Databricks-Spark-Assoc Developing DataFrame/DataSet API Applications Practice Question
A developer has a DataFrame `df` and wants to remove duplicate rows considering only the columns `user_id` and `event_type`, keeping the first occurrence according to the current row order. Which DataFrame operation achieves this?
⚠ Common exam trap
Watch out — candidates often confuse `dropDuplicates` with `distinct`, when only `dropDuplicates` lets you scope deduplication to a subset of columns.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
df.dropDuplicates(['user_id', 'event_type'])
`dropDuplicates` with a column subset is the direct DataFrame API for removing rows that repeat on those columns while keeping the rest of each row intact. It avoids the over-aggressive behavior of `distinct()` and the column-collapsing behavior of `groupBy().agg()`. The null-dropping method targets missing values, not duplicates, so it does not meet the requirement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
df.distinct()
Why it's wrong here
`distinct()` considers all columns of the DataFrame when identifying duplicates. Since the scenario wants deduplication based only on `user_id` and `event_type`, using `distinct()` would retain rows that differ in any other column, producing far more rows than desired. It also does not allow specifying a column subset, so it cannot satisfy the requirement as stated.
- ✗
df.groupBy('user_id', 'event_type').agg(first('*'))
Why it's wrong here
`first('*')` is not a valid aggregate expression in Spark SQL; aggregates operate on individual columns, not on the wildcard. Even if rewritten to select specific columns, `groupBy().agg()` would collapse other columns and lose the remainder of each row. It also does not preserve a whole row, making it unsuitable for deduplication while keeping all original columns.
- ✗
df.na.drop(subset=['user_id', 'event_type'])
Why it's wrong here
`na.drop` removes rows containing null values in the specified columns. It does not address duplicate values at all, so repeated `user_id` and `event_type` combinations would remain. This method is designed for missing-data handling, not deduplication, and applying it here would silently delete valid rows that happen to have nulls in those columns.
- ✓
df.dropDuplicates(['user_id', 'event_type'])
Why this is correct
`dropDuplicates` accepts a subset of columns and removes rows that share the same values in those columns, retaining one arbitrary row per group. In practice, when no shuffle-induced reordering has occurred, the first occurrence in the current partition order is kept, which matches the scenario's intent. It is the idiomatic DataFrame API method for this requirement.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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