Based on my prior knowledge, the job feature can potentially have a significant relationship with the task of determining whether a person receives credit or not. In general, the type of job a person has can be an important factor in their ability to receive credit. Let's analyze the relationship between the job feature and the target variable.

To create the dictionary, we need to examine the job feature with respect to the two target classes: "yes" (person receives credit) and "no" (person does not receive credit).

Based on the provided categories for the job feature:
- "skilled": It is possible that individuals with skilled jobs have a higher chance of receiving credit.
- "unskilled resident": Having an unskilled resident job may decrease the likelihood of receiving credit.
- "high qualif/self emp/mgmt": This category indicates individuals with high qualifications, self-employment, or managerial positions. Such individuals are likely to have higher income and financial stability, increasing their chances of receiving credit.
- "unemp/unskilled non res": Being unemployed or having an unskilled non-resident job could decrease the chances of receiving credit.

Now, let's create the dictionary:

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

Please note that this analysis is based on general assumptions and it is always recommended to examine the specific dataset to determine the actual relationship between the job feature and the target variable.