PySpark data engineering interview problem. Difficulty: beginner. Pattern: Aggregation. About 12 minutes. Free to practice.
Count distinct user_id values into a one-row frame. Treat this as a production helper: match the contracted return shape, including empty and duplicate inputs.
From df, compute the number of distinct user_id values. Return a one-row DataFrame with column distinct_users. Assign result. Prefer distinct().count() over collecting rows.
Input: events fixture Output: distinct_users 3 Users 1, 2, and 3 appear in the fixture.
Topics: lakebench, pyspark, distinct, count.
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Interview-style drill: Count distinct user_id values into a one-row frame.
From `df`, compute the number of distinct `user_id` values. Return a one-row DataFrame with column `distinct_users`. Assign `result`. Prefer distinct().count() over collecting rows.