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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.dropFields("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.remove("name", "email", "birthdate")

2. A Data Analyst is working on the DataFrame sensor_df, which contains two columns:
Which code fragment returns a DataFrame that splits the record column into separate columns and has one array item per row?
A)

B)

C)

D)

A) exploded_df = exploded_df.select("record_datetime", "record_exploded")
B) exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))
C) exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))
D) exploded_df = sensor_df.withColumn("record_exploded", explode("record")) exploded_df = exploded_df.select("record_datetime", "sensor_id", "status", "health")

3. 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) select
B) coalesce
C) filter
D) groupBy

4. An application architect has been investigating Spark Connect as a way to modernize existing Spark applications running in their organization.
Which requirement blocks the adoption of Spark Connect in this organization?

A) Debuggability: the ability to perform interactive debugging directly from the application code
B) Complete Spark API support: the ability to migrate all existing code to Spark Connect without modification, including the RDD APIs
C) Upgradability: the ability to upgrade the Spark applications independently from the Spark driver itself
D) Stability: isolation of application code and dependencies from each other and the Spark driver

5. You have:
DataFrame A: 128 GB of transactions
DataFrame B: 1 GB user lookup table
Which strategy is correct for broadcasting?

A) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling itself
B) DataFrame A should be broadcasted because it is larger and will eliminate the need for shuffling DataFrame B
C) DataFrame A should be broadcasted because it is smaller and will eliminate the need for shuffling itself
D) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling DataFrame A

Solutions: