The DELETE command is used to remove rows from a table based on a specified condition. It is commonly used to delete specific records or all records from a table depending on the condition. 

The AVG() function in DB2 calculates the average of a set of values in a column. When NULL values are encountered, the AVG() function ignores them during the calculation, effectively treating them as missing data and excluding them from the average calculation. Therefore, NULL values do not contribute to the sum of values divided by the count of non-NULL values. 

During DB2 installation, users can choose between different installation options based on their requirements. Express installation offers a quick setup with default settings, while Custom installation allows users to customize installation parameters according to their needs. Typical installation provides a standard setup with common configurations, and Advanced installation offers extensive customization options for experienced users. 

The role of the DB2 Setup wizard during installation is to guide users through the setup process. It provides step-by-step instructions for configuring various aspects of the installation, such as specifying installation directories and choosing components to install. 

Fetching multiple rows with a single fetch operation can optimize the performance of cursors in this scenario. By reducing the number of round trips between the application and the database, fetching multiple rows at once minimizes network overhead and improves overall query performance, especially when dealing with large result sets. 

Visual Explain assists in identifying areas for query optimization. By visualizing the query execution plan, developers and database administrators can pinpoint potential performance bottlenecks and take steps to improve the efficiency of their SQL queries. This includes optimizing indexes, rewriting queries, or restructuring the database schema. 

Option 2, assessing scalability requirements, involves understanding the expected growth in data volume, user concurrency, and application complexity to determine if the current hardware can accommodate future demands. This step is essential for ensuring that the upgraded hardware can support anticipated increases in workload without experiencing performance bottlenecks. Evaluating current hardware utilization (option 1) helps identify existing resource constraints but may not provide insight into future scalability needs. Analyzing workload characteristics (option 3) helps assess the types of operations and resource demands expected from the system, guiding hardware selection based on workload patterns. Considering cost implications (option 4) is important for budgeting and ROI analysis but should be balanced with performance requirements and scalability considerations. 

In DB2, transactions involving cursors must be explicitly managed by the programmer. Cursors are not automatically associated with transactions, so they must be explicitly opened, closed, committed, or rolled back within the transaction boundaries. Failure to properly manage transactions involving cursors can lead to issues such as resource leaks, data inconsistencies, or unexpected locking behavior. Therefore, it's essential for developers to carefully handle transactions when using cursors in DB2 applications. 

The Data Manager in DB2's architecture is responsible for handling data storage and retrieval operations. It manages the physical storage of data on disk and retrieves requested data efficiently using various optimization techniques such as buffering and caching. This component ensures that data is stored securely and can be accessed quickly by applications. 

The Parameter List component of a stored procedure allows it to accept input parameters. Input parameters provide a way to pass values into the stored procedure at runtime, enabling dynamic behavior and enhancing the flexibility of the procedure. Developers can define parameters within the stored procedure declaration, specifying their data types and characteristics. 

Option 4: Implement proper indexing - When denormalizing tables, it's crucial to implement proper indexing to mitigate the risk of data redundancy. Indexes help optimize query performance by facilitating efficient data retrieval. By creating indexes on the denormalized tables, the database administrator can ensure that queries execute swiftly while minimizing the impact of redundant data. Therefore, advising the administrator to implement proper indexing is the most appropriate way to mitigate the risk of data redundancy when denormalizing tables. 

One potential drawback of denormalization is the increased risk of data inconsistency. Denormalizing tables may lead to duplicated data across the database, making it more challenging to maintain data consistency. Changes to data in one location may not be reflected in all duplicated instances, resulting in inconsistencies. Thus, careful consideration is needed while denormalizing to balance performance benefits with the risk of data inconsistency.