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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

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

Question 1

Which feature of Spark Connect is considered when designing an application to enable remote interaction with the Spark cluster?

A. It provides a way to run Spark applications remotely in any programming language
B. It can be used to interact with any remote cluster using the REST API
C. It allows for remote execution of Spark jobs
D. It is primarily used for data ingestion into Spark from external sources

Question 2

47 of 55.
A data engineer has written the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv")
df2 = spark.read.csv("product_data.csv")
df_joined = df1.join(df2, df1.product_id == df2.product_id)
The DataFrame df1 contains ~10 GB of sales data, and df2 contains ~8 MB of product data.
Which join strategy will Spark use?

A. Broadcast join, as df2 is smaller than the default broadcast threshold.
B. Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently.
C. Shuffle join, because AQE is not enabled, and Spark uses a static query plan.
D. Shuffle join because no broadcast hints were provided.

Question 3

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

Question 4

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. Loss of access to past job logs and reduced debugging capability for completed jobs
C. Enhanced executor performance due to reduced log size
D. Prevention of driver log accumulation during long-running jobs

Question 5

20 of 55.
What is the difference between df.cache() and df.persist() in Spark DataFrame?

A. persist() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and cache() - Can be used to set different storage levels.
B. cache() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and persist() - Can be used to set different storage levels to persist the contents of the DataFrame.
C. Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_DESER).
D. Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.

Solutions: