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Answer: Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third-party companies with access to the bucket.
**Explanation:** Option B is the correct answer because: - **Cost-effective**: Cloud Storage is cheaper for data sharing compared to BigQuery compute costs - **Data currency**: Regular exports via Cloud Scheduler ensure data is current - **Access control**: Granular permissions can be set on Cloud Storage buckets - **Scalable**: Can handle multiple third-party companies efficiently Other options have limitations: - Option A: Authorized views in BigQuery would incur BigQuery query costs for third parties - Option C: Separate BigQuery dataset still incurs BigQuery costs for data access - Option D: Cloud Dataflow job adds complexity and ongoing operational costs
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NO.9 You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current. Which solution should you choose?
A
Create an authorized view on the BigQuery table to control data access, and provide third-party companies with access to that view.
B
Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third-party companies with access to the bucket.
C
Create a separate dataset in BigQuery that contains the relevant data to share, and provide third-party companies with access to the new dataset.
D
Create a Cloud Dataflow job that reads the data in frequent time intervals, and writes it to the relevant BigQuery dataset or Cloud Storage bucket for third-party companies to use.
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