Entity Framework provides several advanced techniques for querying non-entity types in scenarios with multiple data sources. Implementing custom mapping strategies allows developers to define how non-entity types are mapped to database tables or views, providing flexibility in handling complex data structures.

Using non-entity types in Entity Framework can impact change tracking in the context. By default, changes to non-entity types are ignored by the context, meaning modifications to these types won't trigger automatic change tracking. Developers need to configure explicit tracking for non-entity types if they require change tracking functionality.

The correct option is "Select." In Entity Framework, the Select method is commonly used to shape the result of a SQL query into a non-entity type. It allows you to project the desired columns and transform them as needed.

The correct option is "Select." The Select clause in LINQ is used to shape the data by specifying which properties or expressions to include in the result set. It allows you to project the data into a non-entity type according to your requirements.

Entity Framework can leverage Expression expressions in LINQ queries for complex transformations of data into non-entity types. Expressions provide a way to represent code in a tree-like data structure, allowing for manipulation and execution at runtime. They are particularly useful for building dynamic queries or performing advanced data transformations.

Entity Framework offers Lazy loading for non-entity types when dealing with large datasets. Lazy loading delays the loading of related data until it's specifically requested, which can help optimize performance by reducing the amount of data retrieved initially. This can be particularly beneficial when working with large datasets to minimize resource usage.

Entity Framework allows the use of Dynamic types to enhance the flexibility of querying non-entity data. Dynamic types enable the representation of objects whose structure is determined at runtime, allowing for more dynamic and flexible querying capabilities compared to static typing. This can be advantageous when dealing with scenarios where the schema may vary or evolve over time.

When reporting aggregated data, using non-entity types in Entity Framework can be optimized by leveraging stored procedures. Stored procedures allow encapsulating complex aggregation logic on the database server, reducing network traffic and processing overhead on the application side. This can result in better performance compared to retrieving raw data and performing aggregation in-memory using LINQ to Objects or LINQ to Entities with projections.

Using non-entity types can simplify data transformation in Entity Framework, especially when integrating with legacy systems with non-standard data structures. In such scenarios, mapping these non-standard structures directly to entities can result in increased complexity and performance overhead. By using non-entity types, developers can handle data transformation outside of the entity framework, reducing complexity and improving maintainability.

When querying a non-entity type from a database view in Entity Framework, change tracking and automatic updates are not supported. This means that any modifications made to the data retrieved from the view need to be manually tracked and updated in the database. In contrast, regular entity types in Entity Framework support automatic change tracking and updates, simplifying data manipulation tasks. Developers need to be aware of this difference when working with non-entity types to ensure data consistency and integrity.