Based on prior knowledge, the job feature could be an important factor in determining whether a person receives credit or not. 

To analyze the relationship between the job feature and the task of receiving credit, we can examine the distribution of job categories for each target class (yes or no). Let's go through the process of creating the dictionary:

- For the target class "yes" (receives credit), the possible values of the job feature could be skilled, unskilled resident, high qualif/self emp/mgmt, or unemp/unskilled non res. We can check the frequency of each job category for the "yes" class and include them in the dictionary.

- For the target class "no" (does not receive credit), we can repeat the same process as above.

Based on the analysis, we can create a dictionary with the following format:

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

Please note that the values in both lists are the possible job categories for each target class. If there are any additional job categories observed for either target class, they should be added to the respective list.