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Exam Professional Cloud Architect topic 8 question 9 discussion

Actual exam question from Google's Professional Cloud Architect
Question #: 9
Topic #: 8
[All Professional Cloud Architect Questions]

TerramEarth has equipped all connected trucks with servers and sensors to collect telemetry data. Next year they want to use the data to train machine learning models. They want to store this data in the cloud while reducing costs.
What should they do?

  • A. Have the vehicle's computer compress the data in hourly snapshots, and store it in a Google Cloud Storage (GCS) Nearline bucket
  • B. Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Google BigQuery
  • C. Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Cloud Bigtable
  • D. Have the vehicle's computer compress the data in hourly snapshots, and store it in a GCS Coldline bucket
Show Suggested Answer Hide Answer
Suggested Answer: D 🗳️
Storage is the best choice for data that you plan to access at most once a year, due to its slightly lower availability, 90-day minimum storage duration, costs for data access, and higher per-operation costs. For example:
Cold Data Storage - Infrequently accessed data, such as data stored for legal or regulatory reasons, can be stored at low cost as Coldline Storage, and be available when you need it.
Disaster recovery - In the event of a disaster recovery event, recovery time is key. Cloud Storage provides low latency access to data stored as Coldline Storage.
Reference:
https://cloud.google.com/storage/docs/storage-classes

Comments

Chosen Answer:
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JoeShmoe
Highly Voted 4 years, 4 months ago
D is most cost effective as don't want to use until 'next year'
upvoted 33 times
tartar
3 years, 7 months ago
D is ok
upvoted 8 times
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nitinz
3 years ago
D is most cost effective
upvoted 2 times
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HCL
3 years ago
Hourly snapshots in answer D does not make any sense. The answer is B.
upvoted 1 times
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Deb2293
Most Recent 1 year ago
Selected Answer: D
If the words 'next year' wouldn't have been there then Big Table đź’Ż . But as it will be required next year so Coldline bucket would be the most cost effective solution.
upvoted 1 times
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omermahgoub
1 year, 3 months ago
One option that TerramEarth could consider is storing the telemetry data in a Google Cloud Storage (GCS) Nearline bucket. This would allow them to store the data in the cloud at a lower cost than other storage options, while still providing quick access to the data when needed. By having the vehicle's computer compress the data in hourly snapshots, they can reduce the amount of storage needed and further reduce costs.
upvoted 2 times
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surajkrishnamurthy
1 year, 3 months ago
Selected Answer: D
D is the correct answer Clue is "next year they want to use the data" Therefore moving the data to coldline storage makes more sense
upvoted 4 times
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megumin
1 year, 4 months ago
Selected Answer: D
ok for D
upvoted 1 times
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Mahmoud_E
1 year, 5 months ago
Selected Answer: D
D is the correct answer
upvoted 1 times
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AzureDP900
1 year, 8 months ago
I miss the point about cost optimization and I thought C is right. After reading the discussions I realized D is right answer. I am going with D
upvoted 1 times
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Aiffone
2 years, 2 months ago
Big query does it. B...when it's long term storage, it costs same as coldline https://cloud.google.com/bigquery/docs/best-practices-storage
upvoted 1 times
Wonka
2 years, 2 months ago
It cost same as nearline when not accessed, but coldline is cheaper than BQ
upvoted 4 times
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Aiffone
2 years, 2 months ago
the highlight here is machine learning and not disaster recovery or data arhiving which is what coldline storages are for. You also dont pay for datawarehousing in bigquery until you read from it for machine learning. So its cheap and good for ML. i go with B
upvoted 1 times
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vincy2202
2 years, 3 months ago
D is the correct answer
upvoted 1 times
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mgm7
2 years, 3 months ago
D makes sense IF the "computer" on the vehicle can compress data and can take snapshots. Are we supposed to to assume that these "computers" have snapshot capability though it is no stated anywhere in the question? Yet, if magically this was possible, this is the correct answer. If this indeed is the correct answer then the only logical deduction is that the questions is stated horribly. I only can hope the real exam isn't like this.
upvoted 1 times
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joe2211
2 years, 4 months ago
Selected Answer: D
vote D
upvoted 2 times
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MaxNRG
2 years, 5 months ago
D – Have vehicle’s computer compress data in hourly snapshots, and store in GCS Coldline bucket. A – doesn’t work, since Nearline is more expensive than Coldline in D (0.01$ vs 0.007$ GB/month). B / C – stores compressed data in relational DB, which may not be possible. Even it is implemented, then B (BigQuery) is more expensive than Cloud Storage Coldline (0.01$ vs 0.007$ GB/month) C – Bigtable is most expensive option (0.026$ GB/month) and also it is not integrated with Cloud ML (Dataflow, BiqQuery and Cloud Storage are integrated) D – Coldline fits perfectly – blob storage, cheapest price, integration with ML
upvoted 3 times
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victory108
2 years, 8 months ago
D. Have the vehicleג€™s computer compress the data in hourly snapshots, and store it in a GCS Coldline bucket
upvoted 1 times
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MamthaSJ
2 years, 8 months ago
Answer is D
upvoted 2 times
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Ausias18
2 years, 12 months ago
Answer is D
upvoted 1 times
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lynx256
3 years ago
IMO B and C are not correct because in both of them there is "[...] compress the data [...]" - and neiter BigQuery nor Bigtable are suitable for "compressed data". D is cheaper than A -- so I'll go with D .
upvoted 4 times
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Community vote distribution
A (35%)
C (25%)
B (20%)
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