Google Exam Professional-Data-Engineer Topic 4 Question 72 Discussion

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

Question #: 72
Topic #: 4

[All Google Cloud Certified Professional Data Engineer Questions]

You are migrating a table to BigQuery and are deeding on the data model. Your table stores information related to purchases made across several store locations and includes information like the time of the transaction, items purchased, the store ID and the city and state in which the store is located You frequently query this table to see how many of each item were sold over the past 30 days and to look at purchasing trends by state city and individual store. You want to model this table to minimize query time and cost. What should you do?

APartition by transaction time; cluster by state first, then city then store ID

BPartition by transaction tome cluster by store ID first, then city, then stale

CTop-level cluster by stale first, then city then store

DTop-level cluster by store ID first, then city then state.

Show Suggested Answer

Suggested Answer: C

by Allene at Oct 21, 2023, 04:55 AM

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Francine

6 months ago

You know, I was leaning towards option A at first, but now I'm not so sure. I think Aide makes a good point about the importance of being able to quickly query across states and cities. This is a tough decision!

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Aide

6 months ago

Ah, I see your point Mabel, but I'm not convinced. What if we need to quickly look at trends across all stores in a given state or city? Wouldn't it be better to have that as the top-level clustering?

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Mabel

6 months ago

I disagree, I think option B is the way to go. Partitioning by transaction time makes sense since we're often querying recent data, and clustering by store ID first will help us quickly find all the purchases for a given store.

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Yvonne

6 months ago

Hmm, this is an interesting question. I think the key here is to optimize for the types of queries we'll be running on this table. Since we're frequently looking at purchasing trends by state, city, and store, I'm leaning towards option C - top-level cluster by state, then city, then store ID.

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