Shorten retention notes and clarify exception in ML scale docs

Co-authored-by: florent-leborgne <[email protected]>

Author: Copilot

explore-analyze/machine-learning/anomaly-detection/anomaly-detection-scale.md

@@ -100,7 +100,7 @@ See [Aggregating data for faster performance](ml-configuring-aggregation.md) to
 
 Set a results retention window to reduce the amount of results stored.
 
-{{anomaly-detect-cap}} results are retained indefinitely by default. Results build up over time, and your result index may be quite large. A large results index is slow to query and takes up significant space on your cluster. Consider how long you wish to retain the results and set `results_retention_days` accordingly – for example, to 30 or 60 days – to avoid unnecessarily large result indices. Deleting old results does not affect the model behavior. You can change this setting for existing jobs.
+{{anomaly-detect-cap}} results are retained indefinitely by default, except for predefined {{ml}} modules for logs which retain results for 120 days. Results build up over time, and your result index may be quite large. A large results index is slow to query and takes up significant space on your cluster. Consider how long you wish to retain the results and set `results_retention_days` accordingly – for example, to 30 or 60 days – to avoid unnecessarily large result indices. Deleting old results does not affect the model behavior. You can change this setting for existing jobs.
 
 ## 10. Optimize the renormalization window [renormalization-window]
 

solutions/observability/logs/categorize-log-entries.md

@@ -28,7 +28,7 @@ Create a {{ml}} job to categorize log messages automatically. {{ml-cap}} observe
 4. Click **Create ML job**. This creates and runs the job. It takes a few minutes for the {{ml}} robots to collect the necessary data. After the job has processed the data, you can view its results.
 
 ::::{note}
-The {{ml}} job created for log categories has a default retention period of 120 days for results. This means that categorization results older than 120 days are automatically deleted to save storage space. You can change this retention period by modifying the `results_retention_days` setting for the job.
+Log categorization {{ml}} jobs retain results for 120 days by default. Modify the `results_retention_days` setting to change this period.
 ::::
 
 

solutions/observability/logs/inspect-log-anomalies.md

@@ -33,7 +33,7 @@ Create a {{ml}} job to detect anomalous log entry rates automatically.
 4. Click **Create ML job**. This creates and runs the job. It takes a few minutes for the {{ml}} robots to collect the necessary data. After the job has processed the data, you can view its results.
 
 ::::{note}
-The {{ml}} job created for log anomalies has a default retention period of 120 days for results. This means that anomaly detection results older than 120 days are automatically deleted to save storage space. You can change this retention period by modifying the `results_retention_days` setting for the job.
+Log anomaly {{ml}} jobs retain results for 120 days by default. Modify the `results_retention_days` setting to change this period.
 ::::
 
 ## Anomalies chart [anomalies-chart]