You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?
Select one answer.
Marked answer: E
Community Discussion
Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.
Chose E2
It's E. C looks good because it's very easy and straightforward. But every time you query that view, a transformation will take place, making queries expensive, and it will be forever like this as the original table doesn't change. One time effort of loading the data into a new table with a small query. Heck you can even rename it right after if you don't want to change the reference of the ingestion.
Chose C1
Changing DT to TIMESTAMP. Create a view casting the string. Answer C.
Chose E1
I was hesitating between C and E, but E seems to be the good one
