To analyze the relationship between the feature DIQ010 (diabetes status) and the target task of predicting age group (Senior or non-senior), we can make some assumptions based on prior knowledge.

It is generally understood that diabetes prevalence increases with age. Thus, it is reasonable to assume that a higher proportion of seniors may have diabetes compared to non-seniors.

To create the requested dictionary with typical DIQ010 values for each target class, we would need access to data that includes both the feature and the target variable. Since the data is not provided, we cannot create a dictionary with specific values. However, we can provide a general analysis and a sample dictionary structure for the output.

Based on the assumption that diabetes prevalence increases with age, we can infer the following:

```json
{
	"Adult": [0.1, 0.2, 0.3, 0.4, 0.5],
	"Senior": [0.3, 0.4, 0.5, 0.6, 0.7]
}
```

In this sample dictionary structure, we assume that feature DIQ010 is a numeric variable that ranges between 0 to 1, with higher values indicating a higher likelihood of having diabetes. The values listed are just placeholders and do not represent actual data.

To have a more accurate analysis and create a specific dictionary, we need access to the actual data containing the feature DIQ010 and the target variable indicating age groups.