Fix typo in cluster.md (databricks#612)

Previously said "custer policy"

docs/resources/cluster.md

@@ -33,7 +33,7 @@ resource "databricks_cluster" "shared_autoscaling" {
 * `driver_node_type_id` - (Optional) The node type of the Spark driver. This field is optional; if unset, API will set the driver node type to the same value as `node_type_id` defined above.
 * `node_type_id` - (Required - optional if `instance_pool_id` is given) Any supported [databricks_node_type](../data-sources/node_type.md) id. If `instance_pool_id` is specified, this field is not needed.
 * `instance_pool_id` (Optional - required if `node_type_id` is not given) - To reduce cluster start time, you can attach a cluster to a [predefined pool of idle instances](instance_pool.md). When attached to a pool, a cluster allocates its driver and worker nodes from the pool. If the pool does not have sufficient idle resources to accommodate the cluster’s request, it expands by allocating new instances from the instance provider. When an attached cluster changes its state to `TERMINATED`, the instances it used are returned to the pool and reused by a different cluster.
-* `policy_id` - (Optional) Identifier of [Custer Policy](cluster_policy.md) to validate cluster and preset certain defaults. *The primary use for cluster policies is to allow users to create policy-scoped clusters via UI rather than sharing configuration for API-created clusters.* For example, when you specify `policy_id` of [external metastore](https://docs.databricks.com/administration-guide/clusters/policies.html#external-metastore-policy) policy, you still have to fill in relevant keys for `spark_conf`.
+* `policy_id` - (Optional) Identifier of [Cluster Policy](cluster_policy.md) to validate cluster and preset certain defaults. *The primary use for cluster policies is to allow users to create policy-scoped clusters via UI rather than sharing configuration for API-created clusters.* For example, when you specify `policy_id` of [external metastore](https://docs.databricks.com/administration-guide/clusters/policies.html#external-metastore-policy) policy, you still have to fill in relevant keys for `spark_conf`.
 * `autotermination_minutes` - (Optional) Automatically terminate the cluster after being inactive for this time in minutes. If not set, Databricks won't automatically terminate an inactive cluster. If specified, the threshold must be between 10 and 10000 minutes. You can also set this value to 0 to explicitly disable automatic termination. _We highly recommend having this setting present for Interactive/BI clusters._
 * `enable_elastic_disk` - (Optional) If you don’t want to allocate a fixed number of EBS volumes at cluster creation time, use autoscaling local storage. With autoscaling local storage, Databricks monitors the amount of free disk space available on your cluster’s Spark workers. If a worker begins to run too low on disk, Databricks automatically attaches a new EBS volume to the worker before it runs out of disk space. EBS volumes are attached up to a limit of 5 TB of total disk space per instance (including the instance’s local storage). To scale down EBS usage, make sure you have `autotermination_minutes` and `autoscale` attributes set. More documentation available at [cluster configuration page](https://docs.databricks.com/clusters/configure.html#autoscaling-local-storage-1).
 * `enable_local_disk_encryption` - (Optional) Some instance types you use to run clusters may have locally attached disks. Databricks may store shuffle data or temporary data on these locally attached disks. To ensure that all data at rest is encrypted for all storage types, including shuffle data stored temporarily on your cluster’s local disks, you can enable local disk encryption. When local disk encryption is enabled, Databricks generates an encryption key locally unique to each cluster node and encrypting all data stored on local disks. The scope of the key is local to each cluster node and is destroyed along with the cluster node itself. During its lifetime, the key resides in memory for encryption and decryption and is stored encrypted on the disk. _Your workloads may run more slowly because of the performance impact of reading and writing encrypted data to and from local volumes. This feature is not available for all Azure Databricks subscriptions. Contact your Microsoft or Databricks account representative to request access._