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

1. How can a Spark developer ensure optimal resource utilization when running Spark jobs in Local Mode for testing?
Options:

A) Set the spark.executor.memory property to a large value.
B) Use the spark.dynamicAllocation.enabled property to scale resources dynamically.
C) Increase the number of local threads based on the number of CPU cores.
D) Configure the application to run in cluster mode instead of local mode.

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

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

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

3. 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 compresses the data to save disk space.
C) It optimizes by reading only the relevant subset of data from fewer partitions.
D) It ensures that data is loaded into memory all at once for faster query execution.

4. 4 of 55.
A developer is working on a Spark application that processes a large dataset using SQL queries. Despite having a large cluster, the developer notices that the job is underutilizing the available resources. Executors remain idle for most of the time, and logs reveal that the number of tasks per stage is very low. The developer suspects that this is causing suboptimal cluster performance.
Which action should the developer take to improve cluster utilization?

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

5. What is the behavior for function date_sub(start, days) if a negative value is passed into the days parameter?

A) An error message of an invalid parameter will be returned
B) The number of days specified will be removed from the start date
C) The number of days specified will be added to the start date
D) The same start date will be returned

Solutions: