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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. 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.

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

3. A data analyst wants to add a column date derived from a timestamp column.
Options:

A) dates_df.withColumn("date", f.date_format("timestamp", "yyyy-MM-dd")).show()
B) dates_df.withColumn("date", f.unix_timestamp("timestamp")).show()
C) dates_df.withColumn("date", f.to_date("timestamp")).show()
D) dates_df.withColumn("date", f.from_unixtime("timestamp")).show()

4. The following code fragment results in an error:
@F.udf(T.IntegerType())
def simple_udf(t: str) -> str:
return answer * 3.14159
Which code fragment should be used instead?

A) @F.udf(T.IntegerType())
def simple_udf(t: int) -> int:
return t * 3.14159
B) @F.udf(T.IntegerType())
def simple_udf(t: float) -> float:
return t * 3.14159
C) @F.udf(T.DoubleType())
def simple_udf(t: float) -> float:
return t * 3.14159
D) @F.udf(T.DoubleType())
def simple_udf(t: int) -> int:
return t * 3.14159

5. 13 of 55.
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:
region_id
region_name
10
North
12
East
14
West
The resulting Python dictionary must contain a mapping of region_id to region_name, containing the smallest 3 region_id values.
Which code fragment meets the requirements?

A) regions_dict = regions.select("region_id", "region_name").take(3)
B) regions_dict = dict(regions.orderBy("region_id").limit(3).rdd.map(lambda x: (x.region_id, x.region_name)).collect())
C) regions_dict = dict(regions.select("region_id", "region_name").rdd.collect())
D) regions_dict = dict(regions.take(3))

Solutions: