Databricks-Spark-Assoc Developing DataFrame/DataSet API Applications Practice Question
A data engineer is building a PySpark application that must validate incoming records in a DataFrame `raw` before loading them into a curated table. They want to apply user-defined validation logic that cannot be expressed with built-in functions, and they want the result to remain a DataFrame column of Boolean values. Which TWO approaches allow this? (Choose two.)
⚠ Common exam trap
The trap here is assuming `df.transform` executes custom Python row logic, when it only applies a DataFrame-to-DataFrame function.
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
✓
Use `pandas_udf` with a scalar return type and apply it via `withColumn`.
Both a registered Python UDF invoked through SQL expressions and a scalar `pandas_udf` applied with `withColumn` produce Boolean DataFrame columns using custom Python logic. The RDD round-trip leaves the DataFrame API and requires manual schema handling. `transform` alone does not perform row-wise custom logic, and a Scala UDF is not a standard path in a PySpark-only application.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use `df.transform` to apply a Python function that returns a Boolean column.
Why it's wrong here
`transform` applies a function that takes a DataFrame and returns a DataFrame, but it does not itself execute row-wise Python logic. Any row-wise validation still requires a UDF or built-in expression inside the transformed DataFrame. On its own, `transform` does not introduce custom validation semantics, so it does not satisfy the requirement as a standalone approach.
- ✗
Use a Scala UDF registered in the Spark session and invoke it from a Python DataFrame column expression.
Why it's wrong here
A Scala UDF registered in the session is callable from SQL, but in a pure PySpark application the developer would need to compile and register it through JVM access, which is not a standard PySpark workflow. The scenario describes a PySpark application, and mixing languages introduces complexity and serialization concerns that are not required. It is not one of the two conventional approaches.
- ✗
Use `df.rdd.map` to apply the Python function, then convert the RDD back to a DataFrame with a schema.
Why it's wrong here
Converting to an RDD and back to a DataFrame is possible but bypasses the DataFrame optimizer entirely during the map step and requires manual schema definition. It is not a DataFrame-native way to add a validation column and loses Catalyst's ability to optimize the surrounding plan. The scenario asks for approaches that keep the result as a DataFrame column without leaving the DataFrame API.
- ✓
Use `pandas_udf` with a scalar return type and apply it via `withColumn`.
Why this is correct
A `pandas_udf` with a scalar return type operates on batches using Arrow for data transfer and produces a column of the declared type. Applied through `withColumn`, it yields a Boolean column suitable for validation. This approach is generally faster than row-at-a-time Python UDFs because it amortizes serialization across batches.
- ✓
Define a Python function and register it with `spark.udf.register`, then call it from `selectExpr` or `expr`.
Why this is correct
Registering a Python function as a UDF makes it callable from Spark SQL expressions, including `selectExpr` and `expr`. The return type is declared explicitly, and the result appears as a DataFrame column. This is a supported way to embed arbitrary Python validation logic into a DataFrame transformation pipeline when built-in functions are insufficient.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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