diff --git a/java/src/main/native/src/ColumnVectorJni.cpp b/java/src/main/native/src/ColumnVectorJni.cpp
index 85bbdd41b4a..edf34ff71c1 100644
--- a/java/src/main/native/src/ColumnVectorJni.cpp
+++ b/java/src/main/native/src/ColumnVectorJni.cpp
@@ -220,49 +220,14 @@ JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnVector_makeList(JNIEnv *env, j
 JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnVector_fromScalar(JNIEnv *env, jclass,
                                                                     jlong j_scalar,
                                                                     jint row_count) {
-  using ScalarType = cudf::scalar_type_t<cudf::size_type>;
   JNI_NULL_CHECK(env, j_scalar, "scalar is null", 0);
   try {
     cudf::jni::auto_set_device(env);
     auto scalar_val = reinterpret_cast<cudf::scalar const *>(j_scalar);
-    auto dtype = scalar_val->type();
-    cudf::mask_state mask_state =
-        scalar_val->is_valid() ? cudf::mask_state::UNALLOCATED : cudf::mask_state::ALL_NULL;
     std::unique_ptr<cudf::column> col;
-    if (dtype.id() == cudf::type_id::LIST) {
-      // Neither 'cudf::make_empty_column' nor 'cudf::make_column_from_scalar' supports
-      // LIST type for now (https://github.com/rapidsai/cudf/issues/8088), so the list
-      // precedes the others and takes care of the empty column itself.
-      auto s_list = reinterpret_cast<cudf::list_scalar const *>(scalar_val);
-      cudf::column_view s_val = s_list->view();
-
-      // Offsets: [0, list_size, list_size*2, ..., list_szie*row_count]
-      auto zero = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
-      auto step = cudf::make_numeric_scalar(cudf::data_type(cudf::type_id::INT32));
-      zero->set_valid(true);
-      step->set_valid(true);
-      static_cast<ScalarType *>(zero.get())->set_value(0);
-      static_cast<ScalarType *>(step.get())->set_value(s_val.size());
-      std::unique_ptr<cudf::column> offsets = cudf::sequence(row_count + 1, *zero, *step);
-      // Data:
-      // Builds the data column by leveraging `cudf::concatenate` to repeat the 's_val'
-      // 'row_count' times, because 'cudf::make_column_from_scalar' does not support list
-      // type.
-      // (Assumes the `row_count` is not big, otherwise there would be a performance issue.)
-      // Checks the `row_count` because `cudf::concatenate` does not support no rows.
-      auto data_col = row_count > 0
-          ? cudf::concatenate(std::vector<cudf::column_view>(row_count, s_val))
-          : cudf::empty_like(s_val);
-      col = cudf::make_lists_column(row_count, std::move(offsets), std::move(data_col),
-                                    cudf::state_null_count(mask_state, row_count),
-                                    cudf::create_null_mask(row_count, mask_state));
-    } else if (row_count == 0) {
-      col = cudf::make_empty_column(dtype);
-    } else if (cudf::is_fixed_width(dtype)) {
-      col = cudf::make_fixed_width_column(dtype, row_count, mask_state);
-      auto mut_view = col->mutable_view();
-      cudf::fill_in_place(mut_view, 0, row_count, *scalar_val);
-    } else if (dtype.id() == cudf::type_id::STRING) {
+    if (scalar_val->type().id() == cudf::type_id::STRING) {
+      // Tests fail when using the cudf implementation, complaining no child for string column.
+      // So here take care of the String type itself.
       // create a string column of all empty strings to fill (cheapest string column to create)
       auto offsets = cudf::make_numeric_column(cudf::data_type{cudf::type_id::INT32}, row_count + 1,
                                                cudf::mask_state::UNALLOCATED);
@@ -273,7 +238,7 @@ JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_ColumnVector_fromScalar(JNIEnv *env,
 
       col = cudf::fill(str_col->view(), 0, row_count, *scalar_val);
     } else {
-      JNI_THROW_NEW(env, "java/lang/IllegalArgumentException", "Invalid data type", 0);
+      col = cudf::make_column_from_scalar(*scalar_val, row_count);
     }
     return reinterpret_cast<jlong>(col.release());
   }