Spark connector mongodb issue - mongodb

Question

I'm trying to establish a connection between apache spark and mongodb. I have spark version 3.0.0 installed and mongodb 4.2.8 installed on my pc. I am following official documentation to connect but I'm unable to.
When I include the --conf specification while activating it includes error. Although if I only include --package it establishes the connection but then I need conf while creating the dataset so it throws error saying create dataset.
I don't think I have understood how it is installed. Also I couldn't find anything of my version although GitHub site said it supports 3.0.
I am attaching error msg.
C:\WINDOWS\system32>C:\Spark\spark-3.0.0-bin-hadoop2.7\bin\pyspark --conf "spark.mongodb.input.uri=mongodb://127.0.0.1/test.myCollection?readPreference=primaryPreferred" \
Error: pyspark does not support any application options.
Usage: bin\pyspark.cmd [options]
Options:
--master MASTER_URL spark://host:port, mesos://host:port, yarn,
k8s://https://host:port, or local (Default: local[*]).
--deploy-mode DEPLOY_MODE Whether to launch the driver program locally ("client") or
on one of the worker machines inside the cluster ("cluster")
(Default: client).
--class CLASS_NAME Your application\'s main class (for Java / Scala apps).
--name NAME A name of your application.
--jars JARS Comma-separated list of jars to include on the driver
and executor classpaths.
--packages Comma-separated list of maven coordinates of jars to include
on the driver and executor classpaths. Will search the local
maven repo, then maven central and any additional remote
repositories given by --repositories. The format for the
coordinates should be groupId:artifactId:version.
--exclude-packages Comma-separated list of groupId:artifactId, to exclude while
resolving the dependencies provided in --packages to avoid
dependency conflicts.
--repositories Comma-separated list of additional remote repositories to
search for the maven coordinates given with --packages.
--py-files PY_FILES Comma-separated list of .zip, .egg, or .py files to place
on the PYTHONPATH for Python apps.
--files FILES Comma-separated list of files to be placed in the working
directory of each executor. File paths of these files
in executors can be accessed via SparkFiles.get(fileName).
--conf, -c PROP=VALUE Arbitrary Spark configuration property.
--properties-file FILE Path to a file from which to load extra properties. If not
specified, this will look for conf/spark-defaults.conf.
--driver-memory MEM Memory for driver (e.g. 1000M, 2G) (Default: 1024M).
--driver-java-options Extra Java options to pass to the driver.
--driver-library-path Extra library path entries to pass to the driver.
--driver-class-path Extra class path entries to pass to the driver. Note that
jars added with --jars are automatically included in the
classpath.
--executor-memory MEM Memory per executor (e.g. 1000M, 2G) (Default: 1G).
--proxy-user NAME User to impersonate when submitting the application.
This argument does not work with --principal / --keytab.
--help, -h Show this help message and exit.
--verbose, -v Print additional debug output.
--version, Print the version of current Spark.
Cluster deploy mode only:
--driver-cores NUM Number of cores used by the driver, only in cluster mode
(Default: 1).
Spark standalone or Mesos with cluster deploy mode only:
--supervise If given, restarts the driver on failure.
Spark standalone, Mesos or K8s with cluster deploy mode only:
--kill SUBMISSION_ID If given, kills the driver specified.
--status SUBMISSION_ID If given, requests the status of the driver specified.
Spark standalone, Mesos and Kubernetes only:
--total-executor-cores NUM Total cores for all executors.
Spark standalone, YARN and Kubernetes only:
--executor-cores NUM Number of cores used by each executor. (Default: 1 in
YARN and K8S modes, or all available cores on the worker
in standalone mode).
Spark on YARN and Kubernetes only:
--num-executors NUM Number of executors to launch (Default: 2).
If dynamic allocation is enabled, the initial number of
executors will be at least NUM.
--principal PRINCIPAL Principal to be used to login to KDC.
--keytab KEYTAB The full path to the file that contains the keytab for the
principal specified above.
Spark on YARN only:
--queue QUEUE_NAME The YARN queue to submit to (Default: "default").
--archives ARCHIVES Comma separated list of archives to be extracted into the
working directory of each executor.
**This is what happens when i dont include --conf while starting the shell**
>>> from pyspark.sql import SparkSession
>>> spark = SparkSession\
... .builder\
... .master('local')\
... .config('spark.mongodb.input.uri', 'mongodb://user:password#ip.x.x.x:27017/database01.data.coll')\
... .config('spark.mongodb.output.uri', 'mongodb://user:password#ip.x.x.x:27017/database01.data.coll')\
... .config('spark.jars.packages', 'org.mongodb.spark:mongo-spark-connector_2.11:2.3.1')\
... .getOrCreate()
>>> df01 = spark.read\
... .format("com.mongodb.spark.sql.DefaultSource")\
... .option("database","database01")\
... .option("collection", "collection01")\
... .load()
Traceback (most recent call last):
File "<stdin>", line 4, in <module>
File "C:\Spark\spark-3.0.0-bin-hadoop2.7\python\pyspark\sql\readwriter.py", line 184, in load
return self._df(self._jreader.load())
File "C:\Spark\spark-3.0.0-bin-hadoop2.7\python\lib\py4j-0.10.9-src.zip\py4j\java_gateway.py", line 1305, in __call__
File "C:\Spark\spark-3.0.0-bin-hadoop2.7\python\pyspark\sql\utils.py", line 137, in deco
raise_from(converted)
File "<string>", line 3, in raise_from
pyspark.sql.utils.IllegalArgumentException: requirement failed: Missing 'uri' property from options
>>>