data quality monitor sensitivity definition

.generator/schemas/v1/openapi.yaml

@@ -9034,6 +9034,14 @@ components:
           type: array
         model_type_override:
           $ref: "#/components/schemas/MonitorFormulaAndFunctionDataQualityModelTypeOverride"
+        sensitivity:
+          description: |-
+            Sensitivity of the anomaly detection model, expressed as a multiplier on the width
+            of the predicted bounds. Higher values widen the bounds and produce fewer alerts;
+            lower values tighten them and produce more alerts. Defaults to `3.0`.
+          example: 3.0
+          format: double
+          type: number
       type: object
     MonitorFormulaAndFunctionDataQualityQueryDefinition:
       description: A formula and functions data quality query.

examples/v1/monitors/CreateMonitor_1532893480.py

@@ -0,0 +1,57 @@
+"""
+Create a Data Quality monitor with sensitivity returns "OK" response
+"""
+
+from datadog_api_client import ApiClient, Configuration
+from datadog_api_client.v1.api.monitors_api import MonitorsApi
+from datadog_api_client.v1.model.monitor import Monitor
+from datadog_api_client.v1.model.monitor_formula_and_function_data_quality_data_source import (
+    MonitorFormulaAndFunctionDataQualityDataSource,
+)
+from datadog_api_client.v1.model.monitor_formula_and_function_data_quality_monitor_options import (
+    MonitorFormulaAndFunctionDataQualityMonitorOptions,
+)
+from datadog_api_client.v1.model.monitor_formula_and_function_data_quality_query_definition import (
+    MonitorFormulaAndFunctionDataQualityQueryDefinition,
+)
+from datadog_api_client.v1.model.monitor_options import MonitorOptions
+from datadog_api_client.v1.model.monitor_thresholds import MonitorThresholds
+from datadog_api_client.v1.model.monitor_type import MonitorType
+
+body = Monitor(
+    name="Example-Monitor",
+    type=MonitorType.DATA_QUALITY_ALERT,
+    query='formula("query1").last("5m") > 100',
+    message="Data quality alert triggered",
+    tags=[
+        "test:examplemonitor",
+        "env:ci",
+    ],
+    priority=3,
+    options=MonitorOptions(
+        thresholds=MonitorThresholds(
+            critical=100.0,
+        ),
+        variables=[
+            MonitorFormulaAndFunctionDataQualityQueryDefinition(
+                name="query1",
+                data_source=MonitorFormulaAndFunctionDataQualityDataSource.DATA_QUALITY_METRICS,
+                measure="row_count",
+                filter="search for column where `database:production AND table:users`",
+                group_by=[
+                    "entity_id",
+                ],
+                monitor_options=MonitorFormulaAndFunctionDataQualityMonitorOptions(
+                    sensitivity=2.5,
+                ),
+            ),
+        ],
+    ),
+)
+
+configuration = Configuration()
+with ApiClient(configuration) as api_client:
+    api_instance = MonitorsApi(api_client)
+    response = api_instance.create_monitor(body=body)
+
+    print(response)

src/datadog_api_client/v1/model/monitor_formula_and_function_data_quality_monitor_options.py

@@ -32,6 +32,7 @@ def openapi_types(_):
             "custom_where": (str,),
             "group_by_columns": ([str],),
             "model_type_override": (MonitorFormulaAndFunctionDataQualityModelTypeOverride,),
+            "sensitivity": (float,),
         }
 
     attribute_map = {
@@ -40,6 +41,7 @@ def openapi_types(_):
         "custom_where": "custom_where",
         "group_by_columns": "group_by_columns",
         "model_type_override": "model_type_override",
+        "sensitivity": "sensitivity",
     }
 
     def __init__(
@@ -49,6 +51,7 @@ def __init__(
         custom_where: Union[str, UnsetType] = unset,
         group_by_columns: Union[List[str], UnsetType] = unset,
         model_type_override: Union[MonitorFormulaAndFunctionDataQualityModelTypeOverride, UnsetType] = unset,
+        sensitivity: Union[float, UnsetType] = unset,
         **kwargs,
     ):
         """
@@ -68,6 +71,11 @@ def __init__(
 
         :param model_type_override: Override for the model type used in anomaly detection.
         :type model_type_override: MonitorFormulaAndFunctionDataQualityModelTypeOverride, optional
+
+        :param sensitivity: Sensitivity of the anomaly detection model, expressed as a multiplier on the width
+            of the predicted bounds. Higher values widen the bounds and produce fewer alerts;
+            lower values tighten them and produce more alerts. Defaults to ``3.0``.
+        :type sensitivity: float, optional
         """
         if crontab_override is not unset:
             kwargs["crontab_override"] = crontab_override
@@ -79,4 +87,6 @@ def __init__(
             kwargs["group_by_columns"] = group_by_columns
         if model_type_override is not unset:
             kwargs["model_type_override"] = model_type_override
+        if sensitivity is not unset:
+            kwargs["sensitivity"] = sensitivity
         super().__init__(kwargs)

tests/v1/cassettes/test_scenarios/test_create_a_data_quality_monitor_with_sensitivity_returns_ok_response.frozen

