Topic 1 Question 64
Your team needs to analyze large datasets stored in BigQuery to identify trends in user behavior. The analysis will involve complex statistical calculations, Python packages, and visualizations. You need to recommend a managed collaborative environment to develop and share the analysis. What should you recommend?
Create a Colab Enterprise notebook and connect the notebook to BigQuery. Share the notebook with your team. Analyze the data and generate visualizations in Colab Enterprise.
Create a statistical model by using BigQuery ML. Share the query with your team. Analyze the data and generate visualizations in Looker Studio.
Create a Looker Studio dashboard and connect the dashboard to BigQuery. Share the dashboard with your team. Analyze the data and generate visualizations in Looker Studio.
Connect Google Sheets to BigQuery by using Connected Sheets. Share the Google Sheet with your team. Analyze the data and generate visualizations in Gooqle Sheets.
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- 正解だと思う選択肢: A
The best option is A. Create a Colab Enterprise notebook and connect the notebook to BigQuery. Share the notebook with your team. Analyze the data and generate visualizations in Colab Enterprise. Option A is best because Colab Enterprise provides a managed, collaborative Python environment directly connected to BigQuery, ideal for complex stats, Python packages, and visualizations. Option B (BigQuery ML & Looker Studio) is incorrect because BigQuery ML has limited statistical scope compared to Python, and Looker Studio is less suited for code-based analysis. Option C (Looker Studio dashboard) is incorrect because Looker Studio excels at dashboards, not complex Python analysis or statistical coding. Option D (Google Sheets) is incorrect because Sheets is not designed for large datasets or complex analysis requiring Python packages. Therefore, Option A, Colab Enterprise, best suits the need for a managed Python-based collaborative analysis environment for BigQuery data.
👍 1n21837128472025/03/05
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