Based on my prior knowledge, the feature "job" can potentially be related to whether a client subscribes to a term deposit or not. Different types of jobs may have different levels of income, stability, and financial security, which can influence the decision to subscribe to a term deposit.

To analyze the relationship between the "job" feature and the target variable, we can examine the distribution of job types among clients who subscribed to a term deposit and those who did not.

After analyzing the data, the resulting dictionary would be as follows:

```json
{
	"no": ["management", "technician", "blue-collar", "unknown", "retired", "services", "unemployed"],
	"yes": ["entrepreneur", "admin.", "self-employed", "housemaid", "student"]
}
```

Explanation:
- For the target class "no" (clients who did not subscribe to a term deposit), the possible job values are "management", "technician", "blue-collar", "unknown", "retired", "services", and "unemployed".
- For the target class "yes" (clients who subscribed to a term deposit), the possible job values are "entrepreneur", "admin.", "self-employed", "housemaid", and "student".

Please note that the values "unknown" and "student" may indicate less predictable job types and, therefore, their inclusion in the dictionary is based on the assumption that they still provide some relevant information regarding the likelihood of subscribing to a term deposit.