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

1. 36 of 55.
What is the main advantage of partitioning the data when persisting tables?

A) It automatically cleans up unused partitions to optimize storage.
B) It optimizes by reading only the relevant subset of data from fewer partitions.
C) It compresses the data to save disk space.
D) It ensures that data is loaded into memory all at once for faster query execution.

2. A developer wants to refactor some older Spark code to leverage built-in functions introduced in Spark 3.5.0. The existing code performs array manipulations manually. Which of the following code snippets utilizes new built-in functions in Spark 3.5.0 for array operations?

A)

result_df = prices_df \
.agg(F.count_if(F.col("spot_price") >= F.lit(min_price)))
B)

result_df = prices_df \
.agg(F.min("spot_price"), F.max("spot_price"))
C)

result_df = prices_df \
.withColumn("valid_price", F.when(F.col("spot_price") > F.lit(min_price), 1).otherwise(0))
D)

result_df = prices_df \
.agg(F.count("spot_price").alias("spot_price")) \
.filter(F.col("spot_price") > F.lit("min_price"))

3. A data scientist has identified that some records in the user profile table contain null values in any of the fields, and such records should be removed from the dataset before processing. The schema includes fields like user_id, username, date_of_birth, created_ts, etc.
The schema of the user profile table looks like this:

Which block of Spark code can be used to achieve this requirement?
Options:

A) filtered_df = users_raw_df.na.drop(thresh=0)
B) filtered_df = users_raw_df.na.drop(how='all')
C) filtered_df = users_raw_df.na.drop(how='any')
D) filtered_df = users_raw_df.na.drop(how='all', thresh=None)

4. 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 fail because the second join condition (df2.id == df3.id) is incorrect.
D) The code will result in an error because broadcast() must be called before the joins, not inline.

5. A Spark application developer wants to identify which operations cause shuffling, leading to a new stage in the Spark execution plan.
Which operation results in a shuffle and a new stage?

A) DataFrame.groupBy().agg()
B) DataFrame.withColumn()
C) DataFrame.select()
D) DataFrame.filter()

Solutions: