Your company has several sandbox projects that roll up to a single billing account. The FinOps team wants to run ad-hoc SQL queries that show daily spending for each Compute Engine VM, including the labels applied to every instance. They want a fully managed, in-product feature and do not want to maintain any ETL pipelines or custom code. Which approach meets the requirements with the least operational overhead?
Create a budget that sends spend notifications to Pub/Sub and run a Dataflow pipeline that ingests the messages into BigQuery.
Enable the standard usage cost export to BigQuery on the billing account and query the resulting dataset.
Enable the detailed usage cost export to BigQuery on the billing account and query the resulting dataset.
Enable standard usage cost export to Cloud Storage in JSON format and schedule a Cloud Function to load each file into BigQuery.
Enabling the standard usage cost export to BigQuery on the billing account is a built-in Cloud Billing feature that requires no custom data loading. The standard export includes the resource-level labels that the FinOps team needs and is automatically updated daily in the linked BigQuery dataset. The detailed usage export lacks labels, exporting to Cloud Storage would require additional jobs to ingest data into BigQuery, Pub/Sub budget notifications do not contain the necessary cost details, and Cloud Asset Inventory focuses on configuration metadata rather than cost data.
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What is the difference between standard and detailed cost export in GCP?
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What are labels in GCP and how are they used in cost monitoring?
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How does the standard cost export to BigQuery work in GCP?
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What is the difference between standard and detailed usage cost exports in GCP?
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What kind of data does the standard cost export to BigQuery provide?
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Why is BigQuery more suitable for this scenario compared to Cloud Storage?
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GCP Associate Cloud Engineer
Setting up a cloud solution environment
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