Google Professional Data Engineer
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As a data engineer, you are tasked with selecting an appropriate database solution for storing time series data related to CPU and memory usage across millions of computers. This data is collected in one-second interval samples. The database must support real-time, ad hoc analytics performed by analysts. Additionally, you need to ensure that the database and its schema design are cost-efficient, avoiding charges per query executed, and scalable for future dataset growth. Which database and data model would you choose to meet these requirements?
A
Create a table in BigQuery, and append the new samples for CPU and memory to the table
B
Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second
C
Create a narrow table in Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second
D
Create a wide table in Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.