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Answer: Employ Vertex AI Workbench user-managed notebooks to directly query the BigQuery table, perform in-depth data exploration, and generate visualizations and statistical analyses using Python libraries., Combine the use of Vertex AI Workbench for exploratory data analysis and Google Data Studio for creating interactive visualizations to share with the team.
Vertex AI Workbench user-managed notebooks (Option C) provide the flexibility and depth required for exploratory data analysis, including the use of Python libraries for sophisticated statistical analyses and visualizations. They also allow direct querying of BigQuery tables, making them ideal for this scenario. Option E suggests a combination of Vertex AI Workbench for analysis and Google Data Studio for visualization, offering a comprehensive solution that leverages the strengths of both tools for maximum flexibility and team collaboration. While Google Data Studio (Option A) is excellent for interactive dashboards, it lacks the capability for in-depth statistical analysis. Dataprep (Option B) is focused on data preparation rather than analysis. TensorFlow Data Validation on Dataflow (Option D) is more suited for data quality checks than for comprehensive exploratory analysis and reporting.
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You are tasked with evaluating a medium-sized (~10 GB) BigQuery table that will serve as the dataset for an upcoming machine learning project. The primary objectives are to quickly assess the data's suitability for model development with maximum flexibility and to produce a comprehensive one-time report for your team of ML engineers. This report should include detailed visualizations of data distributions and sophisticated statistical analyses to inform the model development process. Considering the need for flexibility, depth of analysis, and ease of sharing insights with the team, which of the following approaches would best achieve these goals? (Choose two correct options if option E is available, otherwise choose one.)
A
Generate the report using Google Data Studio, leveraging its interactive dashboard capabilities for data visualization.
B
Utilize Dataprep for data cleaning and transformation, then export the results to a format suitable for statistical analysis and visualization.
C
Employ Vertex AI Workbench user-managed notebooks to directly query the BigQuery table, perform in-depth data exploration, and generate visualizations and statistical analyses using Python libraries.
D
Run TensorFlow Data Validation on Dataflow to validate the data quality and generate a report based on the validation results.
E
Combine the use of Vertex AI Workbench for exploratory data analysis and Google Data Studio for creating interactive visualizations to share with the team.