@Alon Mansour I think the behavior seen above is similar to this documented scenario on the batch(entire) and latest(last) point anomaly detection. An example of how this behavior detects the anomalies is shown with the same set of timeseries data in the document.
Batch detection creates and applies only one model, the detection for each point is done in the context of the whole series. If the time series data trends up and down without seasonality, some points of change (dips and spikes in the data) may be missed by the model. Similarly, some points of change that are less significant than ones later in the data set may not be counted as significant enough to be incorporated into the model.
To confirm I have taken the data from your file and used the dip in data to run latest(last) point API. This detection is able to find the anomaly as seen in screen shot below. Hope this helps!!
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