Based on prior knowledge, we can analyze the relationship between the "job" feature and the target variable, which is whether the client subscribes to a term deposit.

To analyze this relationship, we can look at the distribution of job categories among clients who subscribed to a term deposit (yes) and those who did not (no). By examining this distribution, we can determine if certain job categories are more likely to result in a subscription.

Let's analyze the relationship between the "job" feature and the target variable:

For the "no" class (clients who did not subscribe to a term deposit), the possible values of the "job" feature are:
- blue-collar
- unknown
- technician
- management
- unemployed
- services
- self-employed
- housemaid
- retired

For the "yes" class (clients who subscribed to a term deposit), the possible values of the "job" feature are:
- technician
- management
- entrepreneur
- unknown
- retired
- admin.
- services
- self-employed
- unemployed
- housemaid
- student

Based on this analysis, we can create the required dictionary:

```json
{
	"no": ["blue-collar", "unknown", "technician", "management", "unemployed", "services", "self-employed", "housemaid", "retired"],
	"yes": ["technician", "management", "entrepreneur", "unknown", "retired", "admin.", "services", "self-employed", "unemployed", "housemaid", "student"]
}
```

Note: The values in the list are the possible job categories for each target class. The job categories that are hard to predict, such as "admin." and "student" for the "yes" class, are included based on the given task requirement.