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

Here are some possible scenarios based on the job categories:

1. Skilled (category: 'skilled'): This category generally consists of jobs that require a certain set of skills or expertise. It is likely that individuals with skilled jobs have a higher chance of receiving credit.

2. Unskilled Resident (category: 'unskilled resident'): This category represents jobs that do not require specific skills and are usually held by residents. The credit approval for this category may vary depending on other factors such as income or credit history.

3. High Qualif/Self Emp/Mgmt (category: 'high qualif/self emp/mgmt'): This category includes jobs that require high qualifications, self-employment, or management positions. Individuals with this type of job might have a higher chance of receiving credit due to their qualifications and income potential.

4. Unemp/Unskilled Non-res (category: 'unemp/unskilled non res'): This category includes individuals who are unemployed or have unskilled jobs and are not residents. It is likely that individuals in this category may have a lower chance of receiving credit due to their employment status or lack of skills.

Based on these possible scenarios, we can create a dictionary to summarize the relationship between the "job" feature and the task of receiving credit. The dictionary will include the possible values of the "job" feature for each target class (yes or no).

Here's the dictionary:

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

Please note that this is a generalized analysis and the actual relationship may vary depending on the specific dataset and target variable. The dictionary provided represents the possible values for each target class based on the given feature description.