diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala
index 8d17459eb4b8b..d779e602545cf 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala
@@ -382,8 +382,10 @@ class GaussianMixture @Since("2.0.0") (
     val spark = dataset.sparkSession
     import spark.implicits._
 
-    val sc = spark.sparkContext
-    val numClusters = $(k)
+    val numFeatures = MetadataUtils.getNumFeatures(dataset, $(featuresCol))
+    require(numFeatures < GaussianMixture.MAX_NUM_FEATURES, s"GaussianMixture cannot handle more " +
+      s"than ${GaussianMixture.MAX_NUM_FEATURES} features because the size of the covariance" +
+      s" matrix is quadratic in the number of features.")
 
     val handlePersistence = dataset.storageLevel == StorageLevel.NONE
 
@@ -401,12 +403,8 @@ class GaussianMixture @Since("2.0.0") (
       instances.persist(StorageLevel.MEMORY_AND_DISK)
     }
 
-    // Extract the number of features.
-    val numFeatures = MetadataUtils.getNumFeatures(dataset.schema($(featuresCol)))
-      .getOrElse(instances.first()._1.size)
-    require(numFeatures < GaussianMixture.MAX_NUM_FEATURES, s"GaussianMixture cannot handle more " +
-      s"than ${GaussianMixture.MAX_NUM_FEATURES} features because the size of the covariance" +
-      s" matrix is quadratic in the number of features.")
+    val sc = spark.sparkContext
+    val numClusters = $(k)
 
     instr.logPipelineStage(this)
     instr.logDataset(dataset)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala b/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala
index e20163977755c..6db0408e8d2b3 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala
@@ -71,24 +71,6 @@ private[spark] object MetadataUtils {
     }
   }
 
-  /**
-   * Examine a schema to identify the number of features in a vector column.
-   * Returns None if the number of features is not specified.
-   */
-  def getNumFeatures(vectorSchema: StructField): Option[Int] = {
-    if (vectorSchema.dataType == new VectorUDT) {
-      val group = AttributeGroup.fromStructField(vectorSchema)
-      val size = group.size
-      if (size >= 0) {
-        Some(size)
-      } else {
-        None
-      }
-    } else {
-      None
-    }
-  }
-
   /**
    * Examine a schema to identify categorical (Binary and Nominal) features.
    *