Question 54

You are a data engineer onboarding a new `bronze.iot.sensor_readings` Delta table into Unity Catalog. Before writing transformation logic, your lead asks you to produce, in a single notebook cell, a profile that returns **summary statistics for numeric, string, and date columns AND histograms of the value distributions for every column** so the team can spot skew, high null fractions, and high-cardinality keys at a glance. You read the table into a DataFrame named `df`. You want the option that computes the full profile (including value-distribution histograms) over the DataFrame, not just count/mean/stddev/min/max. ```python df = spark.read.table("bronze.iot.sensor_readings") # ??? produce a full data profile with histograms ``` Which approach should you use?

• A`df.describe().show()`
• B`df.summary().show()`
• C`dbutils.data.summarize(df)`
• D`df.printSchema()`
• E`df.count()`