DP-203 Develop data processing Practice Question
You are building a data transformation in Azure Databricks using PySpark. The data includes a column 'timestamp' in string format 'yyyy-MM-dd HH:mm:ss'. You need to convert this to a timestamp type and extract the date part for partitioning. Which code snippet should you use?
⚠ Common exam trap
Test-takers frequently confuse `to_date` and `to_timestamp`, assuming both extract only the date, or they forget that `cast('date')` does not accept a custom format string, leading to runtime errors or null values.
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.withColumn('date', to_date('timestamp', 'yyyy-MM-dd HH:mm:ss'))
`to_date` with the format string 'yyyy-MM-dd HH:mm:ss' converts the string column to a date type, extracting only the date part (year, month, day) as required for partitioning. This matches the requirement to convert the timestamp string to a date for partitioning, not a full timestamp.
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.withColumn('date', col('timestamp').cast('date'))
Why it's wrong here
May not parse correctly without format.
- ✗
df.withColumn('date', to_timestamp('timestamp', 'yyyy-MM-dd HH:mm:ss'))
Why it's wrong here
Returns timestamp, not date.
- ✓
df.withColumn('date', to_date('timestamp', 'yyyy-MM-dd HH:mm:ss'))
Why this is correct
Correctly converts string to date with format.
- ✗
df.withColumn('date', to_date('timestamp'))
Why it's wrong here
Missing format; may produce null.
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
Key term
Azure Databricks
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure that lets data teams prepare data, run machine learning models, and build data pipelines using a single workspace.
Key term
Data Transformation Pipelines
Data transformation pipelines are automated sequences of steps that take raw data from a source, clean and reshape it into a usable format, and then load it into a destination for analysis or storage.
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