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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 DataFrame df has columns name, age, and salary. The developer needs to sort the DataFrame by age in ascending order and salary in descending order.
Which code snippet meets the requirement of the developer?

A) df.orderBy("age", "salary", ascending=[True, False]).show()
B) df.sort("age", "salary", ascending=[True, True]).show()
C) df.sort("age", "salary", ascending=[False, True]).show()
D) df.orderBy(col("age").asc(), col("salary").asc()).show()

2. 15 of 55.
A data engineer is working on a Streaming DataFrame (streaming_df) with the following streaming data:
id
name
count
timestamp
1
Delhi
20
2024-09-19T10:11
1
Delhi
50
2024-09-19T10:12
2
London
50
2024-09-19T10:15
3
Paris
30
2024-09-19T10:18
3
Paris
20
2024-09-19T10:20
4
Washington
10
2024-09-19T10:22
Which operation is supported with streaming_df?

A) streaming_df.select(countDistinct("name"))
B) streaming_df.show()
C) streaming_df.count()
D) streaming_df.filter("count < 30")

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

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

4. 27 of 55.
A data engineer needs to add all the rows from one table to all the rows from another, but not all the columns in the first table exist in the second table.
The error message is:
AnalysisException: UNION can only be performed on tables with the same number of columns.
The existing code is:
au_df.union(nz_df)
The DataFrame au_df has one extra column that does not exist in the DataFrame nz_df, but otherwise both DataFrames have the same column names and data types.
What should the data engineer fix in the code to ensure the combined DataFrame can be produced as expected?

A) df = au_df.unionByName(nz_df, allowMissingColumns=True)
B) df = au_df.union(nz_df, allowMissingColumns=True)
C) df = au_df.unionAll(nz_df)
D) df = au_df.unionByName(nz_df, allowMissingColumns=False)

5. A data engineer is working on a Streaming DataFrame streaming_df with the given streaming data:

Which operation is supported with streamingdf ?

A) streaming_df.orderBy("timestamp").limit(4)
B) streaming_df.filter (col("count") < 30).show()
C) streaming_df.groupby("Id") .count ()
D) streaming_df. select (countDistinct ("Name") )

Solutions: