To optimize concurrency in a high-traffic DB2 database, implementing row-level locking can minimize lock contention, as it allows multiple transactions to access different rows simultaneously. This approach reduces the likelihood of transactions waiting for locks, thus improving overall system throughput without compromising data consistency. 

When an index is created on a table in DB2, it is based on the specified columns provided during the index creation process. This index allows DB2 to quickly locate rows based on the indexed columns, enhancing query performance. The table structure remains unchanged, but an additional data structure (the index) is created to facilitate faster data retrieval. 

When considering using JSON to store product information in a DB2 database, factors such as data complexity and structure should be carefully evaluated. JSON is well-suited for semi-structured data and flexible schema, but it may not be the optimal choice for highly structured data with complex relationships. Additionally, performance implications need to be considered, as querying and manipulating JSON data may have different performance characteristics compared to traditional relational data. Compatibility with existing systems is another crucial factor to assess, ensuring seamless integration with other applications and databases. Furthermore, security considerations, such as data encryption and access controls, should not be overlooked to safeguard sensitive information stored in JSON documents. 

A functional dependency is a relationship between two attributes where the value of one attribute uniquely determines the value of another attribute. In other words, if X and Y are attributes of a relation, then Y is functionally dependent on X if each value of X is associated with precisely one value of Y. This concept is fundamental to normalization as it helps in identifying and eliminating redundancy in a database schema. 

When creating an index in DB2, it is crucial to consider the data type of the indexed columns. The data type affects the index's efficiency and storage requirements. For instance, using a wide data type for indexing may result in larger index sizes, impacting storage and query performance. Additionally, choosing the appropriate data type ensures compatibility with the types of queries that will be executed against the indexed columns, optimizing query execution and enhancing overall database performance. 

The Health Monitor integrates with DB2 features like Performance Management, providing insights into various performance metrics, aiding in database optimization. By connecting with Performance Management, it enables comprehensive monitoring and management of database performance. This integration is crucial for administrators to ensure the smooth functioning of the database environment. 

The PRIMARY KEY constraint in DB2 enforces uniqueness and not null for one or more columns in a table. It uniquely identifies each record in the table, preventing duplicate or null values in the specified column(s). This constraint is crucial for ensuring entity integrity and efficient data retrieval operations. 

The CHECK constraint in DB2 ensures that data inserted or updated in a column meets specific conditions or criteria defined by the user. It allows users to define custom rules to restrict the values that can be entered into a column, ensuring data integrity and validity. Therefore, the correct option is to ensure that data meets specified conditions. 

The EXPORT utility in DB2 is used to extract data from database tables and write it to an external file. This functionality is particularly useful for generating backups, creating reports, or transferring data to other systems. The exported data can be stored in various formats, including delimited text files, binary files, or even XML, depending on the requirements of the application. 

Advanced techniques for performance tuning in DB2 include query optimization, which involves analyzing and fine-tuning SQL queries to improve execution efficiency. It often involves creating or modifying indexes, adjusting buffer pool sizes, and using advanced SQL features like common table expressions (CTEs) or window functions to optimize query execution plans.