Evaluating the app’s compatibility with existing systems allows the team to avoid potential integration issues that could arise from using an app that does not align with their current Salesforce setup. Furthermore, the level of support provided by the app developers is essential, as it can significantly affect the implementation process and ongoing maintenance. Reliable support can help resolve issues quickly and ensure that the app continues to meet the evolving needs of the business. In contrast, factors such as the app’s popularity, price point, or marketing materials should not be the primary focus. While popularity can indicate general user satisfaction, it does not guarantee that the app will meet the specific needs of the company. Similarly, choosing the lowest cost option may lead to compromises in functionality or support, which could ultimately result in higher costs due to inefficiencies or the need for additional solutions. Lastly, marketing materials may not accurately represent the app’s capabilities, making it essential to rely on user reviews and detailed evaluations instead. Thus, a comprehensive assessment of integration capabilities and customization options is paramount for making an informed decision.

In contrast, a pie chart, while useful for showing proportions, can obscure the actual conversion rates and make it difficult to compare regions directly. This type of chart is better suited for displaying parts of a whole rather than comparing multiple categories. Similarly, a line graph is more appropriate for showing trends over time rather than comparing static values across different categories, which could lead to confusion in this context. Lastly, a scatter plot is designed to show relationships between two variables, which may not be relevant when the primary goal is to compare conversion rates across regions. In summary, the bar chart provides the clearest and most effective means of visualizing conversion rates across regions, enabling the marketing team to make informed decisions based on the data presented. This approach aligns with best practices in data visualization, emphasizing clarity and ease of comparison.

By prioritizing a centralized data governance framework, the architecture can facilitate real-time analytics while ensuring that the data being analyzed is accurate and reliable. This is particularly important in a B2B context where decision-making relies heavily on data insights. Furthermore, a centralized approach helps in managing data protection regulations effectively, as it allows for a cohesive strategy to address compliance issues across different jurisdictions. In contrast, a decentralized architecture could lead to inconsistencies in data management practices, making it difficult to achieve a unified view of customer interactions. Focusing solely on real-time data processing without considering data quality would likely result in poor decision-making based on inaccurate or incomplete data. Lastly, neglecting regional compliance requirements could expose the organization to legal risks and penalties, undermining the overall integrity of the solution. Therefore, a centralized data governance framework is essential for achieving the desired outcomes in this complex integration scenario.

\[ \text{Number of Batches} = \frac{\text{Total Records}}{\text{Records per Batch}} = \frac{10000}{200} = 50 \] This means that 50 batch executions are required to process all the records. Next, we need to calculate how long it will take to complete all these executions. Since the job is scheduled to run every hour and each execution takes a maximum of 5 minutes, we can calculate the total time taken in hours. Each batch execution takes 5 minutes, so the total time in minutes for 50 executions is: \[ \text{Total Time (minutes)} = \text{Number of Batches} \times \text{Time per Batch} = 50 \times 5 = 250 \text{ minutes} \] To convert this into hours, we divide by 60: \[ \text{Total Time (hours)} = \frac{250}{60} \approx 4.17 \text{ hours} \] Since the job runs every hour, it will take 5 hours to complete all executions, as the last batch will start at the 4th hour and finish in the 5th hour. Therefore, the total number of batch executions required is 50, and the total time taken to complete the job is 5 hours. This scenario illustrates the importance of understanding how batch processing works in Salesforce, particularly the limits on execution time and the scheduling of jobs. It also highlights the need for careful planning when dealing with large volumes of data to ensure that processing is efficient and within the platform’s constraints.

Using the existing partial copy sandbox (option b) may not be sufficient, as it only contains a subset of the production data and configurations, which could lead to incomplete testing scenarios. Testing directly in the production environment (option c) is highly discouraged as it poses significant risks, including potential disruptions to live operations and data integrity issues. Cloning the existing partial copy sandbox (option d) would not resolve the limitations of the partial copy itself, as it would still lack the complete data set necessary for thorough testing. By utilizing a full sandbox, the team can simulate real-world scenarios, validate the new feature’s functionality, and ensure that it integrates seamlessly with existing processes. This approach aligns with best practices in Salesforce development, emphasizing the importance of maintaining a stable production environment while allowing for extensive testing and validation in a controlled setting. This strategy not only mitigates risks but also enhances the overall quality of the deployment, ensuring that the new feature meets user expectations and business requirements.

