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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:

Question 1

10 of 55.
What is the benefit of using Pandas API on Spark for data transformations?

A. It runs on a single node only, utilizing memory efficiently.
B. It computes results immediately using eager execution.
C. It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
D. It is available only with Python, thereby reducing the learning curve.

Question 2

What is a feature of Spark Connect?

A. It supports only PySpark applications
B. Supports DataFrame, Functions, Column, SparkContext PySpark APIs
C. It supports DataStreamReader, DataStreamWriter, StreamingQuery, and Streaming APIs
D. It has built-in authentication

Question 3

A data engineer has been asked to produce a Parquet table which is overwritten every day with the latest data. The downstream consumer of this Parquet table has a hard requirement that the data in this table is produced with all records sorted by the market_time field.
Which line of Spark code will produce a Parquet table that meets these requirements?

A. final_df \
.sort("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
B. final_df \
.sort("market_time") \
.coalesce(1) \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
C. final_df \
.sortWithinPartitions("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
D. final_df \
.orderBy("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")

Question 4

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

Question 5

A data engineer observes that an upstream streaming source sends duplicate records, where duplicates share the same key and have at most a 30-minute difference in event_timestamp. The engineer adds:
dropDuplicatesWithinWatermark("event_timestamp", "30 minutes")
What is the result?

A. It removes all duplicates regardless of when they arrive
B. It removes duplicates that arrive within the 30-minute window specified by the watermark
C. It is not able to handle deduplication in this scenario
D. It accepts watermarks in seconds and the code results in an error

Solutions: