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

1. Given a DataFrame df that has 10 partitions, after running the code:
result = df.coalesce(20)
How many partitions will the result DataFrame have?

A) 20
B) 1
C) 10
D) Same number as the cluster executors

2. Given the code fragment:

import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A) psdf.to_spark()
B) psdf.to_pandas()
C) psdf.to_dataframe()
D) psdf.to_pyspark()

3. 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 can be used to interact with any remote cluster using the REST API.
D) It allows for remote execution of Spark jobs.

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

5. A Data Analyst needs to retrieve employees with 5 or more years of tenure.
Which code snippet filters and shows the list?

A) filter(employees_df.tenure >= 5)
B) employees_df.filter(employees_df.tenure >= 5).collect()
C) employees_df.where(employees_df.tenure >= 5)
D) employees_df.filter(employees_df.tenure >= 5).show()

Solutions: