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Google Professional Data Engineer Exam - Topic 4 Question 74 Discussion

You are collecting loT sensor data from millions of devices across the world and storing the data in BigQuery. Your access pattern is based on recent data tittered by location_id and device_version with the following query:You want to optimize your queries for cost and performance. How should you structure your data?
C) Cluster table data by create_date location_id and device_version
A) Partition table data by create_date, location_id and device_version
B) Partition table data by create_date cluster table data by tocation_id and device_version
D) Cluster table data by create_date, partition by location and device_version

Google Professional Data Engineer Exam - Topic 4 Question 74 Discussion

Actual exam question for Google's Professional Data Engineer exam
Question #: 74
Topic #: 4
[All Professional Data Engineer Questions]

You are collecting loT sensor data from millions of devices across the world and storing the data in BigQuery. Your access pattern is based on recent data tittered by location_id and device_version with the following query:

You want to optimize your queries for cost and performance. How should you structure your data?

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Suggested Answer: C

by Louisa at Dec 13, 2023, 07:16 PM

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Maryann
6 months ago
Not sure about D, sounds a bit complicated for what we need.
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Flo
6 months ago
Wait, can you really partition by multiple fields like that?
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Kattie
7 months ago
C doesn't really address cost optimization, right?
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Rodolfo
7 months ago
I think A could work too, but B seems more efficient.
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Staci
7 months ago
B is the best option for optimizing cost and performance.
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Antione
7 months ago
I’m leaning towards option A, but I’m a bit uncertain about whether just partitioning by create_date is enough for performance.
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Kallie
8 months ago
I feel like I’ve seen a similar question before, but I can’t recall if clustering should come before partitioning or vice versa.
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Ivan
8 months ago
I think option B sounds familiar; it mentions both partitioning and clustering, which might be the best approach for optimizing queries.
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Bobbye
8 months ago
I remember we discussed partitioning and clustering in class, but I'm not sure if I should prioritize one over the other here.
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Brent
8 months ago
I'm a little confused by the difference between partitioning and clustering. Can someone help me understand which one would be better for this use case?
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Staci
8 months ago
Okay, I think I've got a strategy here. The key is to optimize for the query pattern, which is filtering by location_id and device_version. Partitioning by those columns seems like the way to go.
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Makeda
8 months ago
Hmm, I'm a bit unsure about this one. Partitioning and clustering can both be effective, but I'll need to carefully consider the tradeoffs between the options presented.
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Alisha
8 months ago
This looks like a pretty straightforward data optimization question. I'd start by analyzing the query pattern and thinking about how to structure the data to improve performance and cost.
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Brittney
8 months ago
This is a great opportunity to demonstrate my understanding of BigQuery optimization techniques. I'll carefully evaluate each option and choose the one that best fits the access pattern described in the question.
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Shalon
8 months ago
I'm pretty sure the formula is (FVFA i, n-1 + 1) * annuity, so I'll go with option B.
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Jamey
8 months ago
Okay, let's think this through step-by-step. I believe the key is configuring the item group setup and item model group setup properly.
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Terrilyn
2 years ago
That's a good point, Candida. I was also considering option B, but I'm a little concerned about the potential for data skew if some locations or device versions are much more heavily used than others.
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Tayna
2 years ago
C: Good point, we should weigh the benefits of both before making a decision.
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Rebbecca
2 years ago
B: True, but we should consider the potential for data skew with clustering.
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Rory
2 years ago
A: It could, but partitioning can also help with organizing the data efficiently.
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Cordie
2 years ago
D: I think clustering would further improve query performance.
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Amie
2 years ago
C: But what about clustering the table data by create_date, location_id and device_version?
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Shakira
2 years ago
B: I agree, that would help optimize the queries for cost and performance.
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Dalene
2 years ago
A: You should partition table data by create_date, location_id and device_version.
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Candida
2 years ago
Hmm, let me think this through. I'm leaning towards option B because partitioning by create_date and clustering by location_id and device_version seems like it could give us the best of both worlds in terms of querying efficiency.
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Hyman
2 years ago
Haha, this is starting to sound like a real-life engineering meeting. I'm glad we're all putting in the effort to think this through carefully.
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Cassie
2 years ago
Ah, good catch, Michael. That's a really important consideration. Maybe option D could be a better choice, with clustering by create_date and partitioning by location and device_version?
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