Cloud Digital Leader How Google Cloud Resources Are Managed Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✓

  Enable billing export to BigQuery and query the exported tables

  Why this is correct

  Billing export to BigQuery automatically writes detailed daily usage and cost tables into a BigQuery dataset, preserving granular fields like service SKU, project, labels, and resource-level usage. These tables support standard SQL queries, allowing you to aggregate historical spend by date, label, or service, and to join with other datasets for deeper analysis. This is the only option that gives you a full, queryable history of cost trends over time, rather than a snapshot or summary.

• ✗

  Set up a budget alert to receive cost reports daily

  Why it's wrong here

  Budget alerts are event-driven notifications triggered when forecasted or actual spend crosses configured thresholds; they are not a data repository. Even if you configure a daily budget report, the email only lists current cost and forecast against the budget, not a historical series of spend across days or months. Therefore, budget alerts give you a real-time pulse, not the longitudinal data needed to analyze spending trends.

• ✗

  Use the Cloud Billing API to programmatically fetch cost data

  Why it's wrong here

  The Cloud Billing API does let you programmatically retrieve cost data from within the current billing period, but it has limitations: it is paginated, rate-limited, and designed for point-in-time reconciliation rather than heavyweight analytical queries. You would need to write custom code to fetch, store, and then query the data yourself, and the API doesn't expose the same rich, denormalized table structure that BigQuery export provides for efficient SQL analysis.

• ✗

  Download the monthly invoice CSV and import it into Sheets

  Why it's wrong here

  Monthly invoice CSVs are summary statements that show totals per SKU or per project, but they typically lack the granular per-line-item fields (such as resource labels or daily usage timestamps) needed for trend analysis. Moreover, importing into Sheets limits you to spreadsheet functions and manual pivots, which become impractical as the volume of historical data grows. This approach is neither automated nor scalable for complex, cross-cutting cost analysis.

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