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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?

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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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Justine
6 months ago
Not sure if isolating user groups will really solve the problem.
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Freeman
6 months ago
Wait, why are only the pilot users affected? That’s weird!
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Rose
6 months ago
Adding more nodes seems like overkill without checking the metrics first.
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Nada
7 months ago
Definitely agree, metrics can reveal a lot about performance issues.
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Tegan
7 months ago
I think checking the Cloud Monitoring metrics is a solid first step.
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Arlene
7 months 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
7 months 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
7 months 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
8 months 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
8 months 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
8 months 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
8 months 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
8 months 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
8 months 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
1 year 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
11 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
11 months ago
User 2: Good idea. We can use Key Visualizer to identify any hotspots causing the poor performance.
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Hillary
12 months ago
User 1: Let's check the Cloud Monitoring table/bytes_used metric from Bigtable.
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Karol
1 year 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
12 months ago
User 3: Adding more nodes to the Bigtable cluster could also help improve performance.
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Mirta
12 months ago
Lazaro: Let's try Key Visualizer first and see if that helps pinpoint the problem.
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Yun
12 months ago
User 3: Adding more nodes to the Bigtable cluster could also help with the poor performance issue.
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Johnna
12 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
1 year 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
1 year ago
User 1: Have you checked the Cloud Monitoring table/bytes_used metric from Bigtable?
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Brittney
1 year ago
User 1: Have you tried checking the Cloud Monitoring table/bytes_used metric from Bigtable?
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Delsie
1 year 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
1 year ago
Let's start by checking the Cloud Monitoring table/bytes_used metric from Bigtable and using Key Visualizer.
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Valentin
1 year ago
I agree, isolating the user groups seems like a drastic solution.
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Yolando
1 year 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
1 year 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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Ashanti
1 year ago
Adding more nodes to the Bigtable cluster could also be a good solution to improve performance.
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Floyd
1 year ago
I agree with Aron. That could help us identify the root cause of the poor performance.
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Aron
1 year ago
I think we should check the Cloud Monitoring table/bytes_used metric from Bigtable.
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