diff --git a/sdk/python/kubeflow/training/api/training_client_test.py b/sdk/python/kubeflow/training/api/training_client_test.py
index 8d0ab6310d..344ee76e2e 100644
--- a/sdk/python/kubeflow/training/api/training_client_test.py
+++ b/sdk/python/kubeflow/training/api/training_client_test.py
@@ -4,6 +4,8 @@
 
 import pytest
 from kubeflow.training import (
+    KubeflowOrgV1JobCondition,
+    KubeflowOrgV1JobStatus,
     KubeflowOrgV1PyTorchJob,
     KubeflowOrgV1PyTorchJobSpec,
     KubeflowOrgV1ReplicaSpec,
@@ -70,6 +72,13 @@ def get(self, timeout):
     return MockResponse()
 
 
+def get_job_response(*args, **kwargs):
+    if kwargs.get("namespace") == RUNTIME:
+        return generate_job_with_status(create_job(), constants.JOB_CONDITION_FAILED)
+    else:
+        return generate_job_with_status(create_job())
+
+
 def generate_container() -> V1Container:
     return V1Container(
         name="pytorch",
@@ -127,6 +136,21 @@ def create_job():
     return pytorchjob
 
 
+def generate_job_with_status(
+    job: constants.JOB_MODELS_TYPE,
+    condition_type: str = constants.JOB_CONDITION_SUCCEEDED,
+) -> constants.JOB_MODELS_TYPE:
+    job.status = KubeflowOrgV1JobStatus(
+        conditions=[
+            KubeflowOrgV1JobCondition(
+                type=condition_type,
+                status=constants.CONDITION_STATUS_TRUE,
+            )
+        ]
+    )
+    return job
+
+
 class DummyJobClass:
     def __init__(self, kind) -> None:
         self.kind = kind
@@ -279,6 +303,61 @@ def __init__(self, kind) -> None:
     ),
 ]
 
+test_data_wait_for_job_conditions = [
+    (
+        "timeout waiting for succeeded condition",
+        {
+            "name": TEST_NAME,
+            "namespace": TIMEOUT,
+            "wait_timeout": 0,
+        },
+        TimeoutError,
+    ),
+    (
+        "invalid expected condition",
+        {
+            "name": TEST_NAME,
+            "namespace": "value",
+            "expected_conditions": {"invalid"},
+        },
+        ValueError,
+    ),
+    (
+        "invalid expected condition(lowercase)",
+        {
+            "name": TEST_NAME,
+            "namespace": "value",
+            "expected_conditions": {"succeeded"},
+        },
+        ValueError,
+    ),
+    (
+        "job failed unexpectedly",
+        {
+            "name": TEST_NAME,
+            "namespace": RUNTIME,
+        },
+        RuntimeError,
+    ),
+    (
+        "valid case",
+        {
+            "name": TEST_NAME,
+            "namespace": "test-namespace",
+        },
+        generate_job_with_status(create_job()),
+    ),
+    (
+        "valid case with specified callback",
+        {
+            "name": TEST_NAME,
+            "namespace": "test-namespace",
+            "callback": lambda job: "test train function",
+        },
+        generate_job_with_status(create_job()),
+    ),
+]
+
 
 test_data_get_job_pod_names = [
     (
@@ -354,6 +433,8 @@ def training_client():
         ),
     ), patch(
         "kubernetes.config.load_kube_config", return_value=Mock()
+    ), patch.object(
+        TrainingClient, "get_job", side_effect=get_job_response
     ):
         client = TrainingClient(job_kind=constants.PYTORCHJOB_KIND)
         yield client
@@ -434,3 +515,19 @@ def test_update_job(training_client, test_name, kwargs, expected_output):
     except Exception as e:
         assert type(e) is expected_output
     print("test execution complete")
+
+
+@pytest.mark.parametrize(
+    "test_name,kwargs,expected_output", test_data_wait_for_job_conditions
+)
+def test_wait_for_job_conditions(training_client, test_name, kwargs, expected_output):
+    """
+    test wait_for_job_conditions function of training client
+    """
+    print("Executing test:", test_name)
+    try:
+        out = training_client.wait_for_job_conditions(**kwargs)
+        assert out == expected_output
+    except Exception as e:
+        assert type(e) is expected_output
+    print("test execution complete")