Practice Test 2 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
You use BigQuery as the main data warehouse. As you are checking the bill of Google Cloud usage for the past month, you noticed a hike in BigQuery costs. Upon more inspection, you found out it’s due to ad-hoc queries scanning large amounts of data. You want to limit the size of data queries process to control costs and avoid excessive bills. What should you do?
A. Set monthly flat-rate pricing for BigQuery.
B. Change to using Google Storage as external table instead of using BigQuery’s storage option.
C. Launch a Cloud SQL instance. Replicate BigQuery datasets to Cloud SQL. Allow users with ad-hoc queries to use Cloud SQL instead.
D. Set custom quotas on User-level with access on BigQuery based on their business requirements.
If you have multiple BigQuery projects and users, you can manage costs by requesting a custom quota that specifies a limit on the amount of query data processed per day.
Creating a custom quota on query data allows you to control costs at the project level or at the user level.
- Project-level custom quotas limit the aggregate usage of all users in that project.
- User-level custom quotas are separately applied to each user or service account within a project.
In this scenario, the aim is to control data volume quotas. So, option D is the best approach.
- Option A is incorrect: Flat-rate can be a possible approach. However, BigQuery does not provide flexible flat-rate pricing and the cheapest is $10,000 for 500 slots, which may not be a desirable option for small-medium businesses.
- Option B is incorrect: You are charged for the number of bytes processed whether the data is stored in BigQuery or in an external data source such as Cloud Storage, Google Drive, or Cloud Bigtable. Moving data to Google Storage will not save costs when querying data.
- Option C is incorrect: Replicating data from BigQuery to Cloud SQL is not straightforward. You may need to export data to Cloud Storage, then import data to Cloud SQL. This solution can be more costly in the long run depending on data size since you pay for Google Storage costs as well as choosing the Cloud SQL instance type.
Source(s):
BigQuery – Creating custom cost controls:
BigQuery Pricing – Monthly Flat Rate:
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