Exam Professional Cloud Database Engineer topic 1 question 46 discussion

Actual exam question from Google's Professional Cloud Database Engineer

Question #: 46
Topic #: 1

[All Professional Cloud Database Engineer Questions]

You are choosing a database backend for a new application. The application will ingest data points from IoT sensors. You need to ensure that the application can scale up to millions of requests per second with sub-10ms latency and store up to 100 TB of history. What should you do?

A. Use Cloud SQL with read replicas for throughput.
B. Use Firestore, and rely on automatic serverless scaling.
C. Use Memorystore for Memcached, and add nodes as necessary to achieve the required throughput.
D. Use Bigtable, and add nodes as necessary to achieve the required throughput.

Show Suggested Answer

Suggested Answer: D 🗳️

by fredcaram at Dec. 20, 2022, 11:57 p.m.

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fredcaram

Highly Voted 2 years, 6 months ago

Selected Answer: D

This is a bigtable use case

upvoted 7 times

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hussain.sain

Most Recent 1 year ago

point to consider is : IOT data, scaling for accepting millions of request. storing historic data of 100TB. Big Table is used for low latency but its for operational workload and we need Fully managed Redis and Memcached for sub-millisecond data access. that's the only argument here. i think option c is correct.

upvoted 1 times

hussain.sain

1 year ago

https://medium.com/google-cloud/which-database-should-i-choose-44be039179ea

upvoted 1 times

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theseawillclaim

1 year, 8 months ago

Selected Answer: D

Key is 100TB of historical data. BigTable is the only service (among the mentioned) that is able to handle that load within the required latency.

upvoted 1 times

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fsmathias

2 years ago

Selected Answer: C

Correct answer is C

upvoted 1 times

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dynamic_dba

2 years, 3 months ago

D. Simply a classic use case for Bigtable. Neither Cloud SQL, Firestore nor Memorystore have either the capacity or latency to provide the solution.

upvoted 2 times

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lepach

2 years, 5 months ago

https://cloud.google.com/memorystore/docs/redis/redis-overview#what_its_good_for What it's good for: Stream Processing: Whether processing a Twitter feed or stream of data from IoT devices, Memorystore for Redis is a perfect fit for streaming solutions. Correct answer is C

upvoted 1 times