Databricks-Spark-Assoc Using Spark SQL Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  date_format(event_time, 'yyyy-MM-dd')

  Why it's wrong here

  date_format converts a date or timestamp into a formatted string, not a date type. If event_time is already a string, date_format may fail or require an implicit cast. It also returns a string rather than a date, so the resulting column would not be a true date column, contrary to the requirement of extracting the date portion.

• ✗

  trunc(event_time, 'MM')

  Why it's wrong here

  The trunc function truncates a date or timestamp to the specified unit, such as month or year. It does not parse a string with a custom pattern and cannot extract just the date portion from 'yyyy-MM-dd HH:mm:ss'. Applying it to a string column would either error or produce unintended results, so it does not meet the scenario's need.

• ✗

  substring(event_time, 1, 10)

  Why it's wrong here

  While substring would extract the first ten characters and coincidentally yield the date text for this format, it returns a string rather than a date. This approach is brittle: it fails for other valid timestamp formats and does not validate the date. It also does not leverage Spark SQL's date parsing, so it is not the appropriate expression here.

• ✓

  to_date(event_time, 'yyyy-MM-dd HH:mm:ss')

  Why this is correct

  The to_date function parses the string using the supplied pattern and returns a date value containing only the date portion. Using the format string ensures correct parsing of the timestamp text. This produces the desired event_date column while leaving the original event_time column unchanged, which is exactly what the scenario requires.