Answer-first summary for fast verification
Answer: Execute Python Script, Summarize Data
The question requires generating a statistical summary with both p-value and unique count for each feature column. The 'Summarize Data' module provides basic statistical summaries including unique counts, but it does not calculate p-values - it outputs percentile values (P0.5, P1, P5, P95, P99, P99.5) which are often confused with p-values. To calculate actual p-values, which require hypothesis testing, the 'Execute Python Script' module is needed to write custom statistical code. The community consensus strongly supports CE (100% agreement in the poll), with detailed explanations confirming that 'Summarize Data' handles unique counts while 'Execute Python Script' is required for p-value calculation. Other options like 'Export Count Table' only provide frequency counts without comprehensive statistics, 'Convert to Indicator Values' is for data transformation, and 'Compute Linear Correlation' focuses on correlation coefficients rather than comprehensive statistical summaries.
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You are analyzing a dataset in Azure Machine Learning Studio. You must generate a statistical summary that includes the p-value and the unique value count for each feature column. Which two modules should you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
A
Computer Linear Correlation
B
Export Count Table
C
Execute Python Script
D
Convert to Indicator Values
E
Summarize Data
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