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?

APartition table data by create_date, location_id and device_version

BPartition table data by create_date cluster table data by tocation_id and device_version

CCluster table data by create_date location_id and device_version

DCluster table data by create_date, partition by location and device_version

Show Suggested Answer

Suggested Answer: C

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

Limited Time Offer

25%

Off

Get Premium Professional Data Engineer Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Maryann

3 months ago

Not sure about D, sounds a bit complicated for what we need.

upvoted 0 times

...

Flo

4 months ago

Wait, can you really partition by multiple fields like that?

upvoted 0 times

...

Kattie

4 months ago

C doesn't really address cost optimization, right?

upvoted 0 times

...

Rodolfo

4 months ago

I think A could work too, but B seems more efficient.

upvoted 0 times

...

Staci

4 months ago

B is the best option for optimizing cost and performance.

upvoted 0 times

...

Antione

5 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.

upvoted 0 times

...

Kallie

5 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.

upvoted 0 times

...

Ivan

5 months ago

I think option B sounds familiar; it mentions both partitioning and clustering, which might be the best approach for optimizing queries.

upvoted 0 times

...

Bobbye

5 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.

upvoted 0 times

...

Brent

5 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?

upvoted 0 times

...

Staci

5 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.

upvoted 0 times

...

Makeda

5 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.

upvoted 0 times

...

Alisha

5 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.

upvoted 0 times

...

Brittney

5 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.

upvoted 0 times

...

Shalon

5 months ago

I'm pretty sure the formula is (FVFA i, n-1 + 1) * annuity, so I'll go with option B.

upvoted 0 times

...

Jamey

5 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.

upvoted 0 times

...

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.

upvoted 0 times

Tayna

2 years ago

C: Good point, we should weigh the benefits of both before making a decision.

upvoted 0 times

...

Rebbecca

2 years ago

B: True, but we should consider the potential for data skew with clustering.

upvoted 0 times

...

Rory

2 years ago

A: It could, but partitioning can also help with organizing the data efficiently.

upvoted 0 times

...

Cordie

2 years ago

D: I think clustering would further improve query performance.

upvoted 0 times

...

Amie

2 years ago

C: But what about clustering the table data by create_date, location_id and device_version?

upvoted 0 times

...

Shakira

2 years ago

B: I agree, that would help optimize the queries for cost and performance.

upvoted 0 times

...

Dalene

2 years ago

A: You should partition table data by create_date, location_id and device_version.

upvoted 0 times

...

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.

upvoted 0 times

...

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.

upvoted 0 times

...

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?

upvoted 0 times

...