sure if adding a WHERE clause is enough to meet the 100 ms requirement.
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Jennifer
21 days ago
This is a tricky one. I'm leaning towards Option C with the Cloud Dataflow pipeline. That way, I can control the entire data processing flow and ensure the latency requirements are met. The Dataflow Worker role should give the application the necessary access.
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Melinda
26 days ago
Okay, I think I've got a strategy here. Option B with the Authorized View seems like the best approach. That way, I can grant the necessary permissions to the application service account and the query will be optimized for low latency predictions.
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Gail
1 month ago
Hmm, I'm a bit confused. The question mentions a REST API application, but it's not clear how that fits into the pipeline. I'll need to think through the different options and how they address the latency requirement.
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Frederic
1 month ago
This seems like a straightforward question, but I want to make sure I understand the requirements correctly. The key is to create a pipeline that can serve predictions for individual users with low latency.
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Precious
2 months ago
I think option D is the way to go. Using Cloud Dataflow to read predictions for all users and writing them to Cloud Bigtable will allow for efficient access to individual user predictions.
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Lashaun
2 months ago
I'm leaning towards option C. Creating a Cloud Dataflow pipeline using BigQueryIO seems like a scalable solution for serving predictions with low latency.
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Denny
2 months ago
I disagree, I believe option B is the best choice. Creating an Authorized View will provide the necessary access to the application service account.
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Leigha
3 months ago
You know, I was thinking the same thing as Avery. The Dataflow pipeline seems like the best way to ensure low latency and high performance, even with a large dataset. Option C is my pick.
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Avery
3 months ago
Hmm, I'm not sure about that. What if the dataset is really large? Won't that lead to performance issues? I'd go with Option C and use Dataflow to handle the reading and processing.
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Amina
2 months ago
I agree, using Dataflow for reading and processing will help with performance issues.
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Shawna
2 months ago
Option C seems like the best choice. Dataflow can handle large datasets efficiently.
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Ceola
3 months ago
I think we should go with option A. Adding a WHERE clause to the query seems like the most efficient way to serve predictions for individual user IDs.
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Marge
3 months ago
Option B is the way to go. Creating an Authorized View and sharing the dataset with the application service account is the most efficient and scalable solution here.
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Dalene
2 months ago
B) Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account.
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