Databricks Certified Associate Developer for Apache Spark Q&A: Return new DataFrame result of inner join between DataFrame storesDF and DataFrame employeesDF.

Question

The code block shown below contains an error. The code block intended to return a new DataFrame that is the result of an inner join between DataFrame storesDF and DataFrame employeesDF on column storeId. Identify the error.

Code block:

StoresDF.join(employeesDF, "inner", "storeID")

A. The key column storeID needs to be wrapped in the col() operation.
B. The key column storeID needs to be in a list like [“storeID”].
C. The key column storeID needs to be specified in an expression of both DataFrame columns like storesDF.storeId == employeesDF.storeId.
D. There is no DataFrame.join() operation – DataFrame.merge() should be used instead.
E. The column key is the second parameter to join() and the type of join in the third parameter to join() – the second and third arguments should be switched.

Answer

E. The column key is the second parameter to join() and the type of join in the third parameter to join() – the second and third arguments should be switched.

Explanation 1

According to the Databricks website, this exam assesses the understanding of the Spark DataFrame API and the ability to apply the Spark DataFrame API to complete basic data manipulation tasks within a Spark session.

Code block: StoresDF.join(employeesDF, “inner”, “storeID”)

The error is in the order of the arguments for the join() operation. According to the PySpark documentation, the join() operation takes three arguments: other, on and how. The other argument is the right side of the join, the on argument is the join expression or column name(s), and the how argument is the type of join. In this case, the code block has switched the on and how arguments, which will cause an error.

The correct way to write the code block is:

Code block: StoresDF.join(employeesDF, “storeID”, “inner”)

Therefore, the correct answer is option E: The column key is the second parameter to join() and the type of join in the third parameter to join() – the second and third arguments should be switched.

Explanation 2

Code block: StoresDF.join(employeesDF, “inner”, “storeID”)

The correct answer is C. The key column storeID needs to be specified in an expression of both DataFrame columns like storesDF.storeId == employeesDF.storeId.

This is because the join() method requires that the join condition be specified as an expression that evaluates to a boolean value. The expression should be specified as a condition that compares the columns from both DataFrames that are being joined.

For example, if you want to join two DataFrames on the column “storeId”, you would use the following code:

storesDF.join(employeesDF, storesDF.storeId == employeesDF.storeId, "inner")

Explanation 3

The correct answer is **C**. The key column storeID needs to be specified in an expression of both DataFrame columns like storesDF.storeId == employeesDF.storeId.

The join() method takes the following parameters:

python
join(other, on=None, how=None)

where:

other: Right side of the join.
on: Columns (names) to join on. Must be found in both DataFrames. If not specified and no other join keys given explicitly, join will use intersection of keys from both frames.
how: One of ‘inner’, ‘outer’, ‘left_outer’, ‘right_outer’, ‘leftsemi’. Default is ‘inner’.

In the code block shown below:

python
StoresDF.join(employeesDF, "inner", "storeID")

the key column storeID needs to be specified in an expression of both DataFrame columns like storesDF.storeId == employeesDF.storeId.

Explanation 4

The correct answer is E. The column key is the second parameter to join() and the type of join is in the third parameter to join(). The code block should be written as follows:
Code snippet

StoresDF.join(employeesDF, "storeID", "inner")

The other answers are incorrect:

A. The key column does not need to be wrapped in the col() operation.
B. The key column does not need to be in a list.
C. The key column can be specified as a simple column name, without an expression.
D. There is a join() operation in DataFrame.

Explanation 5

The correct answer is E. The column key is the second parameter to join() and the type of join in the third parameter to join() – the second and third arguments should be switched. Here is a detailed explanation:

E. The column key is the second parameter to join() and the type of join in the third parameter to join() – the second and third arguments should be switched.

This is true because the join() operation of DataFrames uses standard SQL semantics for join operations. The syntax of the join() operation is as follows:

join(other, on=None, how=None)

where other is another DataFrame, on is a column name or a list of column names, and how is a string specifying the type of join. The code block shown in the question has the on and how arguments in reverse order, which will cause an error. The correct code block should be:

StoresDF.join(employeesDF, “storeID”, “inner”)

A. The key column storeID needs to be wrapped in the col() operation. This is false because the col() operation is not necessary when specifying a single column name or a list of column names as the on argument. The col() operation is used to create a Column object that can be used for expressions or conditions.

B. The key column storeID needs to be in a list like [“storeID”]. This is false because the on argument can accept either a single column name or a list of column names. Both forms are equivalent when joining on a single column.

C. The key column storeID needs to be specified in an expression of both DataFrame columns like storesDF.storeId == employeesDF.storeId. This is false because this form of expression is only needed when joining on multiple columns or when using different column names in each DataFrame. When joining on a single column with the same name in both DataFrames, it is sufficient to use the column name as the on argument.

D. There is no DataFrame.join() operation – DataFrame.merge() should be used instead. This is false because there is a DataFrame.join() operation in PySpark that performs SQL-style joins on DataFrames. DataFrame.merge() is a pandas operation that performs similar functionality but with different syntax and options.

Explanation 6

The correct answer is E. The column key is the second parameter to join() and the type of join is in the third parameter to join(). In the code block, the column key is storeID and the type of join is inner, so the order of the arguments should be switched.

The correct code block would be:
Python

StoresDF.join(employeesDF, "storeID", "inner")

The other answers are incorrect.

Answer A is incorrect because the col() operation is not necessary. The column storeID can be used directly as the key.
Answer B is incorrect because the key column does not need to be in a list.
Answer C is incorrect because the key column does not need to be specified in an expression of both DataFrame columns.
Answer D is incorrect because there is a join() operation in DataFrames.

