Databricks Data Engineer Associate Question 164
Single answerYou are working with a DataFrame in Databricks that contains a column named log_data with strings in the format 'User: <username> part of the string into a new column called username. Which of the following code snippets can correctly achieve this?
- A
df.withColumn('username', F.regexp_extract(F.col('log_data'), 'User: (.*?)|', 1))
- B
df.withColumn('username', F.substring(F.col('log_data'), 7, 10))
- C
df.withColumn('username', F.split(F.col('log_data'), 'User: ')[1])
- D
df.withColumn('username', F.expr('regexp_extract(log_data, "User: (.*?)\|", 1)'))
Show answer and explanation
Correct answer: A
Explanation
The correct approach to extract a pattern from a string column in PySpark is to use the regexp_extract function. This function allows you to define a regular expression with capturing groups to extract specific portions of a string. In this case, the pattern 'User: (.*?)|' correctly targets the username, making the first option the appropriate choice.
- A. Correct.
This is the correct code.
regexp_extractextracts the specific portion of the string based on the provided regular expression. The pattern 'User: (.?)|' matches the username between 'User: ' and '|', and the capturing group (.?) ensures only the username is captured. - B. Incorrect.
This is incorrect because
substringdoes not dynamically parse text based on patterns. It extracts a fixed range of characters, which may not work for varying username lengths. - C. Incorrect.
This is incorrect as
splitwould provide a list-like output, and attempting to directly index into it in this way is not valid in PySpark. - D. Incorrect.
This is incorrect because
F.expris not the most direct or appropriate approach to solve this problem in this context. While it could theoretically work, it is not the recommended method for the Databricks Certified Data Engineer Associate exam.