In version control, a "snapshot" is a copy of the repository at a specific point in time. Snapshots capture the state of the data model, making it possible to reference or restore previous versions as needed.

ER diagram tools primarily enable users to create visually appealing diagrams. These diagrams, known as Entity-Relationship diagrams, help in illustrating the structure of a database by representing entities, attributes, and their relationships visually.

Data partitioning involves dividing a database into smaller parts to improve scalability and performance, while clustering groups related data together on the same node to enhance data locality. These concepts are often used together in distributed database systems to optimize data distribution and access patterns, improving overall system performance.

In ERDs, cardinality represents the number of instances of an entity that can be associated with another entity. It defines how entities are related and the possible quantity of relationships, such as one-to-one, one-to-many, or many-to-many.

The primary objective of normalization in database design is to minimize data redundancy and dependency. This ensures that data is efficiently organized, reduces the risk of anomalies, and enhances data integrity in the database.

Storage optimization in relational schema design focuses on enhancing query performance by organizing and storing data efficiently. It involves strategies like indexing, partitioning, and denormalization to ensure quick and effective data retrieval.

Challenges in implementing a Generalization and Specialization hierarchy include increased complexity in querying and retrieval. While it provides a structured hierarchy, the intricacies may pose challenges in certain operations.

The correct term is Functionally Dependent. An attribute is functionally dependent on the primary key if its value is uniquely determined by the entire primary key, not just a proper subset. Understanding this concept is crucial for database design and normalization.

To optimize performance in a Key-Value Store, all of the above techniques may be employed. Caching helps reduce the need to fetch data from the underlying storage repeatedly, indexing improves lookup speed, and compression reduces the amount of data transferred, collectively enhancing overall system performance.

Type 3 SCD is advantageous as it captures both current and historical data. It maintains a separate column for historical changes, providing a snapshot of changes over time without excessive storage requirements. This is particularly useful for scenarios where a limited history is needed.