Correct max number of pinned clusters in the databricks_cluster docs (#2825)

This fixes #2824

Author: alexott

docs/resources/cluster.md

@@ -50,7 +50,7 @@ resource "databricks_cluster" "shared_autoscaling" {
 * `spark_env_vars` - (Optional) Map with environment variable key-value pairs to fine-tune Spark clusters. Key-value pairs of the form (X,Y) are exported (i.e., X='Y') while launching the driver and workers.
 * `custom_tags` - (Optional) Additional tags for cluster resources. Databricks will tag all cluster resources (e.g., AWS EC2 instances and EBS volumes) with these tags in addition to `default_tags`. If a custom cluster tag has the same name as a default cluster tag, the custom tag is prefixed with an `x_` when it is propagated.
 * `spark_conf` - (Optional) Map with key-value pairs to fine-tune Spark clusters, where you can provide custom [Spark configuration properties](https://spark.apache.org/docs/latest/configuration.html) in a cluster configuration.
-* `is_pinned` - (Optional) boolean value specifying if the cluster is pinned (not pinned by default). You must be a Databricks administrator to use this.  The pinned clusters' maximum number is [limited to 70](https://docs.databricks.com/clusters/clusters-manage.html#pin-a-cluster), so `apply` may fail if you have more than that.
+* `is_pinned` - (Optional) boolean value specifying if the cluster is pinned (not pinned by default). You must be a Databricks administrator to use this.  The pinned clusters' maximum number is [limited to 100](https://docs.databricks.com/clusters/clusters-manage.html#pin-a-cluster), so `apply` may fail if you have more than that (this number may change over time, so check Databricks documentation for actual number).
 
 The following example demonstrates how to create an autoscaling cluster with [Delta Cache](https://docs.databricks.com/delta/optimizations/delta-cache.html) enabled: