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

2. A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set for spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?

A) A sort-merge join
B) A Cartesian join
C) A broadcast nested loop join
D) A shuffled hash join

3. 31 of 55.
Given a DataFrame df that has 10 partitions, after running the code:
df.repartition(20)
How many partitions will the result DataFrame have?

A) 10
B) Same number as the cluster executors
C) 5
D) 20

4. A data scientist is analyzing a large dataset and has written a PySpark script that includes several transformations and actions on a DataFrame. The script ends with a collect() action to retrieve the results.
How does Apache Spark™'s execution hierarchy process the operations when the data scientist runs this script?

A) The entire script is treated as a single job, which is then divided into multiple stages, and each stage is further divided into tasks based on data partitions.
B) The script is first divided into multiple applications, then each application is split into jobs, stages, and finally tasks.
C) The collect() action triggers a job, which is divided into stages at shuffle boundaries, and each stage is split into tasks that operate on individual data partitions.
D) Spark creates a single task for each transformation and action in the script, and these tasks are grouped into stages and jobs based on their dependencies.

5. A data analyst builds a Spark application to analyze finance data and performs the following operations: filter, select, groupBy, and coalesce.
Which operation results in a shuffle?

A) coalesce
B) filter
C) groupBy
D) select

Solutions: