Based on prior knowledge, the occupation of an individual can be a good indicator of their income level. Let's analyze the relationship between the occupation feature and the task of whether the person earns more than $50,000 per year.

To do this, we need to look at the distribution of occupations for each target class (yes or no).

Let's start by examining the possible values of the occupation feature for the target class "no":

1. Machine-op-inspct
2. Farming-fishing
3. Protective-serv
4. ?
5. Other-service
6. Craft-repair
7. Adm-clerical
8. Exec-managerial
9. Tech-support
10. Sales
11. Priv-house-serv
12. Transport-moving
13. Handlers-cleaners
14. Armed-Forces

Now, let's analyze the possible values of the occupation feature for the target class "yes":

1. Prof-specialty
2. Exec-managerial
3. Tech-support
4. Sales
5. Craft-repair
6. Protective-serv
7. Other-service
8. Transport-moving

Based on this analysis, we can create the dictionary:

```json
{
	"no": ["Machine-op-inspct", "Farming-fishing", "Protective-serv", "?", "Other-service", "Craft-repair", "Adm-clerical", "Exec-managerial", "Tech-support", "Sales", "Priv-house-serv", "Transport-moving", "Handlers-cleaners", "Armed-Forces"],
	"yes": ["Prof-specialty", "Exec-managerial", "Tech-support", "Sales", "Craft-repair", "Protective-serv", "Other-service", "Transport-moving"]
}
```

Please note that the occupation values that are hard to predict (e.g., "?", "Handlers-cleaners", "Armed-Forces") are included in the dictionary. However, it is advisable to further investigate and determine if these values should be considered as separate categories or if they should be grouped together. Additionally, it's possible that there might be other values not present in the dataset, so it's crucial to have a comprehensive understanding of the data before drawing concrete conclusions.