diff --git a/python/dask_cudf/dask_cudf/expr/_collection.py b/python/dask_cudf/dask_cudf/expr/_collection.py
index 97e1dffc65b..c1dd16eac8d 100644
--- a/python/dask_cudf/dask_cudf/expr/_collection.py
+++ b/python/dask_cudf/dask_cudf/expr/_collection.py
@@ -202,27 +202,58 @@ class Index(DXIndex, CudfFrameBase):
 ##
 
 
-try:
-    from dask_expr._backends import create_array_collection
-
-    @get_collection_type.register_lazy("cupy")
-    def _register_cupy():
-        import cupy
-
-        @get_collection_type.register(cupy.ndarray)
-        def get_collection_type_cupy_array(_):
-            return create_array_collection
-
-    @get_collection_type.register_lazy("cupyx")
-    def _register_cupyx():
-        # Needed for cuml
-        from cupyx.scipy.sparse import spmatrix
-
-        @get_collection_type.register(spmatrix)
-        def get_collection_type_csr_matrix(_):
-            return create_array_collection
-
-except ImportError:
-    # Older version of dask-expr.
-    # Implicit conversion to array wont work.
-    pass
+def _create_array_collection_with_meta(expr):
+    # NOTE: This is the GPU compatible version of
+    # `new_dd_object` for DataFrame -> Array conversion.
+    # This can be removed if dask#11017 is resolved
+    # (See: https://github.com/dask/dask/issues/11017)
+    import numpy as np
+
+    import dask.array as da
+    from dask.blockwise import Blockwise
+    from dask.highlevelgraph import HighLevelGraph
+
+    result = expr.optimize()
+    dsk = result.__dask_graph__()
+    name = result._name
+    meta = result._meta
+    divisions = result.divisions
+    chunks = ((np.nan,) * (len(divisions) - 1),) + tuple(
+        (d,) for d in meta.shape[1:]
+    )
+    if len(chunks) > 1:
+        if isinstance(dsk, HighLevelGraph):
+            layer = dsk.layers[name]
+        else:
+            # dask-expr provides a dict only
+            layer = dsk
+        if isinstance(layer, Blockwise):
+            layer.new_axes["j"] = chunks[1][0]
+            layer.output_indices = layer.output_indices + ("j",)
+        else:
+            suffix = (0,) * (len(chunks) - 1)
+            for i in range(len(chunks[0])):
+                layer[(name, i) + suffix] = layer.pop((name, i))
+
+    return da.Array(dsk, name=name, chunks=chunks, meta=meta)
+
+
+@get_collection_type.register_lazy("cupy")
+def _register_cupy():
+    import cupy
+
+    get_collection_type.register(
+        cupy.ndarray,
+        lambda _: _create_array_collection_with_meta,
+    )
+
+
+@get_collection_type.register_lazy("cupyx")
+def _register_cupyx():
+    # Needed for cuml
+    from cupyx.scipy.sparse import spmatrix
+
+    get_collection_type.register(
+        spmatrix,
+        lambda _: _create_array_collection_with_meta,
+    )
diff --git a/python/dask_cudf/dask_cudf/tests/test_core.py b/python/dask_cudf/dask_cudf/tests/test_core.py
index 7aa0f6320f2..9f54aba3e13 100644
--- a/python/dask_cudf/dask_cudf/tests/test_core.py
+++ b/python/dask_cudf/dask_cudf/tests/test_core.py
@@ -16,6 +16,7 @@
 
 import dask_cudf
 from dask_cudf.tests.utils import (
+    QUERY_PLANNING_ON,
     require_dask_expr,
     skip_dask_expr,
     xfail_dask_expr,
@@ -950,12 +951,16 @@ def test_implicit_array_conversion_cupy():
     def func(x):
         return x.values
 
-    # Need to compute the dask collection for now.
-    # See: https://github.com/dask/dask/issues/11017
-    result = ds.map_partitions(func, meta=s.values).compute()
-    expect = func(s)
+    result = ds.map_partitions(func, meta=s.values)
 
-    dask.array.assert_eq(result, expect)
+    if QUERY_PLANNING_ON:
+        # Check Array and round-tripped DataFrame
+        dask.array.assert_eq(result, func(s))
+        dd.assert_eq(result.to_dask_dataframe(), s, check_index=False)
+    else:
+        # Legacy version still carries numpy metadata
+        # See: https://github.com/dask/dask/issues/11017
+        dask.array.assert_eq(result.compute(), func(s))
 
 
 def test_implicit_array_conversion_cupy_sparse():
@@ -967,8 +972,6 @@ def test_implicit_array_conversion_cupy_sparse():
     def func(x):
         return cupyx.scipy.sparse.csr_matrix(x.values)
 
-    # Need to compute the dask collection for now.
-    # See: https://github.com/dask/dask/issues/11017
     result = ds.map_partitions(func, meta=s.values).compute()
     expect = func(s)