Based on prior knowledge, we can analyze the relationship between the "job" feature and the task of determining whether a person receives credit or not. 

One possible relationship that can be observed is that individuals with a "high qualif/self emp/mgmt" job are more likely to receive credit compared to others. They typically have stable income and are considered as low-risk borrowers.

On the other hand, individuals categorized as "unemp/unskilled non res" are less likely to receive credit. These individuals might have unstable or no income, which can make them a high-risk category for lenders.

Regarding the other two job categories, "skilled" and "unskilled resident," it is somewhat unclear how they would affect the person's creditworthiness based solely on the job category. Therefore, we cannot make a definitive conclusion without additional information.

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

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

Please note that the values in the dictionary correspond to the job categories that are more likely to belong to each target class.