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

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

2. 18 of 55.
An engineer has two DataFrames - df1 (small) and df2 (large). To optimize the join, the engineer uses a broadcast join:
from pyspark.sql.functions import broadcast
df_result = df2.join(broadcast(df1), on="id", how="inner")
What is the purpose of using broadcast() in this scenario?

A) It ensures that the join happens only when the id values are identical.
B) It filters the id values before performing the join.
C) It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
D) It increases the partition size for df1 and df2.

3. 35 of 55.
A data engineer is building a Structured Streaming pipeline and wants it to recover from failures or intentional shutdowns by continuing where it left off.
How can this be achieved?

A) By configuring the option checkpointLocation during writeStream.
B) By configuring the option recoveryLocation during writeStream.
C) By configuring the option recoveryLocation during SparkSession initialization.
D) By configuring the option checkpointLocation during readStream.

4. A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:

The resulting Python dictionary must contain a mapping of region -> region id containing the smallest 3 region_id values.
Which code fragment meets the requirements?
A)

B)

C)

D)

The resulting Python dictionary must contain a mapping of region -> region_id for the smallest 3 region_id values.
Which code fragment meets the requirements?

A) regions = dict(
regions_df
.select('region_id', 'region')
.sort('region_id')
.take(3)
)
B) regions = dict(
regions_df
.select('region', 'region_id')
.sort(desc('region_id'))
.take(3)
)
C) regions = dict(
regions_df
.select('region', 'region_id')
.sort('region_id')
.take(3)
)
D) regions = dict(
regions_df
.select('region_id', 'region')
.limit(3)
.collect()
)

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

Solutions: