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

2. 41 of 55.
A data engineer is working on the DataFrame df1 and wants the Name with the highest count to appear first (descending order by count), followed by the next highest, and so on.
The DataFrame has columns:
id | Name | count | timestamp
---------------------------------
1 | USA | 10
2 | India | 20
3 | England | 50
4 | India | 50
5 | France | 20
6 | India | 10
7 | USA | 30
8 | USA | 40
Which code fragment should the engineer use to sort the data in the Name and count columns?

A) df1.orderBy(col("count").desc(), col("Name").asc())
B) df1.orderBy(col("Name").desc(), col("count").asc())
C) df1.sort("Name", "count")
D) df1.orderBy("Name", "count")

3. 24 of 55.
Which code should be used to display the schema of the Parquet file stored in the location events.parquet?

A) spark.sql("SELECT * FROM events.parquet").show()
B) spark.read.parquet("events.parquet").printSchema()
C) spark.read.format("parquet").load("events.parquet").show()
D) spark.sql("SELECT schema FROM events.parquet").show()

4. What is the benefit of Adaptive Query Execution (AQE)?

A) It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.
B) It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.
C) It automatically distributes tasks across nodes in the clusters and does not perform runtime adjustments to the query plan.
D) It allows Spark to optimize the query plan before execution but does not adapt during runtime.

5. A data engineer wants to write a Spark job that creates a new managed table. If the table already exists, the job should fail and not modify anything.
Which save mode and method should be used?

A) saveAsTable with mode ErrorIfExists
B) saveAsTable with mode Overwrite
C) save with mode ErrorIfExists
D) save with mode Ignore

Solutions: