What will be the output of this test code that verifies a create operation in a simple in-memory list database?
database = [] def create_item(item): database.append(item) return True result = create_item({'id': 1, 'name': 'Test Item'}) print(result, len(database))
Check what the create_item function returns and how the database list changes.
The create_item function appends the item and returns True. The database length becomes 1 after adding the item.
Which assertion correctly verifies that an update operation changed the name of an item in a dictionary database?
database = {1: {'id': 1, 'name': 'Old Name'}}
# Update operation
updated_item = {'id': 1, 'name': 'New Name'}
database[1] = updated_item
# Assertion to verify update:Check the updated value of the name key for item with id 1.
After update, the name should be 'New Name'. Option A correctly asserts this.
What is the bug in this delete operation test code?
database = [{'id': 1}, {'id': 2}]
# Delete item with id 1
database = [item for item in database if item['id'] != 1]
# Test if item deleted
assert 1 not in databaseLook at what the assertion is checking inside the list.
The assertion checks if integer 1 is not in the list of dictionaries, which is always true. It should check the 'id' keys instead.
Which option describes the best practice to verify a read operation in a CRUD test?
Think about what ensures the read operation is correct, not just working.
Verifying the exact data returned ensures the read operation fetches correct and complete data.
Which test framework feature is most useful to isolate each CRUD operation test to avoid side effects?
Consider how to keep tests independent and repeatable.
Setup and teardown methods ensure each test starts fresh, preventing interference between tests.