Comprehensive and Detailed Explanation:The error message "Resources exceeded" in BigQuery indicates that the query's execution plan is too complex or requires more computational resources (slots) than are available to it in the on-demand, fair-share pool.
Option D is the correct answer. BigQuery's on-demand pricing model uses a massive, shared pool of processing units called slots. While this pool is large, a single query cannot monopolize it, and there are limits to prevent runaway jobs. For consistently complex, high-resource queries, the solution is to switch to capacity- based pricing by purchasing slot reservations (e.g., using BigQuery editions). This provides your project with a dedicated, guaranteed amount of processing capacity, ensuring your complex queries have the resources they need to complete successfully.
Option A is incorrect because API request quotas relate to the number of API calls (e.g., how many jobs you can submit per minute), not the computational resources allocated to a single running query.
Option B is incorrect because table size limits are not related to query execution resources.
Option C is incorrect because while a syntax error would cause a query to fail, it would do so immediately with a syntax error message, not after 20 minutes with a "Resources exceeded" error. While optimizing the query is a good practice, the most direct way to solve a resource limit issue is to provide more resources.
Reference (Google Cloud Documentation Concepts):The Google Cloud documentation on "BigQuery pricing" explains the two main models: on-demand pricing and capacity-based pricing (editions). The
"Resources exceeded" error is a known limitation of the on-demand model for extremely demanding queries.
The documentation on "Introduction to slots" and "Reservations" explicitly presents purchasing dedicated slots as the solution for gaining more predictable and higher query performance for demanding workloads.

A materialized view is a database object that contains the results of a query, which can be updated periodically. It can improve the performance and efficiency of queries that involve aggregations, joins, or filters. By creating a materialized view to aggregate the base table data and include a filter clause to specify the last one year of partitions, you can ensure that the query results always include the latest data from the tables, while also reducing computation cost, maintenance overhead, and duration. The materialized view will automatically refresh when the base table data changes, and will only use the partitions that match the filter clause. Option A is incorrect because it will delete the historical data from the base table, which is not desired.
Option C is incorrect because it will create a redundant table that needs to be updated manually by a scheduled query, which is more complex and costly than using a materialized view. Option D is incorrect because a view does not store any data, but only references the base table data, which means it will not reduce the computation cost or duration of the query. References:
* Materialized views, ML models in data warehouse - Google Cloud
* Data Engineering with Google Cloud Platform - Packt Subscription