Updates for clarity

Author: guyhay

articles/synapse-analytics/spark/apache-spark-concepts.md

@@ -49,14 +49,15 @@ Billing for the instances starts when the Azure VM(s) starts.  Billing for the S
 
 ### Example 2
 
-- You create a Spark pool call SP2; it has an autoscale enabled 10 – 20 medium nodes
+- You create a Spark pool call SP2; it has an autoscale enabled with a minimum of 10 to a maximum of 20 medium nodes
 - You submit a notebook job J1 that uses 10 nodes; a Spark instance SI1 is created to process the job
 - You now submit another job J2 that uses 10 nodes; because there's still capacity in the pool the instance autoscales to 20 nodes and processes J2.
 - Billing starts at the submission of notebook job J1.
   - The Spark pool is instantiated with 10 medium nodes, each with 8 vCores, and typically takes ~3 minutes to start. 10 x 8, 80 vCores.
   - At the submission of J2, the pool autoscales by adding another 10 medium nodes, and typically takes 4 minutes to scale.  Adding 10 x 8, 80 vCores for a total of 160 vCores.
   - Depending on the Spark pool start-up time, runtime of the first notebook job J1, the time to scale-up the pool, runtime of the second notebook, and finally the idle timeout; the pool is likely to run between 22 and 24 minutes (Spark pool instantiation time + J1 notebook job runtime all at 80 vCores) + (Spark pool autoscale up time + J2 notebook job runtime + idle timeout all at 160 vCores).
-  - 80 vCores for 4 minutes + 160 vCores for 20 minutes = 58.67 vCore hours.  
+  - 80 vCores for 4 minutes + 160 vCores for 20 minutes = 58.67 vCore hours.
+  - Note: vCore hours are billed per second, vCore pricing varies by Azure region. For more information, see [Azure Synapse Pricing](https://azure.microsoft.com/pricing/details/synapse-analytics/#pricing)
 
 ### Example 3