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Question 314 - Professional Data Engineer discussion

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You work for an airline and you need to store weather data in a BigQuery table Weather data will be used as input to a machine learning model. The model only uses the last 30 days of weather data. You want to avoid storing unnecessary data and minimize costs. What should you do?

A.
Create a BigQuery table where each record has an ingestion timestamp Run a scheduled query to delete all the rows with an ingestion timestamp older than 30 days.
Answers
A.
Create a BigQuery table where each record has an ingestion timestamp Run a scheduled query to delete all the rows with an ingestion timestamp older than 30 days.
B.
Create a BigQuery table partitioned by ingestion time Set up partition expiration to 30 days.
Answers
B.
Create a BigQuery table partitioned by ingestion time Set up partition expiration to 30 days.
C.
Create a BigQuery table partitioned by datetime value of the weather date Set up partition expiration to 30 days.
Answers
C.
Create a BigQuery table partitioned by datetime value of the weather date Set up partition expiration to 30 days.
D.
Create a BigQuery table with a datetime column for the day the weather data refers to. Run a scheduled query to delete rows with a datetime value older than 30 days.
Answers
D.
Create a BigQuery table with a datetime column for the day the weather data refers to. Run a scheduled query to delete rows with a datetime value older than 30 days.
Suggested answer: B

Explanation:

Partitioning a table by ingestion time means that the data is divided into partitions based on the time when the data was loaded into the table. This allows you to delete or archive old data by setting a partition expiration policy. You can specify the number of days to keep the data in each partition, and BigQuery automatically deletes the data when it expires. This way, you can avoid storing unnecessary data and minimize costs.

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asked 18/09/2024
Sharanjit Kareer
41 questions
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