Databricks-Spark-Assoc Pandas API on Spark Practice Question
A data engineer has a Pandas-on-Spark DataFrame `psdf` with a column `event_ts` stored as string timestamps. They run `psdf['event_ts'].astype('datetime64[ns]')` and then call `.dt.hour` on the resulting Series. In a Databricks notebook, what is the result of this operation?
⚠ Common exam trap
The trap here is assuming that any pandas operation on a Pandas-on-Spark object forces local execution, when supported datetime accessors are actually translated into distributed Spark expressions.
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
✓
The conversion and `.dt.hour` execute as Spark expressions, returning a new Pandas-on-Spark Series without collecting data to the driver.
Pandas API on Spark translates supported pandas operations into Spark logical plans. Casting a string column to datetime64 and accessing `.dt.hour` are both supported and map to Spark's timestamp cast and hour extraction. The result stays distributed as a Pandas-on-Spark Series, and no driver collection is triggered. This preserves scalability and aligns with the library's goal of providing pandas-like syntax over Spark execution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The conversion and `.dt.hour` execute as Spark expressions, returning a new Pandas-on-Spark Series without collecting data to the driver.
Why this is correct
Pandas API on Spark implements `.astype('datetime64[ns]')` and the `.dt` accessor as distributed Spark column expressions. The string-to-timestamp cast maps to Spark's cast to TimestampType, and `.dt.hour` maps to the `hour` function, so no driver collection occurs. The result remains a Pandas-on-Spark Series backed by a Spark plan, which is the expected behavior for supported datetime operations.
- ✗
The operation succeeds only if `spark.sql.execution.arrow.pyspark.enabled` is set to true; otherwise it falls back to a Python UDF that may fail on null timestamps.
Why it's wrong here
Arrow optimization affects conversion between Spark and pandas, not the internal translation of `.astype` and `.dt.hour` into Spark expressions. These operations use built-in Spark functions and do not require Arrow to be enabled to succeed. A Python UDF fallback is not the mechanism here, and null timestamps are handled according to Spark's null semantics, not an Arrow-dependent path.
- ✗
The operation triggers an immediate collect of all rows to the driver, converts them to pandas, computes the hour locally, and returns a pandas Series.
Why it's wrong here
Pandas API on Spark avoids collecting to the driver for supported column operations. The `.astype` and `.dt.hour` calls are translated into Spark SQL expressions and remain distributed. An automatic collect would defeat the purpose of using Pandas API on Spark for large data and is not how these operations are implemented. This distractor describes eager pandas behavior, not the distributed engine.
- ✗
The `.dt` accessor is unsupported in Pandas API on Spark, so the call raises an AttributeError before any Spark job is launched.
Why it's wrong here
The `.dt` accessor is supported for datetime-like Series in Pandas API on Spark; it is not universally unsupported. While some datetime properties may have caveats, basic attributes such as `.dt.hour`, `.dt.day`, and `.dt.year` are implemented through Spark functions. An AttributeError would not be the expected outcome for this valid operation, making this distractor incorrect.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-Spark-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-Spark-Assoc exam.