adding a Unit test case for scheduler yaml load (#35116)

* adding a UT for scheduler

* correcting a pip install when specifying version

* reformatting

* removing the comment from test file

Author: achauhan-scc

doc/dev/dev_setup.md

@@ -37,7 +37,7 @@ or execute the various commands available in the toolbox.
     ```
     azure-sdk-for-python> cd sdk/formrecognizer/azure-ai-formrecognizer
     azure-sdk-for-python/sdk/formrecognizer/azure-ai-formrecognizer> pip install -r dev_requirements.txt
-    azure-sdk-for-python/sdk/formrecognizer/azure-ai-formrecognizer> pip install tox<5
+    azure-sdk-for-python/sdk/formrecognizer/azure-ai-formrecognizer> pip install "tox<5"
     azure-sdk-for-python/sdk/formrecognizer/azure-ai-formrecognizer> pip install -e .
     ```
 

sdk/ml/azure-ai-ml/tests/schedule/unittests/test_schedule_entity.py

@@ -184,3 +184,8 @@ def test_invalid_date_string(self):
         with pytest.raises(Exception) as e:
             job_schedule._validate(raise_error=True)
         assert "Not a valid ISO8601-formatted datetime string" in str(e)
+
+    def test_schedule_create_out_of_box_monitoring_job(self):
+        test_path = "./tests/test_configs/schedule/out_of_box_monitoring.yaml"
+        schedule = load_schedule(test_path)
+        assert "genai_app_monitoring", schedule.name

sdk/ml/azure-ai-ml/tests/test_configs/schedule/out_of_box_monitoring.yaml

@@ -0,0 +1,25 @@
+#source ../configs/genai-momo/out-of-box-monitoring.yaml
+$schema:  http://azureml/sdk-2-0/Schedule.json
+name: genai_app_monitoring
+display_name: Virtual Assistant application monitoring
+description: Monitoring setup for virutal Assistant GenAI application with minimal configuration
+
+trigger:
+  type: recurrence
+  frequency: day
+  interval: 1 # runs data analysis job daily at 23:15pm
+  schedule: 
+    hours: 23
+    minutes: 15
+  
+create_monitor:
+  compute:  # clusterless spark compute to run monitoring job
+    instance_type: standard_e4s_v3
+    runtime_version: "3.2"
+  monitoring_target:
+    ml_task: questionanswering #question_answering
+    endpoint_deployment_id: azureml:va-endpoint:va-deployment
+
+# by default, production data associated with prompt flow deployment will be used
+# by default, monitoring will include token usage and generation quality metrics that don't require any data column mapping.
+# if any signal/metrics require data column mapping, user will need to use advanced setting or CLI/SDK for setup