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RAN-DBDEA-00217
Ranvier · DBDEA

Exam DBDEA Topics: transformation Difficulty: easy

A gold orders table feeds a dashboard that averages the rating column, and stakeholders want missing ratings to count as 0 instead of NULL. The engineer preparing the aggregation must keep every order row in the output while making each NULL rating read as 0. Which PySpark operation achieves this?

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  1. A. dropna() on rating — removing the rows where rating is NULL keeps the dashboard free of missing values entirely
  2. B. fillna(0) on rating — it drops the rows whose rating is NULL, so only genuinely rated orders remain in the output
  3. C. Registering a Python UDF — no built-in DataFrame operation can substitute a constant for NULL values in a column
  4. D. fillna(0) on rating — it replaces each NULL rating with 0 while keeping every order row in the DataFrame
Reveal answer

Correct answer: fillna(0) on rating — it replaces each NULL rating with 0 while keeping every order row in the DataFrame

Reference: https://docs.databricks.com/aws/en/pyspark/reference

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Official Answer (D): fillna(0) on rating — it replaces each NULL rating with 0 while keeping every order row in the DataFrame

⚓ Official Documentation Anchor: https://docs.databricks.com/aws/en/pyspark/reference
Ranvier · DBDEA

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