You have millions of customer feedback records stored in BigQuery. You want to summarize the data by using the large language model (LLM) Gemini. You need to plan and execute this analysis using the most efficient approach. What should you do?
A.
Query the BigQuery table from within a Python notebook, use the Gemini API to summarize the data within the notebook, and store the summaries in BigQuery.
B.
Use a BigQuery ML model to pre-process the text data, export the results to Cloud Storage, and use the Gemini API to summarize the pre- processed data.
C.
Create a BigQuery Cloud resource connection to a remote model in Vertex Al, and use Gemini to summarize the data.
D.
Export the raw BigQuery data to a CSV file, upload it to Cloud Storage, and use the Gemini API to summarize the data.
The most efficient approach is C. Use a BigQuery Cloud resource connection to Vertex AI Gemini. This method is optimal because it allows in-database processing within BigQuery, directly using Gemini for summarization via a Cloud Resource Connection, minimizing data movement and maximizing efficiency. Option A (Python Notebook) is inefficient due to data extraction from BigQuery. Option B (BigQuery ML + Cloud Storage) adds unnecessary complexity and data export to Cloud Storage. Option D (CSV Export + Cloud Storage) is the least efficient, involving multiple data export/import steps and being impractical for millions of records. Therefore, Option C provides the most streamlined and efficient way to summarize BigQuery data with Gemini.
Querying BigQuery from a Python notebook and using the Gemini API manually introduces unnecessary complexity and latency. It also requires additional infrastructure (like a notebook environment) and manual intervention. The most efficient approach should minimize data movement, leverage Google Cloud's native integrations, and avoid unnecessary steps like exporting data to external files or manually processing it.
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