diff --git a/onnxruntime/test/providers/qnn/clip_op_test.cc b/onnxruntime/test/providers/qnn/clip_op_test.cc
index 918c477c75a3d..6c4c6c870bd2c 100644
--- a/onnxruntime/test/providers/qnn/clip_op_test.cc
+++ b/onnxruntime/test/providers/qnn/clip_op_test.cc
@@ -22,22 +22,15 @@ static void RunClipTest(const TestInputDef<DataType>& input_def,
                         ExpectedEPNodeAssignment expected_ep_assignment,
                         const std::string& backend_name = "cpu",
                         int opset = 13,
-                        bool enable_fp16_precision = true) {
+                        float fp32_abs_err = 1e-5f) {
   ProviderOptions provider_options;
   provider_options["backend_type"] = backend_name;
 
-  if (backend_name == "htp") {
-    if (enable_fp16_precision) {
-      provider_options["enable_htp_fp16_precision"] = "1";
-    } else {
-      provider_options["enable_htp_fp16_precision"] = "0";
-    }
-  }
-
   RunQnnModelTest(BuildOpTestCase<DataType, DataType>("Clip", {input_def}, min_max_defs, {}),
                   provider_options,
                   opset,
-                  expected_ep_assignment);
+                  expected_ep_assignment,
+                  fp32_abs_err);
 }
 
 //
@@ -77,17 +70,19 @@ TEST_F(QnnCPUBackendTests, Clip_5D_f32) {
 // HTP tests:
 //
 
-// Test Clip with float32 on HTP
-// Fails with QNN SDK 2.35.0:
-// value pair (-4.54545403, -4.54687548) at index #3 don't match, which is -0.00142145 from -4.54545
-TEST_F(QnnHTPBackendTests, DISABLED_Clip_f32) {
+// Test Clip with float32 on HTP.
+// Since QAIRT 2.35, default float precision on QNN HTP became FP16.
+// Converting FP32 -> FP16 -> FP32 may introduce minor accuracy loss.
+// For example, a value of -4.54545403 could become -4.54687548 after the conversion.
+// The expected difference is approximately 0.00142145, so the tolerance is adjusted to 5e-3f.
+TEST_F(QnnHTPBackendTests, Clip_f32) {
   RunClipTest<float>(TestInputDef<float>({1, 1, 3, 4}, false, GetFloatDataInRange(-10.0f, 10.0f, 12)),
                      {TestInputDef<float>({}, true, {-5.0f}),
                       TestInputDef<float>({}, true, {5.0f})},
                      ExpectedEPNodeAssignment::All,
                      "htp",
                      13,
-                     false);
+                     5e-3f);
 }
 
 // Test Clip with int32 on HTP
@@ -248,8 +243,7 @@ TEST_F(QnnGPUBackendTests, Clip_fp32) {
                       TestInputDef<float>({}, true, {5.0f})},
                      ExpectedEPNodeAssignment::All,
                      "gpu",
-                     13,
-                     false);
+                     13);
 }
 
 // Test Clip with int32 on GPU
diff --git a/onnxruntime/test/providers/qnn/transpose_htp_test.cc b/onnxruntime/test/providers/qnn/transpose_htp_test.cc
index 53604dc2af2f8..4ddc2edcb5d88 100644
--- a/onnxruntime/test/providers/qnn/transpose_htp_test.cc
+++ b/onnxruntime/test/providers/qnn/transpose_htp_test.cc
@@ -88,21 +88,15 @@ template <typename DataType>
 static void RunTransposeNonQDQOnHTP(const TestInputDef<DataType>& input_def,
                                     const std::vector<ONNX_NAMESPACE::AttributeProto>& attrs,
                                     ExpectedEPNodeAssignment expected_ep_assignment,
-                                    bool enable_fp16_precision = true) {
+                                    float fp32_abs_err = 1e-5f) {
   ProviderOptions provider_options;
   provider_options["backend_type"] = "htp";
 
-  if (enable_fp16_precision) {
-    provider_options["enable_htp_fp16_precision"] = "1";
-  } else {
-    provider_options["enable_htp_fp16_precision"] = "0";
-  }
-
   RunQnnModelTest(BuildTransposeTestCase<DataType>(input_def, attrs),
                   provider_options,
                   13,
                   expected_ep_assignment,
-                  1e-5f);
+                  fp32_abs_err);
 }
 
 // Check that QNN compiles DQ -> Transpose -> Q as a single unit.
@@ -120,12 +114,14 @@ TEST_F(QnnHTPBackendTests, TransposeInt32OnHTP) {
 }
 
 // Check that QNN supports Transpose with float32 data input on HTP
-// Fails with QNN SDK 2.35.0:
-// value pair (0.183528364, 0.183471695) at index #0 don't match, which is -5.66691e-05 from 0.183528
-TEST_F(QnnHTPBackendTests, DISABLED_TransposeFloatOnHTP) {
+// Since QAIRT 2.35, default float precision on QNN HTP became FP16.
+// Converting FP32 -> FP16 -> FP32 may introduce minor accuracy loss.
+// For example, a value of 7.64300251 could become 7.64453173 after the conversion.
+// The expected difference is approximately 0.00152922, so the tolerance is adjusted to 5e-3f.
+TEST_F(QnnHTPBackendTests, TransposeFloat32OnHTP) {
   RunTransposeNonQDQOnHTP<float>(TestInputDef<float>({1, 3, 224, 128}, false, 0, 10.0f),
                                  {utils::MakeAttribute("perm", std::vector<int64_t>{0, 2, 3, 1})},
-                                 ExpectedEPNodeAssignment::All, false);
+                                 ExpectedEPNodeAssignment::All, 5e-3f);
 }
 
 #endif  // defined(__aarch64__) || defined(_M_ARM64) || defined(__linux__)