diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index ad23dae7c6b7..1b8af3f55757 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1843,6 +1843,7 @@ working with timestamps in `pandas_udf`s to get the best performance, see
 
 ## Upgrading From Spark SQL 2.3 to 2.4
 
+  - Since Spark 2.4, Spark will display table description column Last Access value as UNKNOWN when the value was Jan 01 1970.
   - Since Spark 2.4, Spark maximizes the usage of a vectorized ORC reader for ORC files by default. To do that, `spark.sql.orc.impl` and `spark.sql.orc.filterPushdown` change their default values to `native` and `true` respectively.
   - In PySpark, when Arrow optimization is enabled, previously `toPandas` just failed when Arrow optimization is unable to be used whereas `createDataFrame` from Pandas DataFrame allowed the fallback to non-optimization. Now, both `toPandas` and `createDataFrame` from Pandas DataFrame allow the fallback by default, which can be switched off by `spark.sql.execution.arrow.fallback.enabled`.
   - Since Spark 2.4, writing an empty dataframe to a directory launches at least one write task, even if physically the dataframe has no partition. This introduces a small behavior change that for self-describing file formats like Parquet and Orc, Spark creates a metadata-only file in the target directory when writing a 0-partition dataframe, so that schema inference can still work if users read that directory later. The new behavior is more reasonable and more consistent regarding writing empty dataframe.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/interface.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/interface.scala
index c6105c552604..a4ead538bb51 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/interface.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/interface.scala
@@ -114,7 +114,10 @@ case class CatalogTablePartition(
       map.put("Partition Parameters", s"{${parameters.map(p => p._1 + "=" + p._2).mkString(", ")}}")
     }
     map.put("Created Time", new Date(createTime).toString)
-    map.put("Last Access", new Date(lastAccessTime).toString)
+    val lastAccess = {
+      if (-1 == lastAccessTime) "UNKNOWN" else new Date(lastAccessTime).toString
+    }
+    map.put("Last Access", lastAccess)
     stats.foreach(s => map.put("Partition Statistics", s.simpleString))
     map
   }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveDDLSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveDDLSuite.scala
index 31fd4c5a1f99..0b3de3d4cd59 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveDDLSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveDDLSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.hive.execution
 
 import java.io.File
 import java.net.URI
+import java.util.Date
 
 import scala.language.existentials
 
@@ -2250,6 +2251,18 @@ class HiveDDLSuite
     }
   }
 
+  test("SPARK-24812: desc formatted table for last access verification") {
+    withTable("t1") {
+      sql(
+        "CREATE TABLE IF NOT EXISTS t1 (c1_int INT, c2_string STRING, c3_float FLOAT)")
+      val desc = sql("DESC FORMATTED t1").filter($"col_name".startsWith("Last Access"))
+        .select("data_type")
+      // check if the last access time doesnt have the default date of year
+      // 1970 as its a wrong access time
+      assert(!(desc.first.toString.contains("1970")))
+    }
+  }
+
   test("SPARK-24681 checks if nested column names do not include ',', ':', and ';'") {
     val expectedMsg = "Cannot create a table having a nested column whose name contains invalid " +
       "characters (',', ':', ';') in Hive metastore."