How does MongoDB optimize query performance?

MongoDB optimizes query performance primarily through query optimization and indexing. Query optimization involves analyzing the queries that are run against the database and determining the most efficient way to execute them. This process ensures that the database engine can respond to requests with minimal latency.

Indexing plays a crucial role in this optimization process, as it allows MongoDB to quickly locate the documents that match a query without scanning the entire collection. Indexes provide a data structure that improves search speed, making data retrieval faster and more efficient. When properly indexed, frequent queries can be executed in a fraction of the time it would take to conduct a full database scan.

The other options do not accurately capture the mechanisms MongoDB uses for optimizing query performance. For instance, relying exclusively on disk storage would actually hinder performance, as data retrieval speed would depend solely on disk read times, which can be slow. Limiting the size of collections does not inherently improve query performance and may cause difficulties in managing large datasets. Converting data to relational format is contradictory to MongoDB's architecture as a NoSQL database, where flexibility and unstructured data storage are vital characteristics. Thus, option B encapsulates the main strategies MongoDB employs to enhance query performance effectively.