Based on the given feature description, we can analyze its relationship with the target variable to determine how it relates to predicting the person's age group.

Looking at the feature PAQ605, it represents if the respondent engages in moderate or vigorous-intensity sports, fitness, or recreational activities in a typical week. A value of 1 indicates that the respondent takes part in weekly moderate or vigorous-intensity physical activity, while a value of 2 indicates they do not.

To analyze the relationship between this feature and the age group, we can consider that seniors may generally have lower levels of physical activity compared to non-seniors. This is because as people age, they tend to engage in less vigorous physical activity due to various factors such as health conditions or decreased mobility. Therefore, we might expect seniors to have a higher proportion of 2 values (not engaging in physical activity) compared to non-seniors.

Now, let's create a dictionary with the possible values of feature PAQ605 for each target class.

```json
{
	"Adult": [1.0, 1.0, 1.0, 1.0, 1.0], 
	"Senior": [1.0, 1.0, 1.0, 2.0, 2.0] 
}
```

In this dictionary, we have included 5 typical values for each target class. For the "Adult" class, all values are 1.0, indicating that adults tend to engage in moderate or vigorous-intensity physical activity in a typical week. For the "Senior" class, we have a mix of 1.0 and 2.0 values, which implies that seniors may have both active and non-active individuals, but the proportion of 2.0 values (not engaging in physical activity) might be relatively higher compared to the "Adult" class.