Cloud Digital Leader How Google Cloud Resources Are Managed Practice Question
A finance team needs to analyze detailed cost data across multiple projects, including resource-level breakdowns, labels, and cost attribution. They want to export this data to a BigQuery dataset for custom analysis. Which billing export option should they enable?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable billing export to BigQuery (standard usage cost data)
Standard billing export to a BigQuery dataset provides detailed cost data with resource-level items, labels, and cost breakdowns. It is the recommended method for granular analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the Cloud Billing API to pull cost data programmatically
Why it's wrong here
The Cloud Billing API provides programmatic access to billing account cost data, but it returns aggregated or paginated data and does not capture all detailed usage line items for complex analysis. Moreover, you would need to write and maintain custom code to extract, transform, and load into BigQuery, which is not a native integration. Also, the API is rate-limited and not designed for large-scale historical exports.
- ✗
Set up a budget alert with notifications to Pub/Sub and ingest into BigQuery
Why it's wrong here
Budget alerts are configured to monitor spending thresholds and send notifications via email or Pub/Sub; they do not provide a full billing export. The message body contains only budget amount, current spend, and alert threshold, not the granular line-item usage data needed for detailed analysis. Ingesting these notifications into BigQuery would only give a slim record of budget status over time, not comprehensive cost breakdowns.
- ✗
Enable billing export to Cloud Storage in CSV format
Why it's wrong here
Cloud Storage CSV export provides daily cost files, but you lose native BigQuery schema enforcement and query performance. To analyze the data, you would need to load the CSV into BigQuery or use external table querying, adding an extra step and slower performance. Additionally, CSV files can be large and require managing partitioning and file formats, whereas BigQuery export automatically handles daily tables.
- ✓
Enable billing export to BigQuery (standard usage cost data)
Why this is correct
This native integration streams detailed billing line items into a BigQuery dataset automatically, with daily and monthly tables that include metadata like resource labels, SKU, cost, and usage amount. It supports standard SQL queries for custom reports, joins with other datasets, and integrates with Looker Studio or other BI tools. The export is fully managed, eliminating custom ETL, and is the recommended approach for comprehensive cost analytics.
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Related to this question
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BigQuery and Data Analytics
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
Key term
Labels
Labels are descriptive text or tags attached to IT resources to organize, identify, and manage them based on attributes like purpose, environment, or owner.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This GCDL practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the GCDL exam.