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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:

1. A Spark DataFrame df is cached using the MEMORY_AND_DISK storage level, but the DataFrame is too large to fit entirely in memory.
What is the likely behavior when Spark runs out of memory to store the DataFrame?

A) Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.
B) Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
C) Spark duplicates the DataFrame in both memory and disk. If it doesn't fit in memory, the DataFrame is stored and retrieved from the disk entirely.
D) Spark will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead.

2. A developer is working with a pandas DataFrame containing user behavior data from a web application.
Which approach should be used for executing a groupBy operation in parallel across all workers in Apache Spark 3.5?
A)
Use the applylnPandas API
B)

C)

A) Use a Pandas UDF:
@pandas_udf("double")
def mean_func(value: pd.Series) -> float:
return value.mean()
df.groupby("user_id").agg(mean_func(df["value"])).show()
B) Use the applyInPandas API:
df.groupby("user_id").applyInPandas(mean_func, schema="user_id long, value double").show()
C) Use the mapInPandas API:
df.mapInPandas(mean_func, schema="user_id long, value double").show()
D) Use a regular Spark UDF:
from pyspark.sql.functions import mean
df.groupBy("user_id").agg(mean("value")).show()

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

4. What is a feature of Spark Connect?

A) It supports DataStreamReader, DataStreamWriter, StreamingQuery, and Streaming APIs
B) It has built-in authentication
C) It supports only PySpark applications
D) Supports DataFrame, Functions, Column, SparkContext PySpark APIs

5. 11 of 55.
Which Spark configuration controls the number of tasks that can run in parallel on an executor?

A) spark.executor.memory
B) spark.executor.cores
C) spark.sql.shuffle.partitions
D) spark.task.maxFailures

Solutions: