Improved Correctness Score

Author: sreekzz

articles/hdinsight/interactive-query/llap-schedule-based-autoscale-best-practices.md

@@ -52,7 +52,7 @@ Feature Supportability with HDInsight 4.0 Interactive Query(LLAP) Autoscale
 
 
 > [!NOTE]  
-> It's recommended to have sufficient gap between two schedules so that data cache is efficiently utilized i.e schedule scale up's when there is peak usage and scale down's when there is no usage. 
+> It's recommended to have sufficient gap between two schedules so that data cache is efficiently utilized i.e. schedule scale up's when there is peak usage and scale down's when there is no usage. 
 
 ### **Interactive Query Autoscale FAQs**
 
@@ -77,8 +77,7 @@ If there are running jobs(triggered from Spark Cluster) while scale-down is trig
 
 <b>3. Why is my query running slow even after scale-up?</b>
 
-As the Autoscale Smart probe add/remove worker nodes as part of autoscale, LLAP data cache on newly added worker nodes would require warming up after scale-up. First query on a given dataset might be slow due to cache-misses but the subsequent queries would run fast. It's recommended to run some queries on performance critical tables after scaling to warm up the data cache (Optional). 
-
+As the Autoscale Smart probe `add/remove` worker nodes as part of autoscale, LLAP data cache on newly added worker nodes would require warming up after scale-up. First query on a given dataset might be slow due to cache-misses but the subsequent queries would run fast. It's recommended to run some queries on performance critical tables after scaling to warm up the data cache (Optional). 
 
 <b>4. Does schedule based autoscale support Workload Management in LLAP?</b>