The IsLoaded() method is used when using explicit loading in Entity Framework to check if related data is already loaded. This method returns true if the related data is already loaded and false otherwise. This helps in avoiding unnecessary loading of related entities and ensures efficient use of database resources. Explicit loading is a technique used to load related entities explicitly when needed.

Eager loading would be the preferred choice in this scenario. It retrieves all necessary data upfront, minimizing the number of database round-trips, which is crucial for high-traffic applications to maintain responsiveness. Lazy loading, on the other hand, could lead to N+1 query issues, causing performance bottlenecks. Explicit loading allows for more control but is less efficient than eager loading for high-traffic scenarios. No loading would result in empty or incomplete data, not suitable for a web application.

Both lazy and eager loading can be suitable, depending on the specific requirements and use cases of the application. Lazy loading is beneficial for minimizing initial loading time and conserving resources by loading data on-demand. Eager loading, on the other hand, retrieves all necessary data upfront, suitable for scenarios where most related data is needed together, potentially reducing subsequent queries and improving responsiveness. Hence, both can be valid options depending on the context.

Implementing explicit loading with filters would be the most appropriate approach in this scenario. Explicit loading provides fine-grained control over which related data to load, allowing developers to specify filters based on certain conditions. This ensures that only the necessary subset of related data is loaded, optimizing performance and reducing unnecessary data retrieval. Eager loading would load all related data upfront, potentially retrieving more data than needed, while lazy loading might result in multiple round-trips to the database, which is less efficient. No loading with filters would not retrieve any related data, defeating the purpose of the requirement.

The Average method in Entity Framework is used to compute the average value of a numeric column within a dataset or query result set. It calculates the mean value of all numeric values present in the specified column.

The correct option is "Sum calculates the sum of values in a specified column, whereas Aggregate can perform various aggregate functions such as sum, average, minimum, maximum, and more." The Sum method in Entity Framework specifically calculates the sum of values in a specified column, while the Aggregate method is more versatile and allows for performing various aggregate functions beyond just summing values. Aggregate can handle complex scenarios where you might need to compute multiple aggregations simultaneously or apply custom logic during aggregation.

In Entity Framework, aggregate functions like SUM, AVG, COUNT, etc., handle nullable properties by ignoring null values. This means that when computing an aggregate function, Entity Framework excludes null values from the calculation. This behavior helps in obtaining accurate results when dealing with nullable properties in queries.

In Entity Framework, aggregate functions can be used in conjunction with the GroupBy clause. This allows for grouping data based on certain criteria and then performing aggregate calculations on each group. The GroupBy method is used to group the data, and then aggregate functions like SUM, AVG, etc., can be applied to each group individually. This process enables complex data analysis and reporting in Entity Framework.

The correct method to determine the number of non-null values in a column is Count(). Count() counts the number of elements in a sequence that satisfy a condition, in this case, being non-null. Sum() calculates the total sum of numeric values in a column. Average() calculates the average value of a numeric column. Max() returns the maximum value in a column.

The Sum method in Entity Framework is used to calculate the total sum of a numeric column within a dataset or query result set. It is particularly useful for calculating the total monetary value, quantity, or any other numerical aggregation.