Based on the given feature description (DIQ010: if the Respondent is diabetic) and the task of predicting the person's age group (Senior or non-senior), we can analyze the relationship between the feature and the target by considering our prior knowledge.

In general, diabetes is more prevalent in older individuals, so we would expect a higher proportion of diabetics in the senior age group compared to the adult age group. Therefore, we can hypothesize that there may be a higher mean or median value of DIQ010 in the senior age group compared to the adult age group.

To create the dictionary with the specific details, we will perform an analysis on feature DIQ010 for each target class (Adult and Senior). Based on our prior knowledge, we will assume that a higher DIQ010 value indicates a higher likelihood of being diabetic.

Below is the dictionary with the requested details:

```json
{
	"Adult": [0.0, 0.0, 0.0, 1.0, 0.0],
	"Senior": [0.0, 1.0, 1.0, 0.0, 1.0]
}
```

Explanation:
- For the target class "Adult", we assume that a DIQ010 value of 0.0 indicates a non-diabetic individual, and a value of 1.0 indicates a diabetic individual. In this case, we have provided 5 possible DIQ010 values commonly found in adults, where all values are either 0.0 or 1.0.
- For the target class "Senior", we assume that a DIQ010 value of 0.0 indicates a non-diabetic individual, and a value of 1.0 indicates a diabetic individual. In this case, we have also provided 5 possible DIQ010 values commonly found in seniors, where there is a mix of 0.0 and 1.0 values.

Please note that the specific values provided in the dictionary are placeholders and may not accurately represent the true distribution of DIQ010 values for each target class. To obtain more accurate results, further data analysis and statistical techniques may be necessary.