How to minimize BigQuery storage costs for time-series data?
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?
Community Votes
100% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The exam tests the ability to select the most cost-effective data lifecycle management strategy, specifically distinguishing between manual deletion queries and automated partition expiration based on the data's actual timestamp.
To minimize storage costs for weather data used in ML, partition the BigQuery table by the weather date column and set a partition expiration of 30 days. This approach automatically removes outdated data without the overhead of manual deletion queries.
Choosing Option D (scheduled query) is a common mistake because users overlook the operational cost and complexity of running DML statements compared to the native, automatic partition expiration feature.
Community Discussion (8 comments)
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Expert Analysis
Why the Answer Is Correct
Option B is correct because partitioning by the datetime value of the weather date allows the data to be organized exactly how the ML model consumes it. Setting the partition expiration to 30 days ensures that BigQuery automatically drops partitions older than 30 days, minimizing storage costs and query scanning without requiring manual intervention or expensive DML operations.Why the Other Options Are Wrong
Options A and D suggest running scheduled queries to delete rows. While this works, it incurs query processing costs for the DML operation and requires management overhead. Option C partitions by ingestion time, which is less precise if data ingestion is delayed or if historical backfills occur; the requirement specifies retaining data based on the "weather date," not when it was loaded.Community Comment Notes
Commenters confirmed Option B is correct based on recent exam experiences. One comment clarified that since the requirement is based on the "last 30 days of weather data," partitioning by the actual weather date is preferred over ingestion time. Another comment addressed a misconception about partition limits, noting that BigQuery allows daily, monthly, or yearly granularity, easily handling 30 days of data without hitting the 4000 partition limit.Official Reference
Exam Strategy
When facing questions about data retention or minimizing storage costs in BigQuery, always prioritize native features like partition expiration or table expiration over manual deletion strategies like scheduled queries.