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Google Professional Data Engineer Exam - Topic 3 Question 114 Discussion

Actual exam question for Google's Professional Data Engineer exam
Question #: 114
Topic #: 3
[All Professional Data Engineer Questions]

You have a data pipeline with a Dataflow job that aggregates and writes time series metrics to Bigtable. You notice that data is slow to update in Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat

a. What should you do?

Choose 2 answers

Show Suggested Answer Hide Answer
Suggested Answer: D, E

https://cloud.google.com/bigtable/docs/performance#performance-write-throughput

https://cloud.google.com/dataflow/docs/reference/pipeline-options


by Robt at Nov 22, 2025, 08:05 PM

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Claribel
9 hours ago
I think using the CoGroupByKey transform is a good idea too.
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Latrice
6 days ago
Totally agree, more nodes means better handling of concurrent users!
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Edward
11 days ago
Increasing the number of nodes in the Bigtable cluster can help with performance.
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Ngoc
16 days ago
E) is a must, but I'd also try B) just for fun. Flatten that data!
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Willard
21 days ago
A) and D) - local execution and more workers? What is this, amateur hour?
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Vallie
26 days ago
I'd go with C) and E). CoGroupByKey and more Bigtable nodes will make this pipeline fly!
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Danica
1 month ago
D) and E) are the way to go. Increase the workers and the Bigtable cluster size.
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Denny
1 month ago
B) and E) should do the trick. Flatten and more Bigtable nodes should help with the slow updates.
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Jeff
1 month ago
I’m a bit confused about the Flatten transform; I thought it was more for restructuring data rather than improving write performance. Not sure if option B is the right call.
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Marge
2 months ago
I practiced a similar question about optimizing data pipelines, and I feel like increasing the Bigtable cluster nodes could definitely improve write speeds. So, option E seems plausible.
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Kenneth
2 months ago
I'm not entirely sure, but I think using the CoGroupByKey transform could help with aggregating data more efficiently. That might be option C?
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Regenia
2 months ago
I'm a bit confused by the Flatten transform option. That doesn't seem directly related to the performance problem. I'm going to go with increasing the Dataflow workers and the Bigtable cluster size - those seem like the most straightforward ways to scale up the system.
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Antonio
2 months ago
I remember something about increasing the number of workers in Dataflow to handle more concurrent users, so maybe option D is a good choice.
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Mollie
2 months ago
Okay, let's think this through. Configuring local execution for Dataflow probably won't help with the Bigtable performance issues. I'm leaning towards the Bigtable cluster size increase and using CoGroupByKey to optimize the data writes.
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Remona
3 months ago
But B could complicate the pipeline. D and E seem safer.
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Gabriele
3 months ago
I think D and E are the best options. More workers and nodes can really help.
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Angella
3 months ago
Hmm, the question is asking for two answers, so I'll need to pick carefully. I think the CoGroupByKey transform and increasing the Bigtable cluster size are the best options to try.
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Camellia
3 months ago
I'm not sure about the Flatten transform, but increasing the number of Dataflow workers and Bigtable nodes sounds like a good way to scale up the pipeline and handle more concurrent users.
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Lorean
2 months ago
I agree, more workers can definitely help.
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