Databricks Certified Machine Learning Associate Practice Mode

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose A0

  - Correct answer : A — `dbutils.data.summarize(pandas_df)`, because the function accepts pandas DataFrames as well as Spark DataFrames and renders an interactive report with types, null counts, and histograms per column. A . `dbutils.data.summarize()` works on both pandas and Spark DataFrames and produces an interactive, browsable notebook report with per-column type, null-count, and histogram information, matching exactly what was asked for. B . pandas' `.describe()` returns a plain numeric table of count, mean, stddev, and quartiles with no histograms, type breakdown, or interactive rendering, so it does not meet the visualization requirement. C . `summary()` on a Spark DataFrame returns a plain text-based statistics table, not an interactive report with histograms, so converting the pandas object back to Spark first does not add the requested visualizations. D . The `precise` flag controls whether statistics are computed exactly or approximately; it does not gate whether the histogram and visualization elements of the report are shown, which appear regardless of this setting.