diff --git a/tests/unit/vertexai/genai/replays/test_assess_multimodal_dataset.py b/tests/unit/vertexai/genai/replays/test_assess_multimodal_dataset.py
index 673a3b5b68..0db129943a 100644
--- a/tests/unit/vertexai/genai/replays/test_assess_multimodal_dataset.py
+++ b/tests/unit/vertexai/genai/replays/test_assess_multimodal_dataset.py
@@ -66,6 +66,29 @@ def test_assess_tuning_resources(client):
     assert isinstance(response, types.TuningResourceUsageAssessmentResult)
 
 
+def test_assess_tuning_validity(client):
+    response = client.datasets.assess_tuning_validity(
+        dataset_name=DATASET,
+        dataset_usage="SFT_VALIDATION",
+        model_name="gemini-2.5-flash-001",
+        template_config=types.GeminiTemplateConfig(
+            gemini_example=types.GeminiExample(
+                contents=[
+                    {
+                        "role": "user",
+                        "parts": [{"text": "What is the capital of {name}?"}],
+                    },
+                    {
+                        "role": "model",
+                        "parts": [{"text": "{capital}"}],
+                    },
+                ],
+            ),
+        ),
+    )
+    assert isinstance(response, types.TuningValidationAssessmentResult)
+
+
 pytestmark = pytest_helper.setup(
     file=__file__,
     globals_for_file=globals(),
@@ -88,7 +111,7 @@ async def test_assess_dataset_async(client):
                         {
                             "role": "user",
                             "parts": [{"text": "What is the capital of {name}?"}],
-                        }
+                        },
                     ],
                 ),
             ),
@@ -114,3 +137,27 @@ async def test_assess_tuning_resources_async(client):
         ),
     )
     assert isinstance(response, types.TuningResourceUsageAssessmentResult)
+
+
+@pytest.mark.asyncio
+async def test_assess_tuning_validity_async(client):
+    response = await client.aio.datasets.assess_tuning_validity(
+        dataset_name=DATASET,
+        dataset_usage="SFT_VALIDATION",
+        model_name="gemini-2.5-flash-001",
+        template_config=types.GeminiTemplateConfig(
+            gemini_example=types.GeminiExample(
+                contents=[
+                    {
+                        "role": "user",
+                        "parts": [{"text": "What is the capital of {name}?"}],
+                    },
+                    {
+                        "role": "model",
+                        "parts": [{"text": "{capital}"}],
+                    },
+                ],
+            ),
+        ),
+    )
+    assert isinstance(response, types.TuningValidationAssessmentResult)
diff --git a/vertexai/_genai/datasets.py b/vertexai/_genai/datasets.py
index e92070704f..3da6db4709 100644
--- a/vertexai/_genai/datasets.py
+++ b/vertexai/_genai/datasets.py
@@ -1054,6 +1054,68 @@ def assess_tuning_resources(
             response["tuningResourceUsageAssessmentResult"],
         )
 
+    def assess_tuning_validity(
+        self,
+        *,
+        dataset_name: str,
+        model_name: str,
+        dataset_usage: str,
+        template_config: Optional[types.GeminiTemplateConfigOrDict] = None,
+        config: Optional[types.AssessDatasetConfigOrDict] = None,
+    ) -> types.TuningValidationAssessmentResult:
+        """Assess if the assembled dataset is valid in terms of tuning a given
+        model.
+
+        Args:
+          dataset_name:
+            Required. The name of the dataset to assess the tuning validity
+            for.
+          model_name:
+              Required. The name of the model to assess the tuning validity
+              for.
+          dataset_usage:
+              Required. The dataset usage to assess the tuning validity for.
+              Must be one of the following: SFT_TRAINING, SFT_VALIDATION.
+          template_config:
+              Optional. The template config used to assemble the dataset
+              before assessing the tuning validity. If not provided, the
+              template config attached to the dataset will be used. Required
+              if no template config is attached to the dataset.
+          config:
+            Optional. A configuration for assessing the tuning validity. If not
+            provided, the default configuration will be used.
+
+        Returns:
+            A dict containing the tuning validity assessment result. The dict
+            contains the following keys:
+            - errors: A list of errors that occurred during the tuning validity
+              assessment.
+        """
+        if isinstance(config, dict):
+            config = types.AssessDatasetConfig(**config)
+        elif not config:
+            config = types.AssessDatasetConfig()
+
+        operation = self._assess_multimodal_dataset(
+            name=dataset_name,
+            tuning_validation_assessment_config=types.TuningValidationAssessmentConfig(
+                model_name=model_name,
+                dataset_usage=dataset_usage,
+            ),
+            gemini_request_read_config=types.GeminiRequestReadConfig(
+                template_config=template_config,
+            ),
+            config=config,
+        )
+        response = self._wait_for_operation(
+            operation=operation,
+            timeout_seconds=config.timeout,
+        )
+        return _datasets_utils.create_from_response(
+            types.TuningValidationAssessmentResult,
+            response["tuningValidationAssessmentResult"],
+        )
+
 
 class AsyncDatasets(_api_module.BaseModule):
 
@@ -1875,3 +1937,65 @@ async def assess_tuning_resources(
             types.TuningResourceUsageAssessmentResult,
             response["tuningResourceUsageAssessmentResult"],
         )
+
+    async def assess_tuning_validity(
+        self,
+        *,
+        dataset_name: str,
+        model_name: str,
+        dataset_usage: str,
+        template_config: Optional[types.GeminiTemplateConfigOrDict] = None,
+        config: Optional[types.AssessDatasetConfigOrDict] = None,
+    ) -> types.TuningValidationAssessmentResult:
+        """Assess if the assembled dataset is valid in terms of tuning a given
+        model.
+
+        Args:
+          dataset_name:
+            Required. The name of the dataset to assess the tuning validity
+            for.
+          model_name:
+              Required. The name of the model to assess the tuning validity
+              for.
+          dataset_usage:
+              Required. The dataset usage to assess the tuning validity for.
+              Must be one of the following: SFT_TRAINING, SFT_VALIDATION.
+          template_config:
+              Optional. The template config used to assemble the dataset
+              before assessing the tuning validity. If not provided, the
+              template config attached to the dataset will be used. Required
+              if no template config is attached to the dataset.
+          config:
+            Optional. A configuration for assessing the tuning validity. If not
+            provided, the default configuration will be used.
+
+        Returns:
+            A dict containing the tuning validity assessment result. The dict
+            contains the following keys:
+            - errors: A list of errors that occurred during the tuning validity
+              assessment.
+        """
+        if isinstance(config, dict):
+            config = types.AssessDatasetConfig(**config)
+        elif not config:
+            config = types.AssessDatasetConfig()
+
+        operation = await self._assess_multimodal_dataset(
+            name=dataset_name,
+            tuning_validation_assessment_config=types.TuningValidationAssessmentConfig(
+                model_name=model_name,
+                dataset_usage=dataset_usage,
+            ),
+            gemini_request_read_config=types.GeminiRequestReadConfig(
+                template_config=template_config,
+            ),
+            config=config,
+        )
+        response = await self._wait_for_operation(
+            operation=operation,
+            timeout_seconds=config.timeout,
+        )
+        return _datasets_utils.create_from_response(
+            types.TuningValidationAssessmentResult,
+            response["tuningValidationAssessmentResult"],
+        )