Reviewing the database change deployment logs should be the first step in troubleshooting as it provides insights into any errors or issues that occurred during the deployment process, helping identify the root cause more efficiently.

Stress testing assesses the system's ability to withstand extreme conditions such as high traffic volumes or heavy loads. By pushing the system to its limits, testers can identify weaknesses or points of failure that need to be addressed to ensure optimal performance under stress.

While the PRINT statement can display error details, it does not provide structured error handling. RAISERROR can raise custom error messages but does not inherently capture error details like error message and line number. Setting up a SQL Server Profiler trace is more for monitoring and auditing purposes rather than error handling. The most appropriate approach is to implement TRY...CATCH blocks, which allow for structured error handling and capturing specific error details.

One of the key challenges in data migration testing is ensuring data integrity, which involves maintaining the accuracy, consistency, and reliability of data between the source and target systems throughout the migration process.

The TRY...CATCH statement is commonly used in SQL to catch and handle errors. It allows developers to encapsulate the code that might generate errors within a TRY block and specify the actions to be taken in case an error occurs in the corresponding CATCH block. This helps in better error handling and ensures that the application remains stable even in the presence of errors.

The DELETE statement in SQL is used to remove one or more rows from a table in a database. It is a Data Manipulation Language (DML) statement that allows users to remove unwanted data from a table, based on a specified condition if needed.

Extraction is the correct option. In the ETL (Extract, Transform, Load) process, the first step is to extract data from the source systems. This involves identifying the relevant data sources and pulling the required data into the ETL pipeline for further processing.

Common challenges in data validation during ETL processes include ensuring data quality, managing large data volumes efficiently, dealing with data latency issues, and maintaining data lineage.

Database testing verifies the accuracy, consistency, and integrity of data within applications, helping to identify and mitigate issues such as data corruption and inconsistency. It ensures that data is reliable and secure, thus reducing the risk of data-related problems impacting application functionality.

SQL is commonly used for writing test scripts in database testing because it is specifically designed for querying and manipulating data in databases. Test scripts often involve executing SQL queries to verify the functionality, performance, and integrity of database operations. SQL's syntax and capabilities make it an ideal choice for database testing tasks.