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

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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. 48 of 55.
A data engineer needs to join multiple DataFrames and has written the following code:
from pyspark.sql.functions import broadcast
data1 = [(1, "A"), (2, "B")]
data2 = [(1, "X"), (2, "Y")]
data3 = [(1, "M"), (2, "N")]
df1 = spark.createDataFrame(data1, ["id", "val1"])
df2 = spark.createDataFrame(data2, ["id", "val2"])
df3 = spark.createDataFrame(data3, ["id", "val3"])
df_joined = df1.join(broadcast(df2), "id", "inner") \
.join(broadcast(df3), "id", "inner")
What will be the output of this code?

A) The code will work correctly and perform two broadcast joins simultaneously to join df1 with df2, and then the result with df3.
B) The code will fail because only one broadcast join can be performed at a time.
C) The code will result in an error because broadcast() must be called before the joins, not inline.
D) The code will fail because the second join condition (df2.id == df3.id) is incorrect.

2. A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?
Options:

A) Use the repartition() transformation with a lower number of partitions
B) Use the coalesce() transformation with a lower number of partitions
C) Use the sortBy() transformation to reorganize the data
D) Use the distinct() transformation to combine similar partitions

3. 32 of 55.
A developer is creating a Spark application that performs multiple DataFrame transformations and actions. The developer wants to maintain optimal performance by properly managing the SparkSession.
How should the developer handle the SparkSession throughout the application?

A) Create a new SparkSession instance before each transformation.
B) Avoid using a SparkSession and rely on SparkContext only.
C) Stop and restart the SparkSession after each action.
D) Use a single SparkSession instance for the entire application.

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

5. 28 of 55.
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) coalesce
B) filter
C) select
D) groupBy

Solutions: