Which practice can mitigate performance degradation when handling large data sets in MongoDB?

Utilizing references and multiple collections can greatly enhance performance when dealing with large data sets in MongoDB. This approach allows for better organization and management of data by separating large entities into smaller, related collections. Doing so helps in efficiently querying specific data without loading entire documents unnecessarily, which can be resource-intensive.

By using references, you can maintain relationships between data points while avoiding the pitfalls of data duplication and maintaining flexibility in your schema. When data is organized this way, it can result in faster read and write operations, particularly in cases where not all related data needs to be retrieved at once. It also allows for easier indexing of data, which can further improve query performance.

Using a single collection that embeds too much data may lead to larger document sizes, which can slow down read and write operations as the database has to process more information and potentially encounter limits on document size. Lazy loading is effective in some contexts, but it doesn't address the root issue of how data is organized in a database structure. Minifying document sizes can sometimes help but does not compensate for poor schema design that doesn't adhere to MongoDB's best practices.