diff --git a/heat/core/dndarray.py b/heat/core/dndarray.py
index e29acf7d27..3764cff4e8 100644
--- a/heat/core/dndarray.py
+++ b/heat/core/dndarray.py
@@ -985,11 +985,12 @@ def __process_key(
         distr_mask_fast_path = False
         # mask along split axis within tuple?
         if arr.is_distributed():
-            split_key = None
-            if isinstance(key, tuple) and len(key) > (arr.split or 0):
+            if isinstance(key, tuple) and len(key) > arr.split:
                 split_key = key[arr.split]
-            elif not isinstance(key, tuple):
+            elif isinstance(key, DNDarray):
                 split_key = key
+            else:
+                split_key = None
 
             if (
                 isinstance(split_key, DNDarray)
@@ -1010,19 +1011,19 @@ def __process_key(
                     # 1D mask on split=0
                     distr_mask_fast_path = True
 
-        # early out if mask and not tuple key
-        if distr_mask_fast_path and not isinstance(key, tuple):
-            return arr, ProcessedKey(
-                key=key.larray,
-                op_type="distr_mask",
-                output_shape=(),  # Dummy shape, bypassed safely in __setitem__
-                output_split=0 if op == "get" else arr.split,
-                split_key_is_ordered=0,
-                key_is_mask_like=True,
-                out_is_balanced=False,
-                root=None,
-                backwards_transpose_axes=tuple(range(arr.ndim)),
-            )
+                # early out if mask and not tuple key
+                if distr_mask_fast_path and not isinstance(key, tuple):
+                    return arr, ProcessedKey(
+                        key=key.larray,
+                        op_type="distr_mask",
+                        output_shape=(),  # Dummy shape, bypassed safely in __setitem__
+                        output_split=0 if op == "get" else arr.split,
+                        split_key_is_ordered=0,
+                        key_is_mask_like=True,
+                        out_is_balanced=False,
+                        root=None,
+                        backwards_transpose_axes=tuple(range(arr.ndim)),
+                    )
 
         # normalize index components
         if isinstance(key, DNDarray):
@@ -1098,19 +1099,15 @@ def __process_key(
                             tuple(key.shape), arr.shape
                         )
                     )
-                if not distr_mask_fast_path:
-                    if key_ndim == 0:
-                        # 0-D boolean mask: keep as 0-D tensor, do not extract non-zero
-                        key = key.larray if isinstance(key, DNDarray) else key
-                    else:
-                        # extract non-zero elements
-                        try:
-                            key = key.nonzero(as_tuple=True)
-                        except TypeError:
-                            key = key.nonzero()
-                else:
-                    # keep the raw boolean mask
+                if key_ndim == 0:
+                    # 0-D boolean mask: keep as 0-D tensor, do not extract non-zero
                     key = key.larray if isinstance(key, DNDarray) else key
+                else:
+                    # extract non-zero elements
+                    try:
+                        key = key.nonzero(as_tuple=True)
+                    except TypeError:
+                        key = key.nonzero()
 
                 key_is_mask_like = True
             else:
@@ -1204,7 +1201,7 @@ def __process_key(
                 elif split_key_is_ordered == 0:
                     op_type = "distributed"
                 elif key_is_mask_like:
-                    op_type = "distr_mask" if distr_mask_fast_path else "local_mask"
+                    op_type = "local_mask"
                 else:
                     op_type = "advanced"