Update troubleshoot/elasticsearch/discovery-troubleshooting.md

Co-authored-by: Liam Thompson <[email protected]>

Author: thekofimensah

troubleshoot/elasticsearch/discovery-troubleshooting.md

@@ -80,7 +80,7 @@ When a node wins the master election, it logs a message containing `elected-as-m
 * Packet captures will reveal system-level and network-level faults, especially if you capture the network traffic simultaneously at all relevant nodes and analyse it alongside the {{es}} logs from those nodes. You should be able to observe any retransmissions, packet loss, or other delays on the connections between the nodes.
 * Long waits for particular threads to be available can be identified by taking stack dumps of the main {{es}} process (for example, using `jstack`) or a profiling trace (for example, using Java Flight Recorder) in the few seconds leading up to the relevant log message.
 
-If your master node is also acting as a data node under heavy indexing or search load, this can cause instability. In clusters under high demand, it is recommended to use (dedicated master nodes)[(/deploy-manage/distributed-architecture/clusters-nodes-shards/node-roles#dedicated-master-node)]—nodes configured with `node.master: true` and `node.data: false`-to reduce load and improve election reliability.
+If your master node is also acting as a data node under heavy indexing or search load, this can cause instability. In clusters under high demand, it is recommended to use [dedicated master nodes](/deploy-manage/distributed-architecture/clusters-nodes-shards.md/node-roles#dedicated-master-node)—nodes configured with `node.master: true` and `node.data: false`-to reduce load and improve election reliability.
 
 Additionally, ensure that the master node is not affected by resource contention from other applications. This is especially important when running in containers (e.g., Docker or Kubernetes), where CPU throttling, memory limits, or pod evictions can disrupt stability. Ensure adequate resource allocation and isolate master nodes from other workloads whenever possible.