Google Cloud Professional Data Engineer — Question 4

You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible.
What should you do?

Answer options

• A. Load the data every 30 minutes into a new partitioned table in BigQuery.
• B. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
• C. Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
• D. Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

Correct answer: B

Explanation

The correct answer is B because storing the data in a Google Cloud Storage bucket allows for efficient and cost-effective updates and integration with BigQuery through a federated data source. Option A is incorrect as creating a new partitioned table every 30 minutes could lead to unnecessary costs and complexity. Option C is not suitable because using Cloud Datastore complicates the integration process and may not be as efficient. Option D, while feasible, does not leverage the advantages of a federated data source, which provides a more direct integration with BigQuery.