Easy, I don't have a local central spark instance. Instead I work in python virtual environments and install the version of pyspark which matches the Databricks runtime I'm building against. Then I've got an environment controlled version of Spark. I also pull in other items as per the runtime. So, for example, with a current library I'm building the requirements look like this.

# Project dependencies
pyspark==3.0.1
pandas==1.0.1
pyarrow==1.0.1
numpy==1.18.1
pytz==2019.3
pyodbc==4.0.31

And these match the versions which are pre-installed as part of Databricks Runtime 7.3 LTS. If I need to target a new version of the runtime then I just update the project dependencies as per that runtime.