Databricks-Spark-Assoc Developing DataFrame/DataSet API Applications Practice Question
A data engineer has a PySpark DataFrame `orders` with a string column `order_ts` formatted as `yyyy-MM-dd HH:mm:ss`. They need a new column `order_date` containing only the date portion as a `date` type, so downstream code can filter by day. Which approach is correct?
⚠ Common exam trap
The trap here is treating any function that visually produces '2024-01-01' as equivalent, ignoring that column type drives downstream comparison behavior.
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
✓
`orders.withColumn('order_date', to_date('order_ts', 'yyyy-MM-dd HH:mm:ss'))`
Converting a formatted timestamp string to a `date` requires an explicit parse with a pattern. `to_date(col, pattern)` parses the string and returns a `date` column, which is the correct type for day-level filters and joins. String manipulation or `from_unixtime` yields strings, and `date_trunc` yields a timestamp, so none of them match the requirement as cleanly or as safely as `to_date`.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
`orders.withColumn('order_date', from_unixtime(unix_timestamp('order_ts', 'yyyy-MM-dd HH:mm:ss'), 'yyyy-MM-dd'))`
Why it's wrong here
This is incorrect because `from_unixtime` returns a formatted string, not a `date`. While the value visually resembles a date, downstream code comparing it to a `date` literal would trigger implicit casting and potential timezone issues. The direct `to_date` function is both simpler and type-correct for this scenario.
- ✗
`orders.withColumn('order_date', date_trunc('day', 'order_ts'))`
Why it's wrong here
This is incorrect because `date_trunc` expects a timestamp or date input and returns a timestamp truncated to the day; when given a string column it may implicitly cast or fail depending on the Spark version. Even when it works, the result is a timestamp at midnight rather than a `date`, which does not match the stated requirement.
- ✗
`orders.withColumn('order_date', substring('order_ts', 1, 10))`
Why it's wrong here
This returns a string, not a `date`, so comparisons against `lit('2024-01-01').cast('date')` would require implicit casting or would behave unpredictably. It also fails on inputs where the date portion is not exactly ten characters or uses a different separator, making it brittle for a production pipeline.
- ✓
`orders.withColumn('order_date', to_date('order_ts', 'yyyy-MM-dd HH:mm:ss'))`
Why this is correct
This is correct because `to_date` parses the string using the supplied pattern and returns a `date` column, which is exactly what downstream day-level filtering needs. It preserves the original string column and adds a properly typed date, avoiding implicit conversions or extra casts.
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JA
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.