Alternatively, you can add the jar file into your spark shell. Download the spark-xml_2.10-0.2.0.jar jar file and copy into the spark's class path and add the jar file in your spark shell using the :cp command as

:cp spark-xml_2.10-0.2.0.jar  
/*
  jar file will get imported into the spark shell
  now you can use this jar file anywhere in your code inside the spark shell.
*/
val rd = spark.read.format("com.databricks.spark.xml").load("C:/Users/kumar/Desktop/d.xml")

Match the Scala version to that of Spark. Starting version 2.0, Spark is built with Scala 2.11 by default. Scala 2.10 users should need the Spark source package and build with Scala 2.10 support. This may help

Compatibility issue with Scala and Spark for compiled jars
spark-xml

Databricks has released new version to read xml to Spark DataFrame

<dependency>
     <groupId>com.databricks</groupId>
     <artifactId>spark-xml_2.12</artifactId>
     <version>0.6.0</version>
 </dependency>

Input XML file I used on this example is available at GitHub repository.

val df = spark.read
      .format("com.databricks.spark.xml")
      .option("rowTag", "person")
      .xml("persons.xml")

Schema

root
 |-- _id: long (nullable = true)
 |-- dob_month: long (nullable = true)
 |-- dob_year: long (nullable = true)
 |-- firstname: string (nullable = true)
 |-- gender: string (nullable = true)
 |-- lastname: string (nullable = true)
 |-- middlename: string (nullable = true)
 |-- salary: struct (nullable = true)
 |    |-- _VALUE: long (nullable = true)
 |    |-- _currency: string (nullable = true)

Outputs:

+---+---------+--------+---------+------+--------+----------+---------------+
|_id|dob_month|dob_year|firstname|gender|lastname|middlename|         salary|
+---+---------+--------+---------+------+--------+----------+---------------+
|  1|        1|    1980|    James|     M|   Smith|      null|  [10000, Euro]|
|  2|        6|    1990|  Michael|     M|    null|      Rose|[10000, Dollor]|
+---+---------+--------+---------+------+--------+----------+---------------+

Note that Spark XML API has some limitations and discussed here Spark-XML API Limitations

Hope this helps !!