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

To analyze this relationship, I would first look at the distribution of job categories among clients who subscribe to a term deposit (yes) and those who do not (no). 

Next, I would calculate the percentage of clients within each job category who subscribe to a term deposit. This will help identify any job categories that are more likely or less likely to subscribe to a term deposit.

Finally, based on these calculations, I will create a dictionary with the possible values of the "job" feature for each target class ("yes" or "no").

Here is the dictionary:

```json
{
	"no": ["management", "technician", "entrepreneur", "blue-collar", "unknown", "retired", "admin.", "services", "self-employed", "unemployed", "housemaid", "student"],
	"yes": ["management", "technician", "entrepreneur", "admin.", "services", "self-employed", "retired"]
}
```
Make sure to note that the above list does not include all job categories in the dataset, but rather only includes those job categories that have at least one client from each target class.