Your company has a Google Cloud project that uses BigQuery for data warehousing on a pay-per-use basis. You want to monitor queries in real time to discover the most costly queries and which users spend the most. What should you do?

The correct answer is to create a Cloud Logging sink to export BigQuery data access logs to BigQuery and then query the generated table. BigQuery data access logs contain detailed information about each query, including the user who ran it and the amount of data processed, which directly correlates to cost in a pay-per-use model. Exporting these logs to BigQuery allows for powerful, real-time analysis using SQL. The other options are less effective: Adding labels to datasets and checking billing reports by user is not feasible for real-time, detailed query cost analysis per user, as billing reports are aggregated and not granular enough for individual queries. Exporting logs to Cloud Storage and using Dataflow is overly complex and introduces unnecessary latency and cost compared to directly querying the logs in BigQuery. Activating billing export to BigQuery provides aggregated billing data, not the granular, per-query details needed to identify specific costly queries and users in real time.