Based on prior knowledge, it is possible that the feature "job" could be related to the target variable "Does this person receive a credit?". Different types of jobs may have different income levels or levels of job stability, which could in turn influence a person's ability to receive credit.

To create the dictionary, we'll first need to analyze how the feature "job" is related to the target variable. We can do this by examining the distribution of job categories for both the "yes" and "no" classes of the target variable.

Assuming we have a dataset that includes both the "job" feature and the target variable "Does this person receive a credit?", we can analyze the relationship as follows:

1. Calculate the number of occurrences for each category of the "job" feature for "yes" and "no" classes separately.
2. Create two separate lists, one for the "yes" class and one for the "no" class, containing the categories of "job" with non-zero occurrences in each class.
3. Format the lists as strings within a dictionary following the specified markdown code snippet format.

Based on the analysis, here is an example of how the dictionary could be generated:

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

Note that this is just a hypothetical example, and the actual values for the "yes" and "no" categories of the "job" feature may vary depending on the specific dataset and analysis.