Spark has many configuration options and you will probably need to use several configurations according to what you do, which data you use, etc. For instance you may want to use spark “locally” (on the DSS server) for some jobs and on YARN on your Hadoop cluster for others, or specify the allocated memory for each worker…
As administrator, in the general settings (from the Administration menu), in the Spark section, you can add / remove / edit named “template” configurations, in which you can set Spark options by key/value pairs. See the Spark configuration documentation
At every place where you can prepare a Spark job, you will have to choose the base template configuration to use, and optionally additional / override configuration options for that specific job.
In most recipes that can load non-HDFS datasets (or sampled HDFS datasets), datasets are loaded as a single partition. They must be repartitioned so that every partition fits in a Spark worker’s RAM. There is a Repartition non-HDFS inputs settings to specify in how many partitions it should be split.