Non-clustered indexes help improve query performance by allowing efficient data retrieval based on columns frequently used in WHERE clauses. Unlike clustered indexes, non-clustered indexes don't alter the physical order of the table data. They're particularly useful for improving search performance on large datasets without affecting the way the data is stored on disk.

In Entity Framework, you can specify an index to be unique by setting the IsUnique property to true within the OnModelCreating method of the DbContext class. This configuration ensures that the corresponding index in the underlying database is unique, preventing duplicate entries in the indexed column.

Index fragmentation in Entity Framework can degrade query performance by causing unnecessary disk I/O operations. Fragmented indexes require additional disk reads to locate and retrieve data, which can significantly slow down query execution, especially for large datasets.

Composite indexes in Entity Framework can potentially degrade query performance by increasing index maintenance overhead. When multiple columns are indexed together, the index needs to be updated whenever any of the indexed columns are modified, leading to increased maintenance costs. While composite indexes can improve query performance for specific queries, they can also introduce overhead in scenarios involving frequent data modifications.

In Entity Framework, to configure an index using Data Annotations, you use the 'Index' attribute. This attribute allows you to specify which properties should be included in the index and any additional configuration options.

In Fluent API of Entity Framework, the 'HasForeignKey' method is used to specify that a property is a foreign key. This method is typically used in the configuration of relationships between entities to explicitly define the foreign key property.

In Entity Framework, the [Index] attribute is used to define non-clustered indexes on properties. By specifying this attribute, you can optimize query performance by creating indexes on columns that are frequently used in searches. This helps enhance the efficiency of data retrieval operations.

Index Fragmentation occurs when the logical order of index pages does not match the physical order of the data pages. This can happen due to data modifications like insertions, updates, or deletions, causing the index to become fragmented and leading to degraded query performance. It's essential to regularly maintain indexes to prevent fragmentation and ensure optimal query execution.

A Covering Index would be the most suitable strategy. This index type includes all the columns that are necessary for a query, avoiding the need to look up data in the original table. When multiple entities are frequently joined on a specific column, using a Covering Index can significantly improve query performance by having all required data within the index itself, reducing the need for additional table lookups and thus enhancing overall query execution speed.

Configuring a Filtered Index would be an effective approach to balance read and write performance. Unlike traditional indexes that include all rows of a table, a Filtered Index allows you to include only a subset of rows based on a condition. In a high-traffic database, this can help reduce the overhead of index maintenance during write operations while still providing performance benefits for read operations. By selectively indexing only the most queried data, you can optimize query performance without significantly impacting write operations, thus achieving a balance between read and write performance in a high-traffic environment.