Quick Answer
The correct code snippet is df.withColumn('date', to_date('timestamp', 'yyyy-MM-dd HH:mm:ss')). This works because the to_date function in PySpark explicitly converts a string column into a date type by stripping the time component, leaving only the year, month, and day—exactly what you need for partitioning by date. On the Microsoft Azure Data Engineer Associate DP-203 exam, this scenario tests your understanding of PySpark’s date-time functions and the critical difference between to_date and to_timestamp: to_date extracts the date part for partitioning, while to_timestamp retains the full timestamp including hours, minutes, and seconds. A common trap is using to_timestamp when the requirement specifies partitioning by date, which would unnecessarily preserve time details and potentially create too many partitions. Remember the memory tip: “Date drops the time, timestamp keeps it all.” This distinction is frequently tested in DP-203 data transformation tasks within Azure Databricks, so always confirm whether the output needs a date or a full timestamp before choosing your function.
This DP-203 practice question tests your understanding of develop data processing. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
df.withColumn('date', col('timestamp').cast('date'))
Why wrong: May not parse correctly without format.
df.withColumn('date', to_timestamp('timestamp', 'yyyy-MM-dd HH:mm:ss'))
Why wrong: Returns timestamp, not date.
df.withColumn('date', to_date('timestamp', 'yyyy-MM-dd HH:mm:ss'))
Correctly converts string to date with format.
df.withColumn('date', to_date('timestamp'))
Why wrong: Missing format; may produce null.
Answer choices
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'))
Option C is correct because `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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
df.withColumn('date', to_date('timestamp'))
Why it's wrong here
Missing format; may produce null.
Common exam traps
The trap here is that candidates often 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.
Detailed technical explanation
Under the hood, `to_date` in PySpark uses Java's `SimpleDateFormat` to parse the string according to the provided pattern, then truncates the time portion to return a `DateType` (year, month, day). In contrast, `to_timestamp` returns a `TimestampType` that includes hours, minutes, and seconds, which can lead to excessive partition granularity if used for partitioning. A real-world scenario is partitioning large event logs by date to optimize query performance in Delta Lake, where using `to_date` ensures each partition contains all events for a single day.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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FAQ
Develop data processing — This question tests Develop data processing — Read the scenario before looking for a memorised answer..
The correct answer is: df.withColumn('date', to_date('timestamp', 'yyyy-MM-dd HH:mm:ss')) — Option C is correct because `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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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