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

1. Given the code:

df = spark.read.csv("large_dataset.csv")
filtered_df = df.filter(col("error_column").contains("error"))
mapped_df = filtered_df.select(split(col("timestamp"), " ").getItem(0).alias("date"), lit(1).alias("count")) reduced_df = mapped_df.groupBy("date").sum("count") reduced_df.count() reduced_df.show() At which point will Spark actually begin processing the data?

A) When the count action is applied
B) When the filter transformation is applied
C) When the groupBy transformation is applied
D) When the show action is applied

2. The following code fragment results in an error:
@F.udf(T.IntegerType())
def simple_udf(t: str) -> str:
return answer * 3.14159
Which code fragment should be used instead?

A) @F.udf(T.IntegerType())
def simple_udf(t: int) -> int:
return t * 3.14159
B) @F.udf(T.IntegerType())
def simple_udf(t: float) -> float:
return t * 3.14159
C) @F.udf(T.DoubleType())
def simple_udf(t: float) -> float:
return t * 3.14159
D) @F.udf(T.DoubleType())
def simple_udf(t: int) -> int:
return t * 3.14159

3. 43 of 55.
An organization has been running a Spark application in production and is considering disabling the Spark History Server to reduce resource usage.
What will be the impact of disabling the Spark History Server in production?

A) Improved job execution speed due to reduced logging overhead
B) Enhanced executor performance due to reduced log size
C) Loss of access to past job logs and reduced debugging capability for completed jobs
D) Prevention of driver log accumulation during long-running jobs

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

5. A Spark application is experiencing performance issues in client mode because the driver is resource-constrained.
How should this issue be resolved?

A) Switch the deployment mode to cluster mode
B) Increase the driver memory on the client machine
C) Add more executor instances to the cluster
D) Switch the deployment mode to local mode

Solutions: