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Questions and Answers
What method in PySpark can be used to convert the data type of a column?
What method in PySpark can be used to convert the data type of a column?
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Which of the following is NOT a common aggregate function in PySpark?
Which of the following is NOT a common aggregate function in PySpark?
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What is the purpose of join in PySpark?
What is the purpose of join in PySpark?
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Which type of join in PySpark retains only the rows present in both tables being joined?
Which type of join in PySpark retains only the rows present in both tables being joined?
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How can you create a new column in a PySpark DataFrame by multiplying two existing columns?
How can you create a new column in a PySpark DataFrame by multiplying two existing columns?
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Which function is used in PySpark to perform conditional operations while adding a new column?
Which function is used in PySpark to perform conditional operations while adding a new column?
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Which of the following methods is used to display the first few rows of a PySpark DataFrame?
Which of the following methods is used to display the first few rows of a PySpark DataFrame?
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How can you change a specific column's data type to integer in PySpark DataFrame?
How can you change a specific column's data type to integer in PySpark DataFrame?
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What function can be used to add a new column in a PySpark DataFrame based on a condition?
What function can be used to add a new column in a PySpark DataFrame based on a condition?
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Which of the following is NOT a valid method for loading data into PySpark?
Which of the following is NOT a valid method for loading data into PySpark?
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Which transformation in PySpark can be used to select specific columns from a DataFrame?
Which transformation in PySpark can be used to select specific columns from a DataFrame?
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What is the purpose of the groupBy() function in PySpark?
What is the purpose of the groupBy() function in PySpark?
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Which function in PySpark can be used to remove duplicate rows from a DataFrame?
Which function in PySpark can be used to remove duplicate rows from a DataFrame?
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Which of the following is NOT a valid operation that can be performed using the GroupBy function in PySpark?
Which of the following is NOT a valid operation that can be performed using the GroupBy function in PySpark?
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What is the purpose of the RDD (Resilient Distributed Dataset) in PySpark?
What is the purpose of the RDD (Resilient Distributed Dataset) in PySpark?
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What function in PySpark can be used to calculate the maximum value of a column in a DataFrame?
What function in PySpark can be used to calculate the maximum value of a column in a DataFrame?
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When performing aggregations in PySpark, which function is NOT typically used?
When performing aggregations in PySpark, which function is NOT typically used?
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Which type of join in PySpark combines all rows from two tables, keeping NULL values for missing matches?
Which type of join in PySpark combines all rows from two tables, keeping NULL values for missing matches?
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In PySpark, which function is used to perform group-wise aggregation on a DataFrame?
In PySpark, which function is used to perform group-wise aggregation on a DataFrame?
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When joining two DataFrames in PySpark, what method is used to specify the columns on which the join should be performed?
When joining two DataFrames in PySpark, what method is used to specify the columns on which the join should be performed?
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When running a query involving multiple tables in PySpark, what is the importance of explicitly stating column names in the SELECT statement?
When running a query involving multiple tables in PySpark, what is the importance of explicitly stating column names in the SELECT statement?
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What is the purpose of the GROUP BY clause in PySpark SQL?
What is the purpose of the GROUP BY clause in PySpark SQL?
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In PySpark, which type of join retains all rows from both tables regardless of match and fills in missing values with nulls?
In PySpark, which type of join retains all rows from both tables regardless of match and fills in missing values with nulls?
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When executing an SQL query in PySpark, why is it important to qualify the table name for columns that are being selected?
When executing an SQL query in PySpark, why is it important to qualify the table name for columns that are being selected?
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