Based on prior knowledge, the feature DIQ010 represents the response of whether the person is diabetic or not. To determine the appropriate ranges of values, we need to analyze the relationship between this feature and the target variable, which is the age group (Senior or Non-Senior).

Analysis:
- As we are predicting the age group, we need to identify typical DIQ010 values for seniors and non-seniors separately.
- It is reasonable to assume that the proportion of diabetics might be higher among seniors compared to non-seniors.
- Therefore, we can expect a higher concentration of positive responses (indicating diabetes) in the DIQ010 values of seniors compared to non-seniors.

Based on this analysis, let's create a dictionary with typical DIQ010 values for both target classes.

```json
{
    "Adult": [0.0, 0.0, 0.0, 0.0, 0.0],
    "Senior": [0.0, 1.0, 0.0, 1.0, 0.0]
}
```
In this dictionary, we presented only five typical values for each target class, but the actual range of values for DIQ010 could be broader. The values [0.0, 0.0, 0.0, 0.0, 0.0] are representative of non-seniors, while the values [0.0, 1.0, 0.0, 1.0, 0.0] represent seniors.