@@ -0,0 +1 @@
+2026-07-31T16:36:50.561Z

tests/v1/cassettes/test_scenarios/test_create_a_data_quality_monitor_with_sensitivity_returns_ok_response.yaml

@@ -0,0 +1,47 @@
+interactions:
+- request:
+    body: '{"message":"Data quality alert triggered","name":"Test-Create_a_Data_Quality_monitor_with_sensitivity_returns_OK_response-1785515810","options":{"thresholds":{"critical":100},"variables":[{"data_source":"data_quality_metrics","filter":"search
+      for column where `database:production AND table:users`","group_by":["entity_id"],"measure":"row_count","monitor_options":{"sensitivity":2.5},"name":"query1"}]},"priority":3,"query":"formula(\"query1\").last(\"5m\")
+      > 100","tags":["test:testcreateadataqualitymonitorwithsensitivityreturnsokresponse1785515810","env:ci"],"type":"data-quality
+      alert"}'
+    headers:
+      accept:
+      - application/json
+      content-type:
+      - application/json
+    method: POST
+    uri: https://api.datadoghq.com/api/v1/monitor
+  response:
+    body:
+      string: '{"id":310055951,"org_id":321813,"type":"data-quality alert","name":"Test-Create_a_Data_Quality_monitor_with_sensitivity_returns_OK_response-1785515810","message":"Data
+        quality alert triggered","tags":["test:testcreateadataqualitymonitorwithsensitivityreturnsokresponse1785515810","env:ci"],"query":"formula(\"query1\").last(\"5m\")
+        > 100","options":{"thresholds":{"critical":100.0},"variables":[{"data_source":"data_quality_metrics","filter":"search
+        for column where `database:production AND table:users`","group_by":["entity_id"],"measure":"row_count","monitor_options":{"sensitivity":2.5},"name":"query1"}],"notify_no_data":false,"notify_audit":false,"new_host_delay":300,"include_tags":true,"silenced":{}},"multi":false,"created_at":1785515810000,"created":"2026-07-31T16:36:50.914551+00:00","modified":"2026-07-31T16:36:50.914551+00:00","deleted":null,"priority":3,"restricted_roles":null,"restriction_policy":null,"draft_status":"published","assets":[],"overall_state_modified":null,"overall_state":"No
+        Data","creator":{"name":"CI Account","handle":"9919ec9b-ebc7-49ee-8dc8-03626e717cca","email":"team-intg-tools-libs-spam@datadoghq.com","id":2320499}}
+
+        '
+    headers:
+      content-type:
+      - application/json
+    status:
+      code: 200
+      message: OK
+- request:
+    body: null
+    headers:
+      accept:
+      - application/json
+    method: DELETE
+    uri: https://api.datadoghq.com/api/v1/monitor/310055951
+  response:
+    body:
+      string: '{"deleted_monitor_id":310055951}
+
+        '
+    headers:
+      content-type:
+      - application/json
+    status:
+      code: 200
+      message: OK
+version: 1

tests/v1/features/monitors.feature

@@ -60,6 +60,16 @@ Feature: Monitors
     And the response "name" is equal to "{{ unique }}"
     And the response "type" is equal to "data-quality alert"
 
+  @team:DataDog/monitor-app
+  Scenario: Create a Data Quality monitor with sensitivity returns "OK" response
+    Given new "CreateMonitor" request
+    And body with value {"name": "{{ unique }}", "type": "data-quality alert", "query": "formula(\"query1\").last(\"5m\") > 100", "message": "Data quality alert triggered", "tags": ["test:{{ unique_lower_alnum }}", "env:ci"], "priority": 3, "options": {"thresholds": {"critical": 100}, "variables": [{"name": "query1", "data_source": "data_quality_metrics", "measure": "row_count", "filter": "search for column where `database:production AND table:users`", "group_by": ["entity_id"], "monitor_options": {"sensitivity": 2.5}}]}}
+    When the request is sent
+    Then the response status is 200 OK
+    And the response "name" is equal to "{{ unique }}"
+    And the response "type" is equal to "data-quality alert"
+    And the response "options.variables[0].monitor_options.sensitivity" is equal to 2.5
+
   @team:DataDog/monitor-app
   Scenario: Create a RUM formula and functions monitor returns "OK" response
     Given new "CreateMonitor" request