Based on prior knowledge, the relationship between the "job" feature and the task of whether a person receives a credit can be analyzed as follows:

- Skilled jobs are often associated with higher incomes and stable employment, which could increase the chances of receiving credit. Therefore, it is likely that the "skilled" category would be present in the "yes" list.

- Unskilled resident jobs may be associated with lower incomes and less stability, which could decrease the chances of receiving credit. Therefore, it is possible that the "unskilled resident" category would be present in the "no" list.

- High qualif/self emp/mgmt jobs typically refer to individuals with higher qualifications and managerial positions. Such individuals may have higher incomes and better financial stability, increasing their chances of receiving credit. Thus, it is likely that the "high qualif/self emp/mgmt" category would be present in the "yes" list.

- Unemp/unskilled non res jobs suggest unemployment or unskilled employment in a non-residential area. This job category could indicate financial instability, which might decrease the chances of receiving credit. Therefore, it is possible that the "unemp/unskilled non res" category would be present in the "no" list.

Based on this analysis, the dictionary can be created as follows:

```json
{
	"yes": ["skilled", "high qualif/self emp/mgmt"],
	"no": ["unskilled resident", "unemp/unskilled non res"]
}
```