Relying solely on the built-in capabilities of Power Virtual Agents limits the bot’s functionality and responsiveness. While it may simplify the development process, it does not provide the flexibility needed to adapt to complex user interactions or integrate with external systems, which is crucial for a customer service bot. Implementing a separate application to manage API calls and data processing introduces unnecessary complexity into the architecture. This approach could lead to challenges in maintaining the system and ensuring that the bot remains responsive to user inputs, as it would require additional layers of communication between the bot and the external services. Using a third-party bot framework may seem appealing for advanced functionalities, but it often requires extensive customization to connect with Power Virtual Agents. This can lead to increased development time, potential integration issues, and a steeper learning curve for the development team, ultimately detracting from the user experience. In summary, leveraging Power Automate for seamless integration with external systems not only enhances the bot’s capabilities but also ensures a smooth and efficient user experience, making it the most suitable choice for the development team.

An Instant Flow, on the other hand, is typically triggered manually by a user action, such as clicking a button. This would not be suitable for the scenario described, as the goal is to automate the process without requiring manual intervention each time a response is submitted. Scheduled Flows are used for processes that need to run at specific intervals, such as daily or weekly tasks. While they are useful for recurring tasks, they do not fit the requirement of responding immediately to new feedback submissions. Business Process Flows are designed to guide users through a set of steps in a business process, often involving multiple stages and approvals. They are more structured and are not intended for simple automation tasks triggered by events like form submissions. In summary, the most appropriate flow type for automating the customer feedback process in this scenario is an Automated Flow, as it allows for immediate action based on the trigger of a new response in Microsoft Forms, followed by the necessary analysis and storage of the results in a SharePoint list. This understanding of flow types is crucial for effectively leveraging Microsoft Power Automate to streamline business processes.

In the context of the connector’s manifest file, defining the necessary actions and triggers is essential for the connector to function correctly within the Power Platform ecosystem. Actions represent operations that can be performed, such as retrieving employee data, while triggers can initiate workflows based on specific events. Properly defining these elements ensures that the connector can handle various scenarios, including error handling, which is vital for providing a robust user experience. The other options present significant drawbacks. For instance, limiting the connector to only retrieve data in XML format contradicts the requirement to handle JSON responses, which is the format returned by the API. Additionally, restricting the connector’s functionality to read operations only undermines its potential utility, as it may need to support create, update, or delete operations in the future. Finally, using a static API key for authentication is not advisable, as it poses security risks and does not comply with the OAuth 2.0 standard, which is designed to provide a more secure and flexible authentication mechanism. In summary, the most critical consideration when designing the custom connector is to implement proper authentication flows and define the necessary actions and triggers in the connector’s manifest file. This approach ensures secure access to the API while providing the flexibility needed for various operations and error handling scenarios.

Dynamic content enables the workflow to utilize variables and expressions that can change depending on the context of the request. For instance, if a request comes from a specific department, the workflow can automatically route it to the appropriate manager or team leader for approval. This adaptability is essential in environments where organizational roles and responsibilities may shift frequently. In contrast, static approval processes would not allow for such flexibility, as they are predefined and do not accommodate changes without manual intervention. Scheduled flows with fixed triggers are also limited in their ability to respond to dynamic changes, as they operate on a set schedule rather than in response to real-time events. Lastly, manual approval requests lack automation altogether, which defeats the purpose of using Power Automate to streamline processes. Thus, the ability to create approval workflows that leverage dynamic content is the most effective way to ensure that the automation remains relevant and efficient as the company evolves. This feature not only enhances the user experience but also significantly reduces the administrative burden associated with managing approval processes across multiple departments.

Once the flow is triggered, the “Get items” action is essential for retrieving data from the SharePoint list. This action allows the flow to access the necessary information for processing. Following this, a “Condition” action is crucial to evaluate whether the retrieved data exceeds the specified threshold. This step is vital as it determines the subsequent actions the flow will take. If the condition evaluates to true (i.e., the data exceeds the threshold), the flow should proceed to send an email summary to the management team using the “Send an email” action. Conversely, if the condition evaluates to false, the flow must log this event in a separate SharePoint list using the “Create item” action. This logging mechanism is important for tracking instances when the data does not meet the threshold, providing transparency and accountability in the reporting process. The other options present various shortcomings. For instance, using a manual trigger (option b) does not align with the requirement for automation, while scheduling the flow to run daily (option c) deviates from the specified weekly schedule. Lastly, sending an email only if the data is empty (option d) fails to address the core requirement of checking against a threshold. Thus, the correct configuration must incorporate a scheduled trigger, data retrieval, conditional logic, and appropriate actions based on the evaluation of that condition.

On the other hand, using a manual input method for the finance team to enter the invoice amount after approval introduces potential for human error and delays in processing. This approach undermines the automation goal and can lead to inconsistencies in data entry. Setting up a scheduled flow to run every hour to check for new invoices is inefficient compared to a trigger-based flow that activates immediately upon the addition of a new invoice. This could lead to delays in processing and approvals. Lastly, creating a static template for invoices that does not adapt to different formats is impractical, as it would limit the flow’s ability to handle diverse invoice designs, leading to potential failures in data extraction. Thus, the implementation of an AI Builder form processing model is the most effective and necessary step to ensure that the flow can accurately extract the invoice amount, thereby streamlining the entire invoice processing workflow.

The use of standard HTML controls (as suggested in option b) is not advisable because it may lead to issues with performance, maintainability, and compatibility within the Power Apps environment. Directly manipulating the DOM can also introduce complexities that are not aligned with the declarative nature of Power Apps. Option c, which suggests utilizing a pre-built control, may seem convenient but does not provide the flexibility or customization needed for specific use cases. Pre-built controls may not fully address unique requirements or allow for the level of interaction and data binding necessary for advanced applications. Lastly, option d, which proposes implementing a JavaScript function outside of the component framework, undermines the benefits of using PCF. This approach could lead to a lack of integration with the Power Apps lifecycle and may not adhere to best practices for performance and security. In summary, the best approach is to create a custom component using the Power Apps Component Framework, as it provides the necessary structure for dynamic data binding and user interaction while ensuring reusability across multiple applications. This aligns with the principles of modular design and enhances the overall maintainability of the application.

This method not only ensures that users receive timely assistance but also allows for a more personalized experience, as the flow can include relevant context about the user’s inquiry. For instance, the flow can capture the user’s conversation history and provide it to the human agent, enabling them to pick up the conversation without requiring the user to repeat themselves. On the other hand, simply providing a fallback option that directs users to a generic contact email lacks the proactive engagement necessary for effective customer support. Creating a separate chatbot for complex inquiries could lead to user confusion and fragmentation of support resources. Lastly, relying solely on analytics to monitor interactions and manually escalate issues is inefficient and reactive rather than proactive, potentially leading to delays in customer support. Thus, the best practice is to integrate the “Call an Action” feature to facilitate real-time escalation, ensuring that users receive the help they need promptly and effectively. This approach aligns with best practices in customer service automation, emphasizing the importance of user experience and operational efficiency.

The `DIVIDE` function is preferred over simple division because it handles division by zero gracefully, returning a blank instead of an error. This is particularly important in data analysis, where certain regions may have no sales data, leading to potential division errors. Option b is incorrect because it simply divides the sales amount by itself, which would always yield 1 (or 100% if multiplied by 100), failing to provide the desired percentage of total sales. Option c uses `SUMX`, which iterates over a table but does not correctly account for the total sales across all regions, leading to inaccurate results. Option d, while it attempts to calculate a percentage, does not utilize the `DIVIDE` function and could lead to errors if the denominator is zero. In summary, the correct DAX formula effectively calculates the percentage of total sales for each region by ensuring that the total sales across all regions is accurately represented in the denominator, while also handling potential division errors. This nuanced understanding of DAX functions and their interactions is crucial for effective data modeling and reporting in Power BI.

Moreover, implementing a fallback mechanism is essential for handling complex queries that the bot cannot resolve. This mechanism can be configured to escalate the conversation to a human agent when the bot encounters an intent it cannot handle, ensuring that users receive the assistance they need without frustration. This approach not only improves user satisfaction but also allows the bot to learn from these interactions over time, refining its understanding of user intents. On the other hand, manually coding all possible user intents and responses (option b) can lead to an unmanageable and inflexible system, as it would require constant updates and maintenance. Relying solely on pre-defined responses (option c) limits the bot’s ability to adapt to new queries and reduces its effectiveness. Lastly, ignoring the built-in capabilities of Power Virtual Agents in favor of a third-party AI service (option d) may complicate the integration process and lead to inconsistencies in intent recognition. Thus, the most effective strategy is to utilize the built-in AI capabilities of Power Virtual Agents, train the bot on various user intents, and implement a fallback mechanism for escalation, ensuring a seamless and efficient user experience.

