Based on prior knowledge, the job feature can provide some insights into whether a client will subscribe to a term deposit. Certain types of jobs may have a higher likelihood of subscribing, while others may have a lower likelihood.

To analyze the relationship between the job feature and the task, we need to examine the distribution of job types for clients who subscribed to a term deposit (target class "yes") and those who did not (target class "no").

Here is the dictionary with the specific details:

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

	"yes": ["retired", "student", "unemployed", "management", "technician", "entrepreneur", "admin.", "self-employed"]
}
```

This dictionary represents the possible values of the job feature for each target class. The "no" list includes job types that are likely to be associated with clients who did not subscribe to a term deposit. The "yes" list includes job types that are likely to be associated with clients who did subscribe to a term deposit.

Please note that this analysis is based on prior knowledge and may vary depending on the specific dataset and context.