diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Concatenate.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Concatenate.cs
index e25d1e0d94..9b350227be 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Concatenate.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Concatenate.cs
@@ -1,10 +1,11 @@
 using System;
+using System.Collections.Generic;
 using Microsoft.ML;
 using Microsoft.ML.Data;
 
 namespace Samples.Dynamic
 {
-    public static class ConcatTransform
+    public static class Concatenate
     {
         public static void Example()
         {
@@ -12,54 +13,61 @@ public static void Example()
             // as well as the source of randomness.
             var mlContext = new MLContext();
 
-            // Get a small dataset as an IEnumerable and them read it as ML.NET's data type.
-            var data = Microsoft.ML.SamplesUtils.DatasetUtils.GetInfertData();
-            var trainData = mlContext.Data.LoadFromEnumerable(data);
+            // Create a small dataset as an IEnumerable.
+            var samples = new List<InputData>()
+            {
+                new InputData(){ Feature1 = 0.1f, Feature2 = new[]{ 1.1f, 2.1f, 3.1f}, Feature3 = 1 },
+                new InputData(){ Feature1 = 0.2f, Feature2 = new[]{ 1.2f, 2.2f, 3.2f}, Feature3 = 2 },
+                new InputData(){ Feature1 = 0.3f, Feature2 = new[]{ 1.3f, 2.3f, 3.3f}, Feature3 = 3 },
+                new InputData(){ Feature1 = 0.4f, Feature2 = new[]{ 1.4f, 2.4f, 3.4f}, Feature3 = 4 },
+                new InputData(){ Feature1 = 0.5f, Feature2 = new[]{ 1.5f, 2.5f, 3.5f}, Feature3 = 5 },
+                new InputData(){ Feature1 = 0.6f, Feature2 = new[]{ 1.6f, 2.6f, 3.6f}, Feature3 = 6 },
+            };
 
-            // Preview of the data.
-            //
-            // Age    Case  Education  induced     parity  pooled.stratum  row_num  ...
-            // 26.0   1.0   0-5yrs      1.0         6.0       3.0      1.0  ...
-            // 42.0   1.0   0-5yrs      1.0         1.0       1.0      2.0  ...
-            // 39.0   1.0   0-5yrs      2.0         6.0       4.0      3.0  ...
-            // 34.0   1.0   0-5yrs      2.0         4.0       2.0      4.0  ...
-            // 35.0   1.0   6-11yrs     1.0         3.0       32.0     5.0  ...
+            // Convert training data to IDataView.
+            var dataview = mlContext.Data.LoadFromEnumerable(samples);
 
-            // A pipeline for concatenating the Age, Parity and Induced columns together into a vector that will be the Features column.
-            // Concatenation is necessary because learners take **feature vectors** as inputs.
-            //   e.g. var regressionTrainer = mlContext.Regression.Trainers.FastTree(labelColumn: "Label", featureColumn: "Features");
-            string outputColumnName = "Features";
-            var pipeline = mlContext.Transforms.Concatenate(outputColumnName, new[] { "Age", "Parity", "Induced" });
+            // A pipeline for concatenating the "Feature1", "Feature2" and "Feature3" columns together into a vector that will be the Features column.
+            // Concatenation is necessary because trainers take feature vectors as inputs.
+            //
+            // Please note that the "Feature3" column is converted from int32 to float using the ConvertType.
+            // The Concatenate requires all columns to be of same type.
+            var pipeline = mlContext.Transforms.Conversion.ConvertType("Feature3", outputKind: DataKind.Single)
+                .Append(mlContext.Transforms.Concatenate("Features", new[] { "Feature1", "Feature2", "Feature3" }));
 
             // The transformed data.
-            var transformedData = pipeline.Fit(trainData).Transform(trainData);
+            var transformedData = pipeline.Fit(dataview).Transform(dataview);
 
             // Now let's take a look at what this concatenation did.
-            // We can extract the newly created column as an IEnumerable of SampleInfertDataWithFeatures, the class we define above.
-            var featuresColumn = mlContext.Data.CreateEnumerable<SampleInfertDataWithFeatures>(transformedData, reuseRowObject: false);
+            // We can extract the newly created column as an IEnumerable of TransformedData.
+            var featuresColumn = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: false);
 
             // And we can write out a few rows
-            Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
+            Console.WriteLine($"Features column obtained post-transformation.");
             foreach (var featureRow in featuresColumn)
-            {
-                foreach (var value in featureRow.Features.GetValues())
-                    Console.Write($"{value} ");
-                Console.WriteLine("");
-            }
+                Console.WriteLine(string.Join(" ", featureRow.Features));
 
             // Expected output:
-            // Features column obtained post-transformation.
-            //
-            // 26 6 1
-            // 42 1 1
-            // 39 6 2
-            // 34 4 2
-            // 35 3 1
+            //  Features column obtained post-transformation.
+            //  0.1 1.1 2.1 3.1 1
+            //  0.2 1.2 2.2 3.2 2
+            //  0.3 1.3 2.3 3.3 3
+            //  0.4 1.4 2.4 3.4 4
+            //  0.5 1.5 2.5 3.5 5
+            //  0.6 1.6 2.6 3.6 6
+        }
+
+        private class InputData
+        {
+            public float Feature1;
+            [VectorType(3)]
+            public float[] Feature2;
+            public int Feature3;
         }
 
-        private class SampleInfertDataWithFeatures
+        private sealed class TransformedData
         {
-            public VBuffer<float> Features { get; set; }
+            public float[] Features { get; set; }
         }
     }
 }