In contrast, the Implicit Grant flow, while simpler, is less secure because it exposes access tokens directly in the URL fragment, making them vulnerable to interception. This flow is generally discouraged for new applications due to its inherent security risks. The Resource Owner Password Credentials Grant is only recommended for trusted applications, as it requires users to share their credentials directly with the application, which can lead to security vulnerabilities if not handled properly. Lastly, the Client Credentials Grant is suitable for server-to-server communication but does not involve user interaction, which may not be appropriate for scenarios requiring user-specific data access. Thus, the best practice for balancing security and usability in a multi-tenant application is to implement the Authorization Code Grant flow with PKCE, as it provides a robust mechanism for secure authorization while maintaining a user-friendly experience.

Batch Data Synchronization, while useful for periodic updates, does not provide the immediacy required for real-time data consistency. It typically involves scheduled jobs that transfer data at set intervals, which could lead to discrepancies if changes occur frequently. Point-to-Point Integration, although it can facilitate direct communication between two systems, often leads to a tangled web of connections as more systems are added, making it less scalable and harder to maintain. Remote Procedure Call (RPC) is a method that allows a program to execute code on a remote server, but it may not be the best fit for scenarios requiring high-volume data transactions and real-time updates, as it can introduce latency and complexity in error handling. In summary, Event-Driven Architecture stands out as the most effective integration pattern for this scenario due to its ability to handle real-time data flows and large transaction volumes efficiently. This approach not only enhances responsiveness but also simplifies the integration landscape, making it easier to manage and scale as the company’s needs evolve.

In the loop, the method iterates through each integer in the `salesFigures` list, accumulating the total sales in the `total` variable. The crucial part of the implementation is the return statement, where the method divides the total by the size of the list. Since `total` is an integer and `salesFigures.size()` returns an integer, the division will also yield an integer result. However, in Apex, when performing division between two integers, the result is automatically cast to a Decimal when assigned to a Decimal variable, which is the return type of the method. This ensures that the average is returned as a Decimal value, adhering to the method’s signature. The second option incorrectly suggests that an error will occur during the division, which is not the case due to Apex’s implicit type conversion. The third option incorrectly assumes that the presence of negative integers would affect the average calculation; however, the average is mathematically valid regardless of the sign of the integers. Lastly, the fourth option raises a valid concern about null values, but the method does not explicitly handle this scenario. If `salesFigures` were null, it would indeed lead to a NullPointerException, which is a potential flaw in the implementation. Thus, while the method is mostly correct, it could be improved by adding a null check for the `salesFigures` list to enhance its robustness.

Reviewing timestamps alone may highlight patterns of access but does not provide insight into whether the modifications were legitimate or if users had the necessary permissions. Implementing a blanket policy to restrict access after hours could hinder legitimate business operations and may not address the root cause of the issue. Increasing monitoring frequency may provide more data but does not directly assess the legitimacy of the modifications or the users involved. In summary, the most prudent course of action is to analyze user roles and permissions, as this will provide a comprehensive understanding of the situation and help ensure compliance with both internal policies and external regulations. This approach not only mitigates risks but also enhances the overall security posture of the organization by ensuring that access controls are appropriately enforced.

Salesforce Connect utilizes OData (Open Data Protocol) to connect to external systems, enabling seamless data access and real-time updates. This is particularly beneficial for non-technical users, as it provides a user-friendly interface to work with external data without requiring extensive coding or technical knowledge. The integration is also efficient, as it minimizes data duplication and ensures that users are always working with the most current information. On the other hand, Salesforce Data Loader is primarily used for bulk data import and export tasks, making it less suitable for real-time updates. While Salesforce APIs (like REST and SOAP APIs) can facilitate real-time integration, they often require more technical expertise to implement and manage effectively. Similarly, the Salesforce Bulk API is designed for handling large volumes of data but is not optimized for real-time synchronization, as it processes data in batches rather than continuously. Therefore, for the specific requirement of real-time inventory updates in Salesforce, Salesforce Connect stands out as the most appropriate tool, balancing ease of use, real-time capabilities, and effective data synchronization. This understanding of the tools and their applications is essential for a B2B Solution Architect, as it directly impacts the efficiency and effectiveness of business processes.

When invoking a Flow from Apex, it is crucial to ensure that the Flow is designed to handle various scenarios and that the conditions are clearly defined. The architect should consider the implications of the Flow’s logic and how it interacts with the data being passed from the Apex trigger. Additionally, it is essential to account for potential governor limits in Salesforce, as invoking Flows from Apex can consume resources that may affect overall system performance. Moreover, the architect should also be aware of the order of execution in Salesforce, which dictates how and when triggers, workflows, and Flows are executed. Understanding this order is vital to ensure that the desired outcomes are achieved without unintended consequences, such as creating duplicate records or failing to meet business requirements. In this case, the Flow’s logic is straightforward, but more complex scenarios may require additional error handling and validation to ensure data integrity and compliance with business rules.

In contrast, the other options present scenarios that are less likely to occur with effective AI integration. For instance, increased reliance on manual data entry processes contradicts the primary advantage of AI, which is to automate and streamline operations. Similarly, while there may be concerns about customer engagement with automated interactions, well-designed AI systems can enhance rather than diminish customer experiences by providing personalized recommendations and timely responses. Lastly, while there may be initial costs associated with integrating complex AI systems, the long-term benefits typically include reduced operational costs through increased efficiency and improved decision-making capabilities. Overall, the successful implementation of AI-driven predictive analytics in a CRM system is expected to lead to enhanced sales forecasting accuracy, enabling the company to make informed strategic decisions and ultimately drive revenue growth. This reflects a broader trend in the B2B sector where organizations are increasingly adopting emerging technologies to gain a competitive edge and improve operational effectiveness.

\[ \text{ROI} = \frac{\text{Net Profit}}{\text{Cost of Investment}} \times 100 \] Where: – Net Profit = Sales – Customer Acquisition Cost (CAC) For Region A: – Sales = $150,000 – CAC = $30,000 – Net Profit = $150,000 – $30,000 = $120,000 Now, we can calculate the ROI for Region A: \[ \text{ROI}_{A} = \frac{120,000}{30,000} \times 100 = 400\% \] For Region B: – Sales = $200,000 – CAC = $50,000 – Net Profit = $200,000 – $50,000 = $150,000 Now, we calculate the ROI for Region B: \[ \text{ROI}_{B} = \frac{150,000}{50,000} \times 100 = 300\% \] Now, we compare the calculated ROIs with the company’s target of 300%. Region A has an ROI of 400%, which exceeds the target, while Region B has an ROI of exactly 300%, which meets the target. Therefore, both regions meet the ROI criterion of at least 300%. In summary, the analysis shows that both regions are performing well in terms of ROI, with Region A exceeding the target and Region B meeting it. This understanding of ROI is crucial for B2B sales strategies, as it helps businesses allocate resources effectively and identify which regions are yielding the best returns on their investments.

When the internal system sends the request, it must include the necessary headers and body formatted according to the SOAP protocol, which typically involves XML rather than JSON. This means that the data must be structured correctly in XML format, adhering to the WSDL (Web Services Description Language) provided by Salesforce for the SOAP API. Furthermore, while the REST API is an alternative for interacting with Salesforce, the question specifically pertains to the SOAP API, making the use of REST irrelevant in this context. Therefore, the internal system must ensure proper authentication and correct data formatting in XML to successfully create the customer record in Salesforce. In summary, understanding the authentication process, the required data format, and the specific API being used is essential for successful integration with Salesforce’s SOAP API. This knowledge is crucial for any B2B Solution Architect working with Salesforce integrations.

1. **First Year Cost**: $1,200 (initial purchase) 2. **Second Year Cost**: $300 (subscription fee) 3. **Third Year Cost**: $300 (subscription fee) Now, we sum these costs to find the TCO: \[ \text{TCO} = \text{First Year Cost} + \text{Second Year Cost} + \text{Third Year Cost} \] Substituting the values: \[ \text{TCO} = 1200 + 300 + 300 = 1800 \] Thus, the total cost of ownership for App A after three years is $1,800. This calculation highlights the importance of understanding both upfront and recurring costs when evaluating software solutions. In a B2B context, companies must consider not only the initial investment but also how ongoing costs will impact their budgets over time. This approach ensures that decision-makers can make informed choices that align with their financial strategies and operational needs. Evaluating TCO is crucial for long-term planning and helps in comparing different solutions effectively, ensuring that the chosen app provides the best value for the investment.

By implementing MDM, the company can integrate transactional, interaction, and demographic data into a cohesive customer profile. This not only enhances the quality of insights derived from the data but also facilitates better analytics and reporting. MDM solutions often include features such as data matching, deduplication, and enrichment, which are essential for maintaining high data quality over time. On the other hand, relying solely on periodic data cleansing processes (option b) can lead to delays in addressing data quality issues, as inconsistencies may persist until the next cleansing cycle. Data virtualization (option c) allows for real-time access to data but does not inherently resolve issues of data quality or consistency, as it does not integrate the data into a unified view. Lastly, allowing departments to manage their own data independently (option d) can lead to silos and inconsistencies, undermining the goal of a unified customer profile. Thus, the most effective approach to ensure data quality and consistency across diverse data sources is to implement a master data management solution, which provides a structured framework for data governance and integration. This strategy not only enhances data quality but also supports the organization’s overall data management objectives.

In terms of performance, using bulk API calls is advantageous because it reduces the number of individual requests sent to the external service, which can significantly decrease latency and improve throughput. This is particularly important in a B2B environment where transaction volumes can be high, and efficiency is paramount. On the other hand, using basic authentication (as suggested in option b) is less secure because it involves sending user credentials with each request, making it vulnerable to interception. Making individual requests for each transaction can lead to performance bottlenecks, especially under high load. Relying on session-based authentication (option c) can also pose security risks, as sessions can be hijacked if not managed properly. Limiting API calls to only those necessary for the initial setup does not address ongoing transaction needs and can lead to operational inefficiencies. Lastly, utilizing a third-party middleware solution that does not support encryption (option d) is a significant security risk, as it exposes sensitive transaction data during transmission. Encryption is essential for protecting data integrity and confidentiality, especially in financial transactions. In summary, the best approach combines secure authentication methods with performance optimization strategies, ensuring that the integration is both secure and efficient.

Additionally, the requirement specifies that each Customer can place multiple Orders, which indicates a one-to-many relationship between Customer and Order. This means that a single Customer can be associated with multiple Orders, but each Order is linked to only one Customer. The other options present incorrect configurations. For instance, creating a one-to-many relationship between Order and Product would not allow for multiple Products to be associated with multiple Orders, which contradicts the requirement. Similarly, a many-to-one relationship would imply that multiple Orders could only relate to a single Product, which is not the case here. Lastly, a one-to-one relationship between Order and Product would limit the flexibility needed for the data model, as it would not allow for multiple Products to be associated with multiple Orders. In summary, the correct approach is to establish a many-to-many relationship between Order and Product using a junction object, while maintaining a one-to-many relationship between Customer and Order. This configuration ensures that the data model accurately reflects the business requirements and allows for efficient data management within Salesforce.

While the number of features offered by a CRM may seem appealing, it is essential to evaluate whether those features are relevant to the sales team’s needs. A feature-rich CRM that does not address the specific requirements of the sales process can lead to confusion and inefficiencies. Similarly, the popularity of a CRM among competitors does not guarantee its suitability for a particular organization. Each company has distinct operational needs, and a CRM should be evaluated based on its ability to meet those needs rather than its market presence. Cost is another critical factor; however, focusing solely on the price without considering the return on investment (ROI) can lead to poor decision-making. A less expensive CRM that does not provide the necessary functionalities or integration capabilities may ultimately cost more in lost sales opportunities and inefficiencies. Therefore, a comprehensive evaluation that includes customization capabilities, relevance of features, and potential ROI is essential for selecting a CRM that will effectively support the sales strategy and operational needs of the organization.

Using profiles and permission sets also adheres to Salesforce best practices, which emphasize the importance of maintaining data security and compliance. This approach not only enhances user experience by providing relevant features but also mitigates risks associated with unauthorized access to sensitive information. In contrast, creating a single user profile for all users would lead to excessive permissions, potentially exposing sensitive data to individuals who do not require it for their job functions. Implementing a third-party mobile management solution that overrides Salesforce’s security settings could introduce vulnerabilities and complicate compliance with Salesforce’s security protocols. Lastly, disabling mobile access entirely would hinder productivity and negate the benefits of having a mobile application, as users would be unable to access real-time data while on the go. Thus, the best practice is to utilize Salesforce’s built-in capabilities to ensure a secure, efficient, and user-friendly mobile experience tailored to the diverse needs of the company’s workforce.

When the user from the Sales profile interacts with the custom Lightning component, they will be able to view the “Annual Revenue” field since they have read access. However, because field-level security restricts them from editing this field, any attempt to update it will result in an error message indicating insufficient permissions. This behavior is consistent with Salesforce’s security model, which prioritizes the enforcement of access controls at the field level. In contrast, the Support profile has no access to the “Annual Revenue” field, meaning users in this profile would not see the field at all in any context, including the Lightning component. The Management profile, on the other hand, has both read and edit access, allowing users in that profile to view and modify the field without restrictions. This nuanced understanding of field-level security is essential for Salesforce architects and administrators, as it directly impacts how data is presented and manipulated within the platform. It is crucial to configure profiles and permissions thoughtfully to ensure that sensitive information is adequately protected while still allowing necessary access for users to perform their roles effectively.

Time series analysis, while useful for forecasting trends over time, is not ideal for predicting binary outcomes directly. It focuses on temporal data and patterns rather than the relationship between variables. Decision trees could be a viable option for classification tasks, but they may not provide the probabilistic interpretation that logistic regression offers, which is crucial for understanding the likelihood of a purchase. K-means clustering, on the other hand, is primarily a clustering technique used for segmenting data into groups based on similarity, rather than predicting outcomes. In predictive analytics, the choice of model significantly impacts the accuracy and interpretability of the results. Logistic regression not only provides a clear probabilistic framework but also allows for the inclusion of multiple predictors, making it a robust choice for this retail scenario. By applying this technique, the company can effectively identify which customer segments are more likely to convert, enabling targeted marketing strategies and improved inventory management.

Incremental data migration, on the other hand, allows for the transfer of data in smaller, manageable batches. This technique is particularly advantageous for organizations that need to maintain operational continuity. By migrating only the changes made since the last migration, the company can significantly reduce downtime and ensure that the most current data is available in the new system. This approach also helps in maintaining data integrity, as it allows for thorough testing of each batch before moving on to the next. The hybrid approach combines elements of both full and incremental migrations, which can be beneficial in certain scenarios. However, it may introduce additional complexity and potential risks if not managed carefully. Direct data transfer, while seemingly straightforward, often lacks the necessary safeguards to ensure data integrity and may not be suitable for complex datasets. Given the company’s requirements for minimal downtime and data integrity, incremental data migration emerges as the most suitable technique. It allows for a phased approach, ensuring that the business can continue its operations while gradually transitioning to the new system. This method also facilitates easier troubleshooting and validation of data, thereby enhancing the overall success of the migration process.

Given that 60% of the patients in the 30-50 age group reported improved health outcomes, we calculate the number of patients in that group who experienced improvement: \[ \text{Improved patients} = 125 \times 0.60 = 75 \] Next, we need to find out what percentage this number (75 patients) represents of the total patient population (500 patients). The formula for calculating the percentage is: \[ \text{Percentage} = \left( \frac{\text{Part}}{\text{Whole}} \right) \times 100 \] Substituting the values we have: \[ \text{Percentage} = \left( \frac{75}{500} \right) \times 100 = 15\% \] Thus, 15% of the total patient population represents those in the 30-50 age group who reported improved health outcomes. This analysis is crucial for the healthcare organization as it allows them to understand the effectiveness of their interventions and tailor their outreach programs accordingly. By segmenting the patient data and analyzing outcomes, they can make informed decisions that enhance patient care and engagement, ultimately leading to better health outcomes across different demographics.

One of the key features of LWC is its component-based architecture, which allows developers to create reusable components that can be easily integrated into different parts of the application. This modular approach not only enhances maintainability but also promotes a more organized code structure, making it easier for teams to collaborate and scale the application over time. In contrast, Visualforce pages are more rigid and often require more server-side processing, which can lead to slower performance and a less dynamic user experience. While Visualforce is still a viable option for certain use cases, it does not provide the same level of efficiency and responsiveness that LWC offers. The other options present misconceptions about LWC. For instance, while LWC does facilitate integration with third-party libraries, it does not eliminate restrictions; developers must still ensure compatibility. Additionally, LWC does not automatically generate Apex controllers; developers must create these as needed to handle business logic. Lastly, LWC is not fully compatible with all legacy browsers, as it relies on modern web standards that may not be supported in older browser versions. Therefore, the primary advantage of LWC lies in its ability to deliver a modern, efficient, and responsive user experience, making it the preferred choice for developing custom user interfaces in Salesforce applications.

Creating separate data models, as suggested in option b, may lead to data silos, where each team operates independently without a clear understanding of the other’s activities. This can result in missed opportunities for collaboration and a lack of cohesive strategy, ultimately hindering overall business performance. Focusing solely on automating the sales process, as indicated in option c, neglects the critical role that marketing plays in the B2B sales cycle. A successful sales strategy must integrate marketing efforts to ensure that leads are nurtured effectively and that messaging is consistent across channels. Lastly, relying on third-party applications to manage marketing efforts separately from Salesforce, as proposed in option d, can complicate the workflow and lead to integration challenges. This approach may also create discrepancies in data, making it difficult to maintain data integrity and a unified view of customer interactions. In summary, a centralized data model that promotes collaboration and leverages Salesforce’s capabilities is essential for achieving a seamless integration of sales and marketing efforts, ensuring both teams work towards common objectives while maintaining data integrity and enhancing user experience.

Additionally, the Field Audit Trail feature enables tracking of changes made to specific fields over time, providing a historical record of data modifications. This is particularly important for organizations that must adhere to strict data protection regulations, as it allows them to demonstrate compliance through detailed audit trails. On the other hand, relying solely on standard Salesforce reports does not provide the granularity needed for effective monitoring, as these reports may not capture all user actions or changes. Similarly, using third-party applications without utilizing Salesforce’s native capabilities can lead to gaps in monitoring and potential security risks. Lastly, manual logging is not only labor-intensive but also prone to human error, which can compromise the integrity of the audit process. By combining Event Monitoring and Field Audit Trail, the compliance officer can create a robust framework that ensures comprehensive oversight of user activities, thereby enhancing security and compliance within the organization. This approach not only meets regulatory requirements but also fosters a culture of accountability and transparency in data handling practices.

Administrators can access these logs directly from the setup menu, which provides a straightforward way to monitor changes. However, it is important to note that the logs are not retained indefinitely; after 180 days, the logs are purged, which means that organizations must have a strategy in place if they need to retain this information for longer periods. Furthermore, the Audit Trail does not allow any user to view all changes made; access to these logs is restricted to users with administrative privileges. This ensures that sensitive information regarding changes to the system is protected and only accessible to authorized personnel. Additionally, the Audit Trail tracks a wide range of changes, including modifications to user permissions, changes to fields, and alterations to the overall configuration of the Salesforce environment. It does not limit itself to just user permissions, making it a comprehensive tool for auditing purposes. Lastly, the Audit Trail feature is not automatically enabled for all users; it requires specific configuration by an administrator to ensure that it meets the organization’s auditing needs. This means that organizations must actively manage and configure the Audit Trail to align with their compliance requirements. Overall, understanding the nuances of the Audit Trail feature is essential for any organization utilizing Salesforce, especially in regulated industries where data integrity and accountability are paramount.

While cost, user access, and vendor reputation are important factors, they do not directly influence the effectiveness of the sales process as significantly as the ability to analyze and report on sales data. A CRM that lacks robust analytics may lead to missed opportunities and ineffective sales strategies, regardless of its cost or the number of users. Therefore, focusing on the analytical capabilities of the CRM ensures that the sales team can leverage data to enhance their performance and achieve better outcomes. Moreover, integrating the CRM with existing marketing automation tools can further enhance its effectiveness by providing a comprehensive view of customer interactions across different touchpoints. This integration allows for a seamless flow of information, enabling the sales team to engage with leads more effectively and tailor their approaches based on historical data. Thus, prioritizing real-time analytics in the CRM selection process is crucial for driving sales success in a B2B environment.

\[ \text{Total Batches} = \frac{\text{Total Records}}{\text{Records per Batch}} = \frac{100,000}{10,000} = 10 \text{ batches} \] This means that the Batch Apex job will need to execute 10 batches to process all records. When implementing Batch Apex, it is crucial to consider Salesforce’s governor limits, which are designed to ensure that no single process monopolizes shared resources. Each batch execution is subject to limits on CPU time, heap size, and the number of records processed. In this scenario, since each batch takes approximately 2 minutes to process, the total execution time for 10 batches would be around 20 minutes. However, Salesforce imposes a limit of 10 minutes for a single batch execution, which means that the architect must ensure that the batch size is optimized to fit within this time frame. Additionally, it is essential to handle exceptions properly within the batch job. Implementing robust error handling and logging mechanisms will help in diagnosing issues that may arise during execution. The architect should also consider the impact of the batch job on system performance and user experience, particularly if the job is scheduled to run during peak hours. In summary, the correct answer is that the job will need to execute 10 batches, and the architect should keep in mind the governor limits, exception handling, and performance considerations during the implementation of the Batch Apex job.