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

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

2. You have:
DataFrame A: 128 GB of transactions
DataFrame B: 1 GB user lookup table
Which strategy is correct for broadcasting?

A) DataFrame A should be broadcasted because it is smaller and will eliminate the need for shuffling itself
B) DataFrame A should be broadcasted because it is larger and will eliminate the need for shuffling DataFrame B
C) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling itself
D) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling DataFrame A

3. 31 of 55.
Given a DataFrame df that has 10 partitions, after running the code:
df.repartition(20)
How many partitions will the result DataFrame have?

A) Same number as the cluster executors
B) 5
C) 10
D) 20

4. A data engineer noticed improved performance after upgrading from Spark 3.0 to Spark 3.5. The engineer found that Adaptive Query Execution (AQE) was enabled.
Which operation is AQE implementing to improve performance?

A) Collecting persistent table statistics and storing them in the metastore for future use
B) Optimizing the layout of Delta files on disk
C) Dynamically switching join strategies
D) Improving the performance of single-stage Spark jobs

5. A data engineer is running a Spark job to process a dataset of 1 TB stored in distributed storage. The cluster has 10 nodes, each with 16 CPUs. Spark UI shows:
Low number of Active Tasks
Many tasks complete in milliseconds
Fewer tasks than available CPUs
Which approach should be used to adjust the partitioning for optimal resource allocation?

A) Set the number of partitions to a fixed value, such as 200
B) Set the number of partitions equal to the number of nodes in the cluster
C) Set the number of partitions equal to the total number of CPUs in the cluster
D) Set the number of partitions by dividing the dataset size (1 TB) by a reasonable partition size, such as 128 MB

Solutions: