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Answer: Feed to social media posts into the API directly from the source, and write the extracted data from the API into BigQuery.
**Explanation:** Option D is the correct solution because: - **Lowest cost and fewest steps** - Feeding posts directly from source to API eliminates intermediate storage steps - **Cloud Storage for archiving** - Raw posts can be stored in Cloud Storage for cost-effective archival and reprocessing - **BigQuery for analytics** - Extracted data (topics, sentiment) goes to BigQuery for analysis and dashboard creation - **Dashboard sharing** - BigQuery integrates well with Data Studio for sharing dashboards internally and externally - **Cost optimization** - Cloud Storage is cheaper for raw data storage than BigQuery - **Reprocessing capability** - Raw posts in Cloud Storage can be reprocessed if needed Other options are less optimal: - Option A: Storing raw posts in BigQuery is expensive for archival - Option B: Cloud SQL is not designed for large-scale analytics - Option C: Adds unnecessary step of storing raw posts before processing
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NO.22 You want to analyze hundreds of thousands of social media posts daily at the lowest cost and with the fewest steps. You have the following requirements:
A
Store the social media posts and the data extracted from the API in BigQuery.
B
Store the social media posts and the data extracted from the API in Cloud SQL.
C
Store the raw social media posts in Cloud Storage, and write the data extracted from the API into BigQuery.
D
Feed to social media posts into the API directly from the source, and write the extracted data from the API into BigQuery.
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