Explanation 7

The error in the given code block is in option B. The key column storeID needs to be in a list like [“storeID”].

When performing a join operation in Apache Spark using the DataFrame API, the column(s) used for joining should be specified as a list of column names. In the given code block, the column “storeID” should be passed as a list to the join() method. Here’s the corrected code:

“`python
StoresDF.join(employeesDF, [“storeID”], “inner”)
“`

By passing the column name as a list, the join operation will correctly use the specified column for the inner join between the two DataFrames.

Option A is not the correct answer because the col() operation is not necessary in this case. It is used when you want to apply transformations or conditions on a specific column.

Option C is also not the correct answer because specifying the column in an expression of both DataFrame columns is not required for a simple join operation.

Option D is incorrect because DataFrame.join() is the correct method to perform joins in Apache Spark. DataFrame.merge() is not a valid method in Spark.

Option E is incorrect because the order of the parameters in the join() method is correct in the given code block. The second parameter specifies the column(s) to join on, and the third parameter specifies the type of join (“inner” in this case).

Therefore, the correct answer is B. The key column storeID needs to be in a list like [“storeID”].

Explanation 8

The correct answer is E. The column key is the second parameter to join() and the type of join in the third parameter to join(). The code block should be changed to:
Code snippet

StoresDF.join(employeesDF, "storeID", "inner")

The other answers are incorrect:

A. The col() operation is not necessary in this case because the column name is already a valid column reference.
B. The key column does not need to be in a list.
C. The key column does not need to be specified in an expression of both DataFrame columns.
D. There is a join() operation in Databricks.

Explanation 9

The error in the given code block is that the key column storeID needs to be in a list like [“storeID”] (B). The join() operation requires the key column name to be in a list format, as the second argument. The correct syntax for the join operation in this case should be storesDF.join(employeesDF, [“storeID”], “inner”). Option A is incorrect as the col() operation is not necessary in this case. Option C is also incorrect as specifying the key column name with the dot notation is not required. Option D is incorrect as DataFrame.merge() is not a valid operation, and Option E is incorrect as switching the second and third arguments will not fix the issue.

Explanation 10

The error in the provided code block is:

B. The key column storeID needs to be in a list like [“storeID”].

Explanation:

When using the join() operation to join two DataFrames, the key column(s) used for the join should be specified as a list of column names. In the provided code block, the key column storeID is not specified as a list, which causes an error.

The correct code block to achieve an inner join between DataFrame storesDF and DataFrame employeesDF on column storeID would be:

storesDF.join(employeesDF, ["storeId"], "inner")

Explanation of the corrections made:

The key column storeID is specified as a list containing one element: [“storeId”].
The type of join is specified as “inner” in the third parameter of join().
The join() operation is performed on the DataFrame storesDF, with the DataFrame employeesDF passed as an argument.

Note that there are other ways to specify the key column(s) used for the join, such as using the col() function or specifying an expression of both DataFrame columns. However, using a list of column names is the most commonly used and straightforward method.

Explanation 11

The correct answer is C. The key column storeID needs to be specified in an expression of both DataFrame columns like storesDF.storeId == employeesDF.storeId.

The error in the code block is that the join() operation expects either a single column name or a list of column names as the second argument, not a string literal. If a single column name is given, it will perform an equi-join on that column. If a list of column names is given, it will perform an equi-join on all the columns in the list. However, if a string literal is given, it will be interpreted as the join type, and the join operation will fail.

To fix the error, we need to either use a single column name or a list of column names as the second argument, and specify the join type as the third argument. Alternatively, we can use an expression of both DataFrame columns as the second argument, and omit the join type argument. This will also perform an equi-join on the specified column.

The other options are incorrect for the following reasons:

A. The key column storeID does not need to be wrapped in the col() operation. The col() operation returns a Column object that can be used to refer to a column in a DataFrame. However, the join() operation can accept either a Column object or a string as the column name argument.

B. The key column storeID does not need to be in a list like [“storeID”]. This is one possible way to fix the error, but not the only one. As mentioned above, we can also use a single column name or an expression of both DataFrame columns as the second argument.

D. There is a DataFrame.join() operation – DataFrame.merge() should not be used instead. The merge() operation is a Pandas method that can be used to join two DataFrames based on common columns or indexes. However, Spark DataFrames do not have a merge() method, and instead use the join() method to perform various types of joins.

E. The column key is not the second parameter to join() and the type of join is not the third parameter to join() – the second and third arguments should not be switched. As explained above, the join() operation expects either a single column name or a list of column names as the second argument, and optionally a string literal as the third argument to specify the join type. Switching them will result in an invalid syntax.

Explanation 12

The correct answer is:

E. The column key is the second parameter to join() and the type of join is the third parameter to join() – the second and third arguments should be switched.

Explanation:

In Apache Spark, the join operation is performed using the join() function. The join() function takes three parameters: the DataFrame to join with, the join expression, and the type of join. In the provided code block, the join type (“inner”) and the join expression (“storeID”) are in the wrong order. The correct syntax should be:

python
StoresDF.join(employeesDF, "storeID", "inner")

Here, “storeID” is the column on which the join operation is performed, and “inner” specifies the type of join. This will return a new DataFrame that is the result of an inner join between DataFrame storesDF and DataFrame employeesDF on the column storeId.

Reference

Databricks Certified Associate Developer for Apache Spark certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Databricks Certified Associate Developer for Apache Spark exam and earn Databricks Certified Associate Developer for Apache Spark certification.

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