Google Professional Cloud Database Engineer Exam - Topic 3 Question 49 Discussion

Actual exam question for Google's Professional Cloud Database Engineer exam

Question #: 49
Topic #: 3

[All Professional Cloud Database Engineer Questions]

Your organization stores marketing data such as customer preferences and purchase history on Bigtable. The consumers of this database are predominantly data analysts and operations users. You receive a service ticket from the database operations department citing poor database performance between 9 AM-10 AM every day. The application team has confirmed no latency from their logs. A new cohort of pilot users that is testing a dataset loaded from a third-party data provider is experiencing poor database performance. Other users are not affected. You need to troubleshoot the issue. What should you do?

AIsolate the data analysts and operations user groups to use different Bigtable instances.

BCheck the Cloud Monitoring table/bytes_used metric from Bigtable.

CUse Key Visualizer for Bigtable.

DAdd more nodes to the Bigtable cluster.

Show Suggested Answer

Suggested Answer: C

https://cloud.google.com/sql/docs/postgres/replication/cross-region-replicas#disaster_recovery

by Howard at Dec 07, 2024, 03:14 AM

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Nada

6 days ago

Definitely agree, metrics can reveal a lot about performance issues.

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Tegan

12 days ago

I think checking the Cloud Monitoring metrics is a solid first step.

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Arlene

17 days ago

Adding more nodes seems like a quick fix, but I wonder if it would actually solve the problem or just mask it temporarily.

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Leonida

23 days ago

I feel like using Key Visualizer could give us insights into the data access patterns, but I can't recall if it directly addresses performance issues.

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Leonida

28 days ago

Checking the Cloud Monitoring metrics sounds familiar; I think it could help identify if there's a resource issue.

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Louvenia

1 month ago

I remember we discussed isolating user groups in a similar practice question, but I'm not sure if that's the best first step here.

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Sunshine

1 month ago

This seems straightforward to me. The new pilot users are the ones experiencing the performance issues, so I'd start by isolating them to a separate Bigtable instance. That should help narrow down the problem.

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Leila

1 month ago

I've seen issues like this before with Bigtable. My strategy would be to use the Key Visualizer tool to get a better understanding of the data distribution and potential hotspots. That should help point me in the right direction.

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Vilma

1 month ago

I'm a bit confused by this question. There are a few different options, but I'm not sure which one is the best approach. Maybe I'll try to eliminate the obvious wrong answers first.

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Tony

1 month ago

Okay, let's see. The key here is to isolate the issue and figure out what's causing the performance problems for the new pilot users. I think I'll start by checking the Cloud Monitoring metrics.

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Solange

1 month ago

Hmm, this seems like a tricky one. I'll need to think through the different options carefully to figure out the best approach.

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Callie

6 months ago

Hmm, I bet the new pilot users are all trying to access the same dataset at the same time. Key Visualizer to the rescue!

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Fatima

5 months ago

User 3: Once we pinpoint the issue, we can add more nodes to the Bigtable cluster if needed.

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Brande

5 months ago

User 2: Good idea. We can use Key Visualizer to identify any hotspots causing the poor performance.

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Hillary

5 months ago

User 1: Let's check the Cloud Monitoring table/bytes_used metric from Bigtable.

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Karol

7 months ago

I'm going to go with the Key Visualizer. It's the most targeted tool for identifying data skew or hotspots, which sounds like the likely culprit here.

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Pedro

5 months ago

User 3: Adding more nodes to the Bigtable cluster could also help improve performance.

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Mirta

5 months ago

Lazaro: Let's try Key Visualizer first and see if that helps pinpoint the problem.

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Yun

5 months ago

User 3: Adding more nodes to the Bigtable cluster could also help with the poor performance issue.

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Johnna

5 months ago

User 2: I'm going to go with the Key Visualizer. It's the most targeted tool for identifying data skew or hotspots, which sounds like the likely culprit here.

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Lazaro

6 months ago

User 2: I think Key Visualizer might be more helpful in this situation. It can identify data skew or hotspots.

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Nohemi

6 months ago

User 1: Have you checked the Cloud Monitoring table/bytes_used metric from Bigtable?

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Brittney

6 months ago

User 1: Have you tried checking the Cloud Monitoring table/bytes_used metric from Bigtable?

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Delsie

7 months ago

Isolating the user groups is an interesting idea, but it feels like overkill for this scenario. I'd try the Key Visualizer and Bigtable metrics first before resorting to that.

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Fausto

6 months ago

Let's start by checking the Cloud Monitoring table/bytes_used metric from Bigtable and using Key Visualizer.

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Valentin

6 months ago

I agree, isolating the user groups seems like a drastic solution.

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Yolando

7 months ago

Definitely check the Bigtable metrics first to see if the table is hitting any resource limits. That could explain the daily performance spike.

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Isadora

7 months ago

The issue seems to be isolated to the new cohort of pilot users, so I'd start by checking the Key Visualizer to see if there's any hot-spotting or uneven data distribution causing the performance degradation.

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