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?
Since the data will not be used until next year, cost efficiency is crucial. Google Cloud Storage Coldline is specifically designed for data that is accessed infrequently, which aligns with this use case. By compressing the data in hourly snapshots, the amount of storage needed is minimized, further reducing costs. Coldline offers the lowest cost per GB compared to Nearline, BigQuery, and Bigtable, making it the most suitable option for long-term, infrequent access data storage.
D is most cost effective as don't want to use until 'next year'
D is ok
D is most cost effective
Hourly snapshots in answer D does not make any sense. The answer is B.
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
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
Answer is D
vote D
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.
Answer is D
D. Have the vehicleג€™s computer compress the data in hourly snapshots, and store it in a GCS Coldline bucket
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.
D is the correct answer
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
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
It cost same as nearline when not accessed, but coldline is cheaper than BQ
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
D is the correct answer
ok for D
If the words 'next year' wouldn't have been there then Big Table 💯 . But as it will be required next year so Coldline bucket would be the most cost effective solution.
I hope this question isn't on the test. This quesiton is the most obnoxiously thoughtless and ill considered question I've come across and there are a lot of really bad ones on this test. I would probably pick D. Dataflow can be really expensive.