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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. A DataFrame df has columns name, age, and salary. The developer needs to sort the DataFrame by age in ascending order and salary in descending order.
Which code snippet meets the requirement of the developer?

A) df.sort("age", "salary", ascending=[False, True]).show()
B) df.orderBy("age", "salary", ascending=[True, False]).show()
C) df.sort("age", "salary", ascending=[True, True]).show()
D) df.orderBy(col("age").asc(), col("salary").asc()).show()

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

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

3. 30 of 55.
A data engineer is working on a num_df DataFrame and has a Python UDF defined as:
def cube_func(val):
return val * val * val
Which code fragment registers and uses this UDF as a Spark SQL function to work with the DataFrame num_df?

A) num_df.select(cube_func("num")).show()
B) num_df.register("cube_func").select("num").show()
C) spark.udf.register("cube_func", cube_func)
num_df.selectExpr("cube_func(num)").show()
D) spark.createDataFrame(cube_func("num")).show()

4. A developer is running Spark SQL queries and notices underutilization of resources. Executors are idle, and the number of tasks per stage is low.
What should the developer do to improve cluster utilization?

A) Enable dynamic resource allocation to scale resources as needed
B) Increase the value of spark.sql.shuffle.partitions
C) Increase the size of the dataset to create more partitions
D) Reduce the value of spark.sql.shuffle.partitions

5. What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

A) The conversion will automatically distribute the data across worker nodes
B) Data will be lost during conversion
C) The operation will fail if the Pandas DataFrame exceeds 1000 rows
D) The operation will load all data into the driver's memory, potentially causing memory overflow

Solutions: