diff --git a/backends/cadence/aot/tests/test_quantizer_ops.py b/backends/cadence/aot/tests/test_quantizer_ops.py
index 99953346b05..871857196a6 100644
--- a/backends/cadence/aot/tests/test_quantizer_ops.py
+++ b/backends/cadence/aot/tests/test_quantizer_ops.py
@@ -56,7 +56,6 @@
     CadenceW8A32MixedQuantizer,  # TODO: T247438158 Add test coverage
     CadenceRmsNormNopQuantizer,  # No-op quantizer, doesn't annotate anything, preserves rms_norm from decomposition
     CadenceWakeWordQuantizer,  # TODO: T247438162 Add test coverage
-    CadenceWith16BitConvActivationsQuantizer,  # TODO: T247438221 Add test coverage
     CadenceWithLayerNormQuantizer,  # TODO: T247438410 Add test coverage
     CadenceWithSoftmaxQuantizer,  # TODO: T247438418 Add test coverage
 }
@@ -93,6 +92,24 @@
         # For linear: [input_activation, weight]
         [qconfig_A16.input_activation, qconfig_A16.weight],
     ),
+    (
+        "conv1d_A16",
+        lambda self: self._build_conv1d_graph(),
+        CadenceWith16BitConvActivationsQuantizer(),
+        torch.ops.aten.conv1d.default,
+        qconfig_A16.output_activation,
+        # For conv1d: [input_activation, weight]
+        [qconfig_A16.input_activation, qconfig_A16.weight],
+    ),
+    (
+        "conv2d_A16",
+        lambda self: self._build_conv2d_graph(),
+        CadenceWith16BitConvActivationsQuantizer(),
+        torch.ops.aten.conv2d.default,
+        qconfig_A16.output_activation,
+        # For conv2d: [input_activation, weight]
+        [qconfig_A16.input_activation, qconfig_A16.weight],
+    ),
 ]
 
 # Derive the set of tested quantizer classes from the test cases.
@@ -149,6 +166,54 @@ def _build_linear_graph(self) -> tuple[torch.fx.GraphModule, torch.fx.Node]:
         self.assertEqual(len(linear_nodes), 1, "Should find exactly one linear node")
         return gm, linear_nodes[0]
 
+    def _build_conv1d_graph(self) -> tuple[torch.fx.GraphModule, torch.fx.Node]:
+        """Build a simple graph with a conv1d operation (no bias)."""
+        builder = GraphBuilder()
+        # Input shape: (batch, in_channels, length)
+        x = builder.placeholder("x", torch.randn(1, 3, 10))
+        # Weight shape: (out_channels, in_channels, kernel_size)
+        weight = builder.placeholder("weight", torch.randn(6, 3, 3))
+        conv1d = builder.call_operator(
+            op=torch.ops.aten.conv1d.default,
+            args=(x, weight),
+            meta=NodeMetadata(
+                {"source_fn_stack": [("conv1d", torch.ops.aten.conv1d.default)]}
+            ),
+        )
+        builder.output([conv1d])
+        gm = builder.get_graph_module()
+
+        conv1d_nodes = gm.graph.find_nodes(
+            op="call_function",
+            target=torch.ops.aten.conv1d.default,
+        )
+        self.assertEqual(len(conv1d_nodes), 1, "Should find exactly one conv1d node")
+        return gm, conv1d_nodes[0]
+
+    def _build_conv2d_graph(self) -> tuple[torch.fx.GraphModule, torch.fx.Node]:
+        """Build a simple graph with a conv2d operation (no bias)."""
+        builder = GraphBuilder()
+        # Input shape: (batch, in_channels, height, width)
+        x = builder.placeholder("x", torch.randn(1, 3, 8, 8))
+        # Weight shape: (out_channels, in_channels, kernel_h, kernel_w)
+        weight = builder.placeholder("weight", torch.randn(6, 3, 3, 3))
+        conv2d = builder.call_operator(
+            op=torch.ops.aten.conv2d.default,
+            args=(x, weight),
+            meta=NodeMetadata(
+                {"source_fn_stack": [("conv2d", torch.ops.aten.conv2d.default)]}
+            ),
+        )
+        builder.output([conv2d])
+        gm = builder.get_graph_module()
+
+        conv2d_nodes = gm.graph.find_nodes(
+            op="call_function",
+            target=torch.ops.aten.conv2d.default,
+        )
+        self.assertEqual(len(conv2d_nodes), 1, "Should find exactly one conv2d node")
+        return gm, conv2d_nodes[0]
+
     @parameterized.expand(QUANTIZER_ANNOTATION_TEST_CASES)
     def test_quantizer_annotation(
         self,