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

1. Which command overwrites an existing JSON file when writing a DataFrame?

A) df.write.overwrite.json("path/to/file")
B) df.write.json("path/to/file", overwrite=True)
C) df.write.format("json").save("path/to/file", mode="overwrite")
D) df.write.mode("overwrite").json("path/to/file")

2. 2 of 55. Which command overwrites an existing JSON file when writing a DataFrame?

A) df.write.mode("append").json("path/to/file")
B) df.write.mode("overwrite").json("path/to/file")
C) df.write.json("path/to/file")
D) df.write.option("overwrite").json("path/to/file")

3. A Spark developer is building an app to monitor task performance. They need to track the maximum task processing time per worker node and consolidate it on the driver for analysis.
Which technique should be used?

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

4. 45 of 55.
Which feature of Spark Connect should be considered when designing an application that plans to enable remote interaction with a Spark cluster?

A) It is primarily used for data ingestion into Spark from external sources.
B) It provides a way to run Spark applications remotely in any programming language.
C) It allows for remote execution of Spark jobs.
D) It can be used to interact with any remote cluster using the REST API.

5. 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")

Solutions: