The answer B is correct because data minimization is the most important design consideration to mitigate the risk of exposing data through application programming interface (API) queries. An API is a set of rules and protocols that allows different software components or systems to communicate and exchange data. API queries are requests sent by users or applications to an API to retrieve or manipulate data. For example, a user may query an API to get information about a product, a service, or a location.
Data minimization is the principle of collecting, processing, and storing only the minimum amount of data that are necessary for a specific purpose. Data minimization can help to reduce the risk of exposing data through API queries by limiting the amount and type of data that are available or accessible through the API.
Data minimization can also help to protect the privacy and security of the data subjects and the data providers, as well as to comply with the relevant laws and regulations.
Some of the benefits of data minimization for API design are:
* Privacy: Data minimization can enhance the privacy of the data subjects by ensuring that only the data that are relevant and essential for the API purpose are collected and processed. This can prevent unnecessary or excessive collection or disclosure of personal or sensitive data, such as names, addresses, phone numbers, email addresses, etc. Data minimization can also help to comply with the privacy laws and regulations that require data protection by design and by default, such as GDPR (General Data Protection Regulation) or CCPA (California Consumer Privacy Act).
* Security: Data minimization can improve the security of the data providers by reducing the attack surface and the potential damage of a data breach. If less data are stored or transmitted through the API, there are fewer opportunities for attackers to access or compromise the data. Data minimization can also help to implement security controls such as encryption, access control, or logging more efficiently and effectively.
* Performance: Data minimization can increase the performance of the API by optimizing the use of resources and bandwidth. If less data are stored or transmitted through the API, there are less storage space and network traffic required. Data minimization can also help to improve the speed and reliability of the API responses.
Some of the techniques for data minimization in API design are:
* Define clear and specific purposes for the API and document them in the API specification or documentation.
* Identify and classify the data that are needed for each purpose and assign them appropriate labels or levels, such as public, internal, confidential, or restricted.
* Implement filters or parameters in the API queries that allow users or applications to specify or limit the data fields or attributes they want to retrieve or manipulate.
* Use pagination or throttling in the API responses that limit the number or size of data items returned per request.
* Use anonymization or pseudonymization techniques that remove or replace any identifying information from the data before sending them through the API.
Some examples of web resources that discuss data minimization in API design are:
* Data Minimization in Web APIs - World Wide Web Consortium (W3C)
* Adding Privacy by Design in Secure Application Development
* Chung-ju/Data-Minimization: A repository of related papers. - GitHub

The first thing that an IS auditor should review when finding that transaction processing times in an order processing system have significantly increased after a major release is stress testing results. Stress testing is a type of testing that evaluates how a system performs under extreme or abnormal conditions, such as high volume, load, or concurrency of transactions. Stress testing results can help explain why transaction processing times in an order processing system have significantly increased after a major release by revealing any bottlenecks, limitations, or errors in the system's capacity, performance, or functionality under stress. The other options are not as relevant as stress testing results in explaining why transaction processing times in an order processing system have significantly increased after a major release, as they do not directly measure how the system performs under extreme or abnormal conditions. Capacity management plan is a document that defines and implements the processes and activities for ensuring that the system has adequate resources and capabilities to meet current and future demands. Training plans are documents that define and implement the processes and activities for ensuring that the system users have adequate skills and knowledge to use the system effectively and efficiently. Database conversion results are outcomes or outputs of transforming data from one format or structure to another to suit the system's requirements or specifications. References: CISA Review Manual (Digital Version), Chapter 3, Section 3.3

The scanning will not degrade system performance. This is the most important consideration when establishing vulnerability scanning on critical IT infrastructure, because any degradation of system performance could affect the availability, reliability, and functionality of the IT services that depend on the infrastructure. Scanning during non-peak hours (A) could reduce the impact of scanning on system performance, but it does not guarantee that the scanning will not cause any degradation. Scanning followed by penetration testing (B) could provide more in-depth information about the vulnerabilities and their exploitability, but it does not address the potential impact of scanning on system performance. Scanning cost- effectiveness is a relevant factor for choosing a scanning service or tool, but it is not as important as ensuring that the scanning will not compromise the system performance.
Reference: CISA Vulnerability Scanning, Description.