Support should be given the greatest consideration when implementing the use of an open-source product, as open-source software may not have the same level of technical support, maintenance, and updates as proprietary software1. Open-source software users may have to rely on the community of developers and users, online forums, or third-party vendors for support, which may not be timely, reliable, or consistent2. Therefore, before implementing an open-source product, users should evaluate the availability and quality of support options, such as documentation, forums, mailing lists, bug trackers, chat channels, etc.3

The main risk associated with adding a new system functionality during the development phase without following a project change management process is that the new functionality may not meet requirements (option B). This is because:
A project change management process is a set of procedures that defines how changes to the project scope, schedule, budget, quality, or resources are requested, evaluated, approved, implemented, and controlled12.
A project change management process helps to ensure that the changes are aligned with the project objectives, stakeholders' expectations, and business needs12.
Adding a new system functionality during the development phase without following a project change management process can introduce risks such as:
The added functionality has not been documented (option A), which can lead to confusion, inconsistency, errors, and rework3.
The project may fail to meet the established deadline (option C), which can result in delays, penalties, and customer dissatisfaction3.
The project may go over budget (option D), which can cause cost overruns, financial losses, and reduced profitability3.
However, the main risk is that the new functionality may not meet requirements (option B), which can have serious consequences such as:
The new functionality may not be compatible with the existing system or other components3.
The new functionality may not be tested or verified for quality, performance, security, or usability3.
The new functionality may not deliver the expected value or benefits to the users or customers3.
The new functionality may not comply with the regulatory or contractual obligations3.
The new functionality may cause dissatisfaction, complaints, or litigation from the stakeholders3.
Therefore, the main risk associated with adding a new system functionality during the development phase without following a project change management process is that the new functionality may not meet requirements (option B), as this can jeopardize the success and acceptance of the project.
References: 1: How to Make a Change Management Plan (Templates Included) - ProjectManager 2: What Is Change Management? Process & Models Explained - ProjectManager 3: 8 Steps for an Effective Change Management Process - Smartsheet

Data integrity is the property that ensures that data is accurate, complete, consistent, and reliable throughout its lifecycle. The best data integrity check is tracing data back to the point of origin, which is the source where the data was originally created or captured. This check can verify that data has not been altered or corrupted during transmission, processing, or storage. It can also identify any errors or discrepancies in data entry or conversion. Counting the transactions processed per day is a performance measure that does not directly assess data integrity. Performing a sequence check is a validity check that ensures that data follows a predefined order or pattern. It can detect missing or out-of-order data elements, but it cannot verify their accuracy or completeness. Preparing and running test data is a testing technique that simulates real data to evaluate how a system handles different scenarios. It can help identify errors or bugs in the system logic or functionality, but it cannot ensure data integrity in production environments. References: Information Systems Operations and Business Resilience, CISA Review Manual (Digital Version)

Including the data warehouse in the impact analysis for any changes in the source system is the best way to prevent data quality issues caused by changes from a source system. A data warehouse is a centralized repository of integrated data from one or more source systems. An impact analysis is a technique of assessing the potential effects and consequences of a change on the existing system or environment. Including the data warehouse in the impact analysis can help to identify and mitigate any data quality issues that may arise from changes in the source system, such as data inconsistency, incompleteness, or inaccuracy. The other options are less effective ways to prevent data quality issues, as they may involve data quality alerts, approval for changes, or access restrictions. References:
* CISA Review Manual (Digital Version), Chapter 5, Section 5.41
* CISA Review Questions, Answers & Explanations Database, Question ID 226