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

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

2. 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) The operation will fail if the Pandas DataFrame exceeds 1000 rows
C) The operation will load all data into the driver's memory, potentially causing memory overflow
D) Data will be lost during conversion

3. What is the difference betweendf.cache()anddf.persist()in Spark DataFrame?

A) Both functions perform the same operation. Thepersist()function provides improved performance asits default storage level isDISK_ONLY.
B) Bothcache()andpersist()can be used to set the default storage level (MEMORY_AND_DISK_SER)
C) persist()- Persists the DataFrame with the default storage level (MEMORY_AND_DISK_SER) andcache()- Can be used to set different storage levels to persist the contents of the DataFrame.
D) cache()- Persists the DataFrame with the default storage level (MEMORY_AND_DISK) andpersist()- Can be used to set different storage levels to persist the contents of the DataFrame

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

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

5. 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)

B)

C)

D)

A) result_df = prices_df \
.agg(F.count_if(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.min("spot_price"), F.max("spot_price"))
D) result_df = prices_df \
.agg(F.count("spot_price").alias("spot_price")) \
.filter(F.col("spot_price") > F.lit("min_price"))

Solutions: