Based on prior knowledge, we can analyze the relationship between the job feature and the task of whether the client subscribes to a term deposit.

To analyze this relationship, we can look at the distribution of job types for both clients who subscribed to a term deposit (yes) and those who did not (no). Based on the distribution, we can identify the possible values of the job feature for each target class.

Here is the dictionary with the required format:

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

Note that for the target class "no", all possible job values are included as they cover the entire range of job types. However, for the target class "yes", one value from the original set of job types (i.e., "unknown") is excluded as it is hard to predict. Nevertheless, the list of each target class is not empty.