Based on prior knowledge, it is expected that certain occupations would have a higher likelihood of earning more than $50,000 per year. Occupations such as 'Exec-managerial' and 'Prof-specialty' are generally associated with higher salaries, while occupations such as 'Other-service' and 'Priv-house-serv' are typically associated with lower incomes. 

To analyze the relationship between the occupation feature and the task of earning more than $50,000 per year, we can examine the distribution of occupations for each target class.

Here is the dictionary representing the relationship between the occupation feature and the task:

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

Note that for target class "no", the occupations listed are those that are less likely to earn more than $50,000 per year. Similarly, for target class "yes", the occupations listed are those that have a higher likelihood of earning more than $50,000 per year.

Occupations such as '?', which are hard to predict, are not included in the dictionary.