Practice Test 3 | Google Cloud Certified Professional Cloud Architect | Dumps | Mock Test
For this question, refer to the TerramEarth case study.
TerramEarth has equipped unconnected 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’ 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.
Correct Answer D
D (Correct answer) – Have the vehicle’s computer compress the data in hourly snapshots, a Store it in a GCS Coldline bucket. This is the Lowest cost for storage for infrequent access that meets the requirement (“next year …”). There is no good reason to use nearline instead of low-cost storage Coldline for one-year-after access.
A – Have the vehicle’s computer compresses the data in hourly snapshots and store it in a Google Cloud Storage (GCS) Nearline bucket. Nearline does not fit the usage pattern described in the question.
Nearline fits this usage pattern: For example, if you want to continuously add files to cloud storage, and plan to access those files once a month for analysis, nearline storage is a great choice
B and C can be eliminated for this reason “Push the telemetry data in real-time to a streaming dataflow job …” since vehicles are unconnected
Reference Resource
Comparison of storage classes https://cloud.google.com/storage/docs/storage-classes
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