PySpark data engineering interview problem. Difficulty: beginner. Pattern: Missing Data. About 12 minutes. Free to practice.
Fill null emails, then drop rows with null phone. Treat this as a production helper: match the contracted return shape, including empty and duplicate inputs.
From df, fill NULL email with unknown@example.com, then drop rows where phone is NULL. Select user_id, email, phone. Order by user_id. Assign result.
Input: user_id | email | phone Output: user_id | email | phone 1 | a@x.com | 111 2 | unknown@example.com | 222 5 | e@x.com | 555 Rows 3 and 4 lose phone and are dropped; row 2 keeps the filled email.
Topics: lakebench, pyspark, fillna, dropna.
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Interview-style drill: Fill null emails, then drop rows with null phone.
From `df`, fill NULL `email` with `unknown@example.com`, then drop rows where `phone` is NULL. Select `user_id`, `email`, `phone`. Order by `user_id`. Assign `result`.