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

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("spot_price").alias("spot_price")) \
.filter(F.col("spot_price") > F.lit("min_price"))
B)

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

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

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

3. 22 of 55.
A Spark application needs to read multiple Parquet files from a directory where the files have differing but compatible schemas.
The data engineer wants to create a DataFrame that includes all columns from all files.
Which code should the data engineer use to read the Parquet files and include all columns using Apache Spark?

A) spark.read.format("parquet").option("inferSchema", "true").load("/data/parquet/")
B) spark.read.option("mergeSchema", True).parquet("/data/parquet/")
C) spark.read.parquet("/data/parquet/")
D) spark.read.parquet("/data/parquet/").option("mergeAllCols", True)

4. 36 of 55.
What is the main advantage of partitioning the data when persisting tables?

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

5. 39 of 55.
A Spark developer is developing a Spark application to monitor task performance across a cluster.
One requirement is to track the maximum processing time for tasks on each worker node and consolidate this information on the driver for further analysis.
Which technique should the developer use?

A) Broadcast a variable to share the maximum time among workers.
B) Use an accumulator to record the maximum time on the driver.
C) Configure the Spark UI to automatically collect maximum times.
D) Use an RDD action like reduce() to compute the maximum time.

Solutions: