The best practice in this situation is to add the custom controller to the Change Set before proceeding with the deployment. This ensures that all necessary components are present and that the Visualforce page can operate as intended once deployed. Deploying the Change Set without the custom controller (as suggested in option b) could lead to issues in production, as the Visualforce page would not have the required logic to function properly. Removing the Visualforce page (option c) is not advisable, as it may be a critical part of the deployment. Lastly, deploying the Change Set and expecting the Visualforce page to work without the custom controller (option d) is unrealistic and would likely result in errors. Therefore, the correct approach is to ensure that all dependencies, including the custom controller, are included in the Change Set to facilitate a smooth and successful deployment process. This highlights the importance of understanding component relationships and dependencies within Salesforce Change Sets, which is essential for effective application management and deployment strategies.

This design pattern promotes reusability and reduces code duplication, as the developer can utilize the existing methods of the standard controller. Additionally, it enhances maintainability, as any changes to the standard controller’s behavior will automatically propagate to the extension without requiring significant modifications. On the other hand, directly interacting with the database in a custom controller (option a) can lead to tightly coupled code that is difficult to maintain and test. Creating a singleton pattern (option c) is not suitable in this context, as it may introduce issues with state management across different user sessions. Lastly, utilizing a batch Apex class (option d) for account creation is inappropriate for a user interface scenario, as it does not provide immediate feedback to the user and complicates the user experience. In summary, the best practice is to use a controller extension that builds upon the standard controller, ensuring a clean, maintainable, and efficient design that adheres to Salesforce’s architectural principles.

The correct approach involves creating a test method that inserts multiple `Contact` records in a single transaction. This allows the developer to verify that the trigger correctly updates the `Last_Contacted_Date__c` field on all related `Account` records. It is essential to include assertions that confirm the expected outcomes, such as checking that the date field is set to the current date for each account associated with the inserted contacts. Moreover, the developer should also monitor governor limits during the execution of the trigger. Salesforce imposes limits on the number of records processed, the number of DML statements executed, and other resources to ensure fair usage across all tenants. By including checks for governor limits, the developer can ensure that the trigger is not only functional but also efficient and scalable. In contrast, the other options present flawed strategies. For instance, testing with a single `Contact` record does not adequately assess the trigger’s performance under bulk conditions, which is a critical aspect of Salesforce development. Similarly, failing to include assertions or focusing solely on execution without verification undermines the purpose of writing tests, which is to validate that the code behaves as intended. Therefore, the comprehensive approach that includes bulk testing and governor limit checks is essential for robust Salesforce development practices.

Refactoring the existing Apex classes to simplify logic may improve maintainability but does not directly address the coverage issue. While using the @isTest(SeeAllData=true) annotation can provide access to existing data, it is generally discouraged as it can lead to tests that are dependent on the state of the database, making them less reliable and harder to maintain. Increasing the number of assertions in existing test methods may help validate outcomes but does not necessarily increase code coverage if the underlying code paths are not being executed. In summary, the most effective strategy for enhancing test coverage is to create new test methods that utilize a variety of input values, ensuring that all branches of the conditional statements are exercised. This method not only improves coverage but also enhances the robustness of the tests by validating different scenarios that the application may encounter in real-world usage.

In contrast, a single-tenant architecture would require each customer to have their own instance of the application, leading to higher costs and more complex management as the user base grows. This model does not leverage the efficiencies of shared resources, making it less suitable for organizations anticipating rapid growth. On-premise deployment, while offering control over the infrastructure, lacks the inherent scalability and flexibility of cloud-based solutions like Salesforce. It requires significant investment in hardware and maintenance, which can hinder the ability to adapt to changing business needs. A hybrid cloud model combines both on-premise and cloud solutions, but it introduces complexity in data management and integration, which can compromise data integrity and security if not managed properly. Thus, prioritizing the multi-tenant architecture of the Salesforce Platform allows the company to benefit from built-in scalability, efficient resource management, and robust security measures, making it the optimal choice for their CRM implementation. This understanding of architectural principles is essential for leveraging the full capabilities of the Salesforce Platform in a growing business environment.

The implementation in Apex could look something like this: “`apex public Decimal calculateTotalPrice(List products) { Decimal totalPrice = 0; for (Product__c product : products) { if (product.Price__c > 50) { totalPrice += product.Price__c; } } return totalPrice; } “` In this code, `Product__c` represents the custom object for products, and `Price__c` is the field that holds the product’s price. The loop effectively checks each product’s price, ensuring that only those above $50 contribute to the total. Option b, which suggests using a SOQL query, is not suitable in this context because SOQL is primarily used for querying records from the database rather than performing calculations on a collection already in memory. Option c, involving a map, is unnecessary since the products are already in a list format, and using a map would complicate the process without providing any benefits. Lastly, option d, which suggests using a set, is also inappropriate because sets do not maintain order and do not allow for duplicate values, which could lead to incorrect calculations if products are not unique. Thus, the correct approach is to utilize a loop to sum the prices of products that exceed $50, ensuring clarity and efficiency in the calculation process. This method not only adheres to best practices in Apex programming but also aligns with the principles of object-oriented design by encapsulating the logic within a method that can be reused and tested independently.

First, we check the highest priority condition: whether the customer is a Gold member with purchases exceeding $10,000. Since the customer is a Silver member, this condition is not satisfied, and we move to the next condition. Next, we evaluate the condition for Silver members. The requirement states that a Silver member must have made purchases exceeding $5,000 to qualify for a 15% discount. In this case, the customer has made purchases of $6,500, which indeed exceeds $5,000. Therefore, this condition is satisfied, and the customer qualifies for a 15% discount. We should also consider the third condition, which applies to all other customers. It states that if purchases exceed $1,000, a 5% discount is applied. However, since the customer has already qualified for the Silver member discount, we do not need to evaluate this condition further. Lastly, since the customer meets the criteria for the Silver member discount, there is no need to check for the scenario where no discount is applied. Thus, the final discount to be applied to this customer is 15%. This scenario illustrates the use of conditional statements effectively, demonstrating how to prioritize conditions and apply the correct logic to derive the appropriate outcome based on the given criteria.

Additionally, incorporating a date field for the submission date is crucial for filtering and reporting purposes. Salesforce’s reporting tools can then be leveraged to create a summary report that aggregates feedback by type and allows users to filter results based on the submission date. This capability is essential for analyzing trends over time and understanding customer sentiment in relation to specific periods. In contrast, using a standard object (as suggested in option b) may not provide the necessary customization and flexibility that a custom object offers. Relying solely on standard reporting features could limit the ability to tailor the feedback tracking system to the company’s specific needs. Implementing a third-party application (option c) might introduce unnecessary complexity and cost, especially when Salesforce’s built-in capabilities can meet the requirements. Lastly, using text fields for feedback type (as in option d) undermines the benefits of structured data entry, making it difficult to maintain data quality and complicating the reporting process. Overall, the recommended approach aligns with Salesforce’s emphasis on customization, data integrity, and effective reporting, ensuring that the company can efficiently track and analyze customer feedback.

Increasing the log retention period (option b) does not directly address the issue of overwhelming log volume; it merely extends the time logs are available for review. Similarly, using the “Debug Only” option (option c) limits the logs to debug statements but may omit important context from other log levels that could provide insights into the trigger’s behavior. Disabling all logging levels except for “Apex Code” (option d) would significantly reduce the information available for analysis, potentially leading to missed insights from other components involved in the transaction. Thus, the best practice is to strategically set log levels for each component, allowing the developer to effectively analyze the relevant information without being overwhelmed by excessive log entries. This method not only enhances the clarity of the logs but also aids in pinpointing the root cause of issues within the Apex trigger.

The first condition, `Budget__c > 50000`, is straightforward and correctly uses the greater than operator to filter projects with a budget exceeding $50,000. The second condition, `Start_Date__c > 2022-01-01`, is also correctly formatted, ensuring that only projects that commenced after January 1, 2022, are included. It is important to note that the date format in SOQL should be in the format `YYYY-MM-DD`, which is adhered to in this query. The third condition, `Status__c = ‘Active’`, is essential for filtering the projects to only those that are currently active. This condition is critical because it directly aligns with the requirement to analyze only ongoing projects. In contrast, the other options present subtle but significant errors. Option b uses `>=` for the budget and start date, which would include projects with a budget of exactly $50,000 and those that started on January 1, 2022, which does not meet the specified criteria of being strictly greater. Option c incorrectly uses `>=` for the start date, which again allows for projects starting on the specified date, contrary to the requirement. Lastly, option d introduces a condition that excludes completed projects but does not address the active status requirement, leading to potential misinterpretation of the intended results. Thus, the correct SOQL query must precisely reflect the conditions without ambiguity, ensuring that only the relevant projects are retrieved for analysis. This understanding of SOQL syntax and logical operators is crucial for effective data retrieval in Salesforce.

This proactive validation reduces the number of invalid submissions reaching the server, thereby minimizing unnecessary processing and improving overall application performance. Furthermore, it aligns with Salesforce best practices, which advocate for a balanced approach to validation that includes both client-side and server-side checks. While server-side validation is critical for security and data integrity, relying solely on it can lead to a poor user experience, as users may not receive immediate feedback on their input errors. Option b, which suggests relying only on server-side validation, overlooks the importance of user experience and can lead to frustration if users are not informed of errors until after submission. Option c, which proposes a combination of validations but disables client-side checks, also fails to leverage the benefits of immediate feedback. Lastly, while HTML5 attributes can provide basic validation, they are not a comprehensive solution and may not cover all necessary validation scenarios, making option d insufficient for robust client-side validation. In summary, the most effective strategy is to implement JavaScript for client-side validation, ensuring that users receive prompt feedback on their input, which enhances both usability and data quality.

The correct query must first filter accounts based on the number of associated contacts. This is achieved using a subquery that groups contacts by `AccountId` and applies the `HAVING` clause to count the number of contacts. The condition `HAVING COUNT(Id) > 5` ensures that only accounts with more than five contacts are selected. Next, we need to filter these accounts based on their creation date. The `CreatedDate` field must be checked to ensure that it falls within the last year. The correct syntax for this is `CreatedDate = LAST_YEAR`, which accurately captures all accounts created in the previous year. Option (a) incorrectly uses `CreatedDate = LAST_YEAR`, which is not the correct syntax for filtering records created in the last year. The correct approach is to use `CreatedDate >= LAST_YEAR` to include all records from the beginning of the last year to the present. Option (b) also misplaces the `HAVING` clause, as it does not group the contacts properly before applying the count condition. Option (c) correctly groups the contacts but uses the wrong condition for the creation date, which limits the results incorrectly. Option (d) uses the correct grouping and counting but fails to specify the correct date range for the creation date, as it uses `CreatedDate >= LAST_YEAR`, which is not the requirement. Thus, the most efficient and correct SOQL query is the one that accurately combines these elements, ensuring that only accounts with more than five contacts created in the last year are returned, while also selecting only the necessary fields.

In contrast, making individual API calls for each record can lead to excessive overhead and increased latency, as each call incurs network latency and processing time on the server. This method is inefficient, especially when dealing with large datasets, as it can overwhelm the API limits and lead to throttling. Using synchronous calls for all operations may seem like a way to maintain data consistency; however, it can also lead to performance bottlenecks. Synchronous calls block the execution until a response is received, which can slow down the application, especially if the API is under heavy load. Lastly, implementing polling mechanisms to check for data updates can lead to unnecessary API calls, consuming resources and potentially hitting API limits. Instead, leveraging Salesforce’s push notifications or webhooks would be a more efficient approach to stay updated with data changes without constant polling. In summary, the best practice for optimizing API performance in this scenario is to utilize bulk API calls, as they allow for efficient data handling and minimize latency, making them the preferred choice for high-volume data operations in Salesforce integrations.

Public Groups are collections of users that can be used for sharing records, but they do not directly address the need for a specific record to be shared with a specific user outside of the role hierarchy. Sharing Rules are typically used to automate sharing based on record criteria or ownership, but they also rely on the existing role hierarchy and may not allow for the flexibility needed in this scenario. Apex Managed Sharing is a programmatic approach that allows developers to control sharing through code, but it is more complex and not necessary for a simple record-sharing scenario. Thus, Manual Sharing is the most effective and straightforward method for the Sales Rep to grant access to the Sales Manager without disrupting the established role hierarchy. This approach ensures that the Sales Manager can view the necessary record while maintaining the integrity of the organization’s access control structure.

By creating three distinct objects, the data model can effectively manage the relationships and attributes associated with each entity. The Order object will store information specific to each order, the Product object will contain details about each product, and the Order_Product junction object will facilitate the many-to-many relationship, allowing for multiple products to be associated with a single order and vice versa. The inclusion of a quantity field in the Order_Product object is crucial, as it enables the application to track how many of each product is included in a specific order. In contrast, the other options present various shortcomings. For instance, using a lookup relationship (as in option b) would not adequately represent the many-to-many relationship, as it would limit each order to a single product. Option c, which suggests consolidating all information into a single object, would lead to significant data redundancy and complicate data management, especially if the same product is ordered multiple times across different orders. Lastly, option d proposes a master-detail relationship, which is inappropriate for many-to-many scenarios, as it would restrict the flexibility needed to associate multiple products with multiple orders. Thus, the optimal approach is to implement a junction object that captures the necessary relationships and attributes while maintaining data integrity and minimizing redundancy. This design not only meets the business requirements but also aligns with best practices in data modeling.

A day consists of 24 hours, which translates to: $$ 24 \text{ hours} \times 60 \text{ minutes/hour} = 1440 \text{ minutes} $$ Given that each job takes 10 minutes to execute, we can calculate how many jobs can be scheduled in a day by dividing the total minutes in a day by the duration of each job: $$ \frac{1440 \text{ minutes}}{10 \text{ minutes/job}} = 144 \text{ jobs} $$ However, we must also consider the limit of 100 concurrent jobs. This means that at any given moment, only 100 jobs can be running simultaneously. Therefore, while theoretically, 144 jobs can be scheduled based on time alone, the actual number of jobs that can be executed concurrently is capped at 100. To ensure that we do not exceed the limit, we can only schedule 100 jobs to run concurrently. If we were to schedule more than 100 jobs, the additional jobs would have to wait until one of the currently running jobs completes, which would not allow for more than 100 jobs to be active at the same time. Thus, the correct answer is that the maximum number of jobs that can be scheduled to run in a single day, while adhering to the concurrent job limit, is 144. This reflects a nuanced understanding of both the time constraints and the limits imposed by the Salesforce platform on concurrent executions.

Using a public List to store orders and checking for duplicates by iterating through the List is less efficient, especially as the number of orders grows. This approach has a time complexity of O(n) for each addition, which can lead to performance issues in larger datasets. Creating a method that generates a unique order ID without checking for existing IDs does not address the core requirement of preventing duplicates. Even if the IDs are unique at the point of creation, there is no guarantee that they will remain unique if not validated against existing entries. Lastly, utilizing a Map to store orders by their IDs without implementing checks for duplicates fails to ensure data integrity. While a Map can provide quick access to orders based on their IDs, it does not prevent the addition of duplicate entries unless explicitly managed. In summary, the most effective and efficient method for managing customer orders while adhering to best practices in Salesforce development is to implement a private static Set to track unique order IDs, ensuring both data integrity and optimal performance. This approach aligns with the principles of encapsulation and efficient data management in object-oriented programming.

Using standard Salesforce list views (option b) may seem easier, but it limits customization and does not provide the tailored experience that the company desires. Static Visualforce pages (option c) do not allow for any interactivity, which would hinder user engagement and efficiency. Lastly, manipulating the DOM directly with JavaScript (option d) can lead to issues with maintainability and compatibility with Salesforce updates, as it bypasses the built-in framework that ensures proper functioning within the Salesforce ecosystem. In summary, the best approach is to utilize Visualforce components and Apex controllers to create a custom filtering interface. This method not only enhances user experience by providing dynamic interactivity but also aligns with Salesforce’s development guidelines, ensuring that the application is robust and future-proof. By following this strategy, the development team can effectively meet the unique needs of the business while adhering to best practices in Salesforce development.

Using `Test.startTest()` and `Test.stopTest()` is essential in this context, as it allows the developer to simulate the execution of the code within the governor limits imposed by Salesforce. This is particularly important when testing methods that may involve complex logic or bulk processing, as it helps to ensure that the method behaves as expected under different conditions. The second option, which suggests writing a single test method that only checks valid inputs, is insufficient because it does not account for potential edge cases that could lead to errors in production. Ignoring these scenarios could result in undetected bugs that may affect the application’s functionality. The third option incorrectly states that the `@isTest` annotation is sufficient without the use of `Test.startTest()` and `Test.stopTest()`. While the annotation is necessary for marking test classes, the simulation of governor limits is also critical for thorough testing. Lastly, the fourth option is fundamentally flawed as it dismisses the importance of testing edge cases altogether. In real-world applications, edge cases can often lead to significant issues if not properly handled, making it essential to include them in the testing strategy. In summary, a comprehensive unit testing strategy should include multiple test methods that cover a variety of scenarios, including edge cases, while utilizing Salesforce’s testing framework to ensure that the method performs correctly under all conditions. This approach not only enhances code reliability but also aligns with best practices in software development.

Creating multiple Visualforce pages for each user interaction (option b) would lead to a fragmented user experience and increased maintenance overhead, as each page would need to be managed separately. Using standard Salesforce page layouts (option c) limits customization and does not provide the dynamic interaction capabilities that AJAX offers. Lastly, implementing a custom JavaScript solution that manipulates the DOM directly (option d) can lead to performance issues and conflicts with Salesforce’s built-in functionalities, as it bypasses the framework’s lifecycle and event handling. In summary, leveraging AJAX within Visualforce components not only aligns with best practices for Salesforce development but also ensures that the application remains responsive and user-friendly. This approach adheres to Salesforce’s guidelines for building efficient applications while providing a robust solution for dynamic user interactions.

This method is preferable because it minimizes the risk of further complications that could arise from directly editing the main branch. Directly modifying the main branch (as suggested in option b) could lead to additional bugs or inconsistencies, especially if other developers are simultaneously working on their features. Creating a new branch from the main branch and merging it back without addressing the bug (as in option c) would not resolve the underlying issue and could propagate the bug into the main branch again. Finally, deleting the feature branch (as in option d) does not address the problem and could lead to loss of work and context for the developers involved. By reverting to the last stable commit and carefully reapplying changes, the team can ensure that they maintain a clean and functional codebase, which is essential for effective collaboration and ongoing development. This approach also aligns with best practices in version control, emphasizing the importance of maintaining a stable main branch while allowing for iterative development in feature branches.

Additionally, implementing validation rules on the Feedback object can help enforce data integrity by ensuring that all required fields are filled out correctly before submission. This reduces the risk of incomplete or erroneous data being entered into the system. Creating a custom Lightning component for data entry enhances the user experience by providing a tailored interface that can be designed to meet specific user needs, making it more intuitive and efficient for users to provide feedback. This approach allows for better control over the layout and functionality compared to standard Salesforce pages, which may not offer the same level of customization. In contrast, the other options present various drawbacks. A lookup relationship (option b) does not enforce the same level of data integrity as a master-detail relationship, potentially leading to data duplication and orphaned records. Using a junction object (option c) complicates the design unnecessarily, as feedback is typically meant to be associated with a single customer rather than multiple customers. Lastly, developing a separate Feedback object with no relationship (option d) completely undermines data integrity and can lead to significant issues with data management and reporting, as there would be no way to track which feedback belongs to which customer. Thus, the best practice in this scenario is to implement a master-detail relationship, utilize validation rules, and create a custom Lightning component to ensure both data integrity and an optimal user experience.

When the session manager is implemented as a Singleton, it guarantees that all parts of the application refer to the same instance, which simplifies the management of session data. For example, if multiple users are interacting with the application simultaneously, having a single session manager prevents issues such as race conditions or data corruption that could arise from having multiple instances trying to modify the same session data concurrently. In contrast, the other options present scenarios that do not align with the core purpose of the Singleton Pattern. Creating multiple instances of the session manager for different user roles or for testing purposes contradicts the essence of the Singleton Pattern, which is to limit instantiation. Additionally, the need for easy replacement of the session manager with different implementations is better suited to the Factory Pattern or Dependency Injection rather than the Singleton Pattern, which focuses on instance control rather than flexibility in instantiation. Thus, the correct understanding of the Singleton Pattern in this context emphasizes its role in maintaining a single, consistent instance that can effectively manage user sessions across the application, ensuring data integrity and reducing complexity in session management.

Set \( A \) contains the Frequent Buyers, which has 50 customers. Set \( B \) contains the Occasional Buyers, which has 80 customers. Therefore, the total number of customers who are either Frequent or Occasional Buyers can be calculated as follows: \[ \text{Total in } A \cup B = |A| + |B| = 50 + 80 = 130 \] Next, we need to find the probability of selecting one of these customers from the total customer base. The total number of customers is 200. The probability \( P \) can be calculated using the formula: \[ P(A \cup B) = \frac{|A \cup B|}{\text{Total Customers}} = \frac{130}{200} \] This simplifies to: \[ P(A \cup B) = \frac{13}{20} = 0.65 \] Thus, the probability that a randomly selected customer is either a Frequent Buyer or an Occasional Buyer is \( \frac{130}{200} \). This question tests the understanding of set theory, specifically the union of sets and probability calculations. It requires the student to not only identify the relevant sets but also to apply the principles of probability to derive the correct answer. Understanding how to manipulate and combine sets is crucial in Salesforce development, especially when dealing with customer segmentation and data analysis.

Sending individual requests for each product update (option b) would lead to excessive API calls, which could quickly exhaust the daily limits imposed by Salesforce and result in performance degradation. While combining updates for products with the same stock level (option c) may seem efficient, it does not address the fundamental requirement of keeping the total number of records within the limit of 200, which could lead to errors if the combined total exceeds this threshold. Lastly, while the REST API is indeed more efficient for certain operations, it does not negate the need to adhere to the specific requirements of the SOAP API in this scenario. Therefore, the optimal solution is to batch the updates into a single SOAP request, which not only aligns with Salesforce’s best practices but also ensures that the integration is efficient and scalable. This method allows the company to effectively manage their inventory updates while minimizing the risk of hitting API limits.

Additionally, the developer should be aware of the governor limits that Salesforce enforces to ensure efficient resource usage. For instance, the maximum number of records that can be returned in a single query is 2000. This means that if the query potentially returns more than 2000 records, the developer will need to implement pagination or additional filtering to manage the data effectively. Moreover, the complexity of the query is also limited; Salesforce allows a maximum of 20 child-to-parent relationships in a single SOQL query. This means that if the developer intends to include associated contacts in the query, they must ensure that the query does not exceed this limit. In contrast, the incorrect options present misunderstandings about the capabilities and limitations of the SOAP API. For example, the SOAP API does impose restrictions on query complexity, and it does support querying related objects through proper SOQL syntax. Furthermore, the assertion that the developer must use the REST API is incorrect, as the SOAP API is fully capable of handling such queries, provided the developer constructs them correctly. Understanding these nuances is essential for effectively utilizing the SOAP API in Salesforce integrations.

Following the basics, progressing to the Advanced Apex module allows learners to explore intricate subjects like asynchronous processing, which includes future methods, batch Apex, and queueable Apex. These advanced topics are crucial for developing scalable applications and integrating with external systems, which is often a requirement in real-world scenarios. Moreover, the hands-on challenges and projects associated with each module reinforce learning through practical application, ensuring that team members can apply what they have learned in a controlled environment. This approach not only solidifies their understanding but also prepares them for real-world challenges they may face in their roles. In contrast, allowing team members to choose their own advanced topics without a structured path can lead to gaps in knowledge, particularly for beginners who may not yet have the necessary background to tackle complex subjects. Focusing solely on advanced topics without a solid foundation can overwhelm beginners and hinder their learning process. Lastly, relying on external resources without the structured guidance of Trailhead can result in inconsistent learning experiences and missed opportunities for hands-on practice, which is vital for mastering Apex programming. Thus, a structured approach that builds from foundational knowledge to advanced concepts, while incorporating practical challenges, is the most effective strategy for ensuring comprehensive understanding and skill development in Apex programming for the entire team.

Moreover, encapsulating the total calculation logic within a private method allows the developer to change the implementation details without affecting other parts of the code that rely on the public interface of the class. This means that if the logic for calculating the total needs to be updated (for example, to include discounts or taxes), the developer can do so without worrying about breaking external dependencies. While it is true that private methods cannot be accessed from outside the class, this does not inherently lead to inefficiency or code duplication. Instead, it encourages a clean separation of concerns, where the public methods serve as the interface for interacting with the class, while the private methods handle the internal logic. Additionally, unit testing can still be performed on the public methods that utilize the private method, ensuring that the total calculation logic is indirectly tested through the public interface. In summary, using a private method for total calculation enhances data integrity and encapsulation, allowing for better maintainability and flexibility of the class while safeguarding against unintended modifications from external sources.

By creating a validation rule that checks these conditions, the system will automatically prevent any record that does not meet these criteria from being saved. This is crucial because relying solely on the user interface to prevent invalid entries (as suggested in option b) can lead to inconsistencies and data integrity issues, as users may bypass UI validations or enter data in bulk without proper checks. Using a trigger (as in option c) could enforce the validation rules, but it is generally more efficient and straightforward to use validation rules for this type of requirement. Triggers can introduce complexity and may lead to performance issues if not managed properly. Additionally, triggers execute after the record is saved, which means that invalid data could temporarily exist in the database before being caught, potentially leading to further complications. Option d suggests using a workflow rule to notify users of invalid entries, but this approach does not prevent the saving of invalid data. Workflow rules are reactive rather than proactive, meaning they only act after the fact, which does not align with the goal of maintaining data integrity from the outset. In summary, implementing a validation rule directly on the custom object “Product” is the most effective approach to ensure that all records adhere to the specified business logic, thereby maintaining data integrity and preventing invalid entries from being saved in the first place.

Once the request is sent using the `Http` class, the response can be processed. The JSON response format provided indicates that the data is nested within a “weather” object. To extract the temperature and humidity values, the `JSON.deserializeUntyped()` method is appropriate, as it allows for dynamic parsing of the JSON structure without needing a predefined Apex class. This method returns a map, which can be easily navigated to access the desired values. In contrast, the other options present various flaws. For instance, using a POST request when the service expects a GET request (as in option b) is incorrect, as is sending the API key as a URL parameter, which is not a secure practice. Neglecting to include the API key (as in option c) would result in an unauthorized request, and using a DELETE method (as in option d) is inappropriate for data retrieval. Additionally, parsing the response with `JSON.deserialize()` requires a predefined class structure, which is not necessary when using `JSON.deserializeUntyped()`. Thus, the correct implementation must adhere to the proper HTTP method, secure authentication practices, and effective JSON parsing techniques.