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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. 26 of 55.
A data scientist at an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user.
Before further processing, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns.
The PII columns in df_user are name, email, and birthdate.
Which code snippet can be used to meet this requirement?

A) df_user_non_pii = df_user.remove("name", "email", "birthdate")
B) df_user_non_pii = df_user.drop("name", "email", "birthdate")
C) df_user_non_pii = df_user.select("name", "email", "birthdate")
D) df_user_non_pii = df_user.dropFields("name", "email", "birthdate")

2. A data engineer wants to write a Spark job that creates a new managed table. If the table already exists, the job should fail and not modify anything.
Which save mode and method should be used?

A) save with mode Ignore
B) save with mode ErrorIfExists
C) saveAsTable with mode ErrorIfExists
D) saveAsTable with mode Overwrite

3. 21 of 55.
What is the behavior of the function date_sub(start, days) if a negative value is passed into the days parameter?

A) The number of days specified will be removed from the start date.
B) The number of days specified will be added to the start date.
C) The same start date will be returned.
D) An error message of an invalid parameter will be returned.

4. 39 of 55.
A Spark developer is developing a Spark application to monitor task performance across a cluster.
One requirement is to track the maximum processing time for tasks on each worker node and consolidate this information on the driver for further analysis.
Which technique should the developer use?

A) Broadcast a variable to share the maximum time among workers.
B) Use an accumulator to record the maximum time on the driver.
C) Configure the Spark UI to automatically collect maximum times.
D) Use an RDD action like reduce() to compute the maximum time.

5. 9 of 55.
Given the code fragment:
import pyspark.pandas as ps
pdf = ps.DataFrame(data)
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A) pdf.to_dataframe()
B) pdf.spark()
C) pdf.to_spark()
D) pdf.to_pandas()

Solutions: