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

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

2. A data engineer needs to write a DataFrame df to a Parquet file, partitioned by the column country, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?

A) df.write.mode("overwrite").parquet("/data/output")
B) df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
C) df.write.partitionBy("country").parquet("/data/output")
D) df.write.mode("append").partitionBy("country").parquet("/data/output")

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

4. A developer wants to test Spark Connect with an existing Spark application.
What are the two alternative ways the developer can start a local Spark Connect server without changing their existing application code? (Choose 2 answers)

A) Add .remote("sc://localhost") to their SparkSession.builder calls in their Spark code
B) Execute their pyspark shell with the option --remote "sc://localhost"
C) Ensure the Spark property spark.connect.grpc.binding.port is set to 15002 in the application code
D) Execute their pyspark shell with the option --remote "https://localhost"
E) Set the environment variable SPARK_REMOTE="sc://localhost" before starting the pyspark shell

5. A developer runs:

What is the result?
Options:

A) It throws an error if there are null values in either partition column.
B) It creates separate directories for each unique combination of color and fruit.
C) It stores all data in a single Parquet file.
D) It appends new partitions to an existing Parquet file.

Solutions: