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

1. 10 of 55.
What is the benefit of using Pandas API on Spark for data transformations?

A) It is available only with Python, thereby reducing the learning curve.
B) It runs on a single node only, utilizing memory efficiently.
C) It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
D) It computes results immediately using eager execution.

2. 16 of 55.
A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior? (Choose 2 answers)

A) The Spark engine requires manual intervention to start executing transformations.
B) Only actions trigger the execution of the transformation pipeline.
C) Transformations are evaluated lazily.
D) Transformations are executed immediately to build the lineage graph.
E) The Spark engine optimizes the execution plan during the transformations, causing delays.

3. A data engineer is running a batch processing job on a Spark cluster with the following configuration:
10 worker nodes
16 CPU cores per worker node
64 GB RAM per node
The data engineer wants to allocate four executors per node, each executor using four cores.
What is the total number of CPU cores used by the application?

A) 80
B) 160
C) 40
D) 64

4. 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 a regular Spark UDF:
from pyspark.sql.functions import mean
df.groupBy("user_id").agg(mean("value")).show()
C) Use the applyInPandas API:
df.groupby("user_id").applyInPandas(mean_func, schema="user_id long, value double").show()
D) Use the mapInPandas API:
df.mapInPandas(mean_func, schema="user_id long, value double").show()

5. Which Spark configuration controls the number of tasks that can run in parallel on the executor?
Options:

A) spark.executor.cores
B) spark.executor.memory
C) spark.task.maxFailures
D) spark.driver.cores

Solutions: