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Google Professional Cloud Database Engineer Exam - Topic 5 Question 76 Discussion

Your ecommerce website captures user clickstream data to analyze customer traffic patterns in real time and support personalization features on your website. You plan to analyze this data using big data tools. You need a low-latency solution that can store 8 TB of data and can scale to millions of read and write requests per second. What should you do?
A) Write your data into Bigtable and use Dataproc and the Apache Hbase libraries for analysis.
B) Deploy a Cloud SQL environment with read replicas for improved performance. Use Datastream to export data to Cloud Storage and analyze with Dataproc and the Cloud Storage connector.
C) Use Memorystore to handle your low-latency requirements and for real-time analytics.
D) Stream your data into BigQuery and use Dataproc and the BigQuery Storage API to analyze large volumes of data.

Google Professional Cloud Database Engineer Exam - Topic 5 Question 76 Discussion

Actual exam question for Google's Professional Cloud Database Engineer exam
Question #: 76
Topic #: 5
[All Professional Cloud Database Engineer Questions]

Your ecommerce website captures user clickstream data to analyze customer traffic patterns in real time and support personalization features on your website. You plan to analyze this data using big data tools. You need a low-latency solution that can store 8 TB of data and can scale to millions of read and write requests per second. What should you do?

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Suggested Answer: A

Start with the lowest tier and smallest size and then grow your instance as needed. Memorystore provides automated scaling using APIs, and optimized node placement across zones for redundancy. Memorystore for Memcached can support clusters as large as 5 TB, enabling millions of QPS at very low latency


by Mammie at Aug 18, 2026, 07:46 AM

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Dulce
3 days ago
Not sure if Memorystore is the best fit for 8 TB of data.
upvoted 0 times
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Blair
8 days ago
Wait, can BigQuery really handle millions of requests?
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Blythe
14 days ago
Definitely leaning towards BigQuery for this one.
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Alishia
19 days ago
I think Cloud SQL might struggle with that scale.
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Rodolfo
24 days ago
Bigtable is great for handling large datasets!
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Arletta
29 days ago
Memorystore sounds like it could work for low-latency needs, but I don't recall if it can handle the scale of 8 TB effectively.
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Laurena
1 month ago
I practiced a similar question where we had to choose between Cloud SQL and Bigtable, and I feel like Bigtable might be more suited for high write loads.
upvoted 0 times
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Stefania
1 month ago
I think using BigQuery could be a good option since it can scale well, but I'm not entirely confident about the specifics of the Storage API.
upvoted 0 times
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Abel
1 month ago
I remember we discussed Bigtable for handling large datasets, but I'm not sure if it's the best choice for real-time analytics.
upvoted 0 times
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