[SPARK-4916]Update SQL programming guide

docs/sql-programming-guide.md

@@ -835,8 +835,7 @@ Spark SQL can cache tables using an in-memory columnar format by calling `sqlCon
 Then Spark SQL will scan only required columns and will automatically tune compression to minimize
 memory usage and GC pressure. You can call `sqlContext.uncacheTable("tableName")` to remove the table from memory.
 
-Note that if you call `schemaRDD.cache()` rather than `sqlContext.cacheTable(...)`, tables will _not_ be cached using
-the in-memory columnar format, and therefore `sqlContext.cacheTable(...)` is strongly recommended for this use case.
+Note that you call schemaRDD.cache() alike sqlContext.cacheTable(...) in 1.2 release of Spark, tables will be cached using the in-memory columnar format.
 
 Configuration of in-memory caching can be done using the `setConf` method on SQLContext or by running
 `SET key=value` commands using SQL.