Using the Power BI JavaScript API is essential for managing state and interactions within the visual. This API provides methods to handle data updates, user interactions, and visual rendering, ensuring that the custom visual integrates seamlessly with the Power BI environment. By leveraging the API, developers can create a responsive visual that updates automatically as new data comes in, enhancing the interactivity and usability of the report. In contrast, creating a static visual that only fetches data upon report loading would lead to outdated information being displayed until the user manually refreshes the report. This approach undermines the purpose of real-time data visualization. Similarly, using a third-party library without integrating it with Power BI could lead to performance issues, as it may not optimize data handling and rendering according to Power BI’s architecture. Lastly, relying solely on D3.js for rendering without utilizing Power BI’s data management capabilities would limit the visual’s interactivity and responsiveness, making it less effective in a dynamic reporting environment. Thus, the best practice for developing a custom visual that requires real-time data updates is to implement a data binding mechanism that effectively utilizes the Power BI JavaScript API, ensuring both performance and a high-quality user experience.

The clustered column chart is an appropriate choice for displaying total sales by product category, as it allows for easy comparison across categories. To calculate the percentage change in sales, the formula used is: $$ \text{Percentage Change} = \frac{\text{Current Quarter Sales} – \text{Previous Quarter Sales}}{\text{Previous Quarter Sales}} \times 100 $$ This formula provides a clear understanding of how sales have increased or decreased over the specified period. By adding a line chart overlay to the clustered column chart, the company can effectively visualize both the absolute sales figures and the relative change in performance, allowing for a more comprehensive analysis. The other options present less effective approaches. For instance, using a pie chart (option b) does not allow for easy comparison of sales figures across categories, and a stacked column chart (option c) does not provide a clear view of individual category performance. Additionally, separating the line chart and clustered column chart (option d) would hinder the ability to analyze the relationship between total sales and percentage change simultaneously. Therefore, the most effective approach is to combine the clustered column chart with a line chart overlay, providing a holistic view of sales performance and trends.

Furthermore, establishing relationships between entities (such as one-to-many or many-to-many relationships) is essential for referential integrity. This ensures that records in one entity can be accurately linked to records in another, preventing orphaned records and maintaining the integrity of the data. For example, if a customer entity is linked to an order entity, the relationship ensures that every order is associated with a valid customer, thereby preserving the integrity of the data. While calculated fields can provide valuable insights and enhance data analysis, they do not directly contribute to maintaining data integrity or managing relationships. Similarly, exporting data to external systems and configuring security roles are important aspects of data management but do not address the core requirement of ensuring that the data model supports complex relationships and maintains referential integrity. Therefore, prioritizing the implementation of business rules and relationships is the most effective approach for the company to achieve its goals in managing customer data within Dataverse.

In contrast, increasing the complexity of the NLP algorithms without additional training data may lead to overfitting, where the bot performs well on training data but poorly on real user interactions. Similarly, limiting the bot’s responses to a predefined set of answers can restrict its ability to handle diverse queries, ultimately leading to user frustration. Reducing the number of intents the bot can recognize may simplify its decision-making process, but it can also result in a loss of functionality, as the bot may not be able to address a wide range of user inquiries. In summary, the most effective strategy for improving the bot’s understanding of user intents involves leveraging user feedback to refine its responses and enhance its overall performance. This approach aligns with best practices in chatbot development, emphasizing the importance of user-centered design and continuous learning.

Once the trigger is activated, the next step involves defining an action that specifies what should happen next. In this scenario, the action required is to update the SQL database with the details of the newly added item. This action is vital as it ensures that the data remains synchronized across different platforms, which is a core benefit of using Power Automate. The other options presented do not align with the requirements of the scenario. For instance, a trigger for the SQL database would not initiate the flow based on SharePoint events, and creating a new item in SharePoint does not address the need to update the SQL database. Similarly, checking the status of the SQL database or sending an email notification does not fulfill the primary objective of integrating SharePoint and SQL through automation. In summary, the correct configuration involves establishing a trigger that responds to new items in the SharePoint list and an action that updates the SQL database accordingly. This understanding of triggers and actions is fundamental to leveraging Power Automate effectively, enabling organizations to enhance their operational efficiency through automation.

Accessibility standards, such as the Web Content Accessibility Guidelines (WCAG), emphasize the importance of contrast ratios and text scalability to accommodate users with varying levels of visual acuity. By implementing these features, the development team not only enhances the user experience for elderly patients but also complies with legal requirements that mandate accessibility in digital products. On the other hand, utilizing complex navigation structures can lead to confusion, particularly for users who may not be tech-savvy. This approach could alienate elderly patients who might struggle with intricate menus. Similarly, incorporating numerous animations may distract or overwhelm users, detracting from the app’s primary purpose of providing healthcare information and services. Lastly, designing for a single device type limits the app’s reach and usability across different platforms, which is counterproductive in a healthcare setting where users may access the app from various devices. In summary, prioritizing high-contrast color schemes and scalable text sizes aligns with both usability and accessibility principles, ensuring that the application is user-friendly for all demographics, particularly those with visual impairments. This approach not only enhances the overall user experience but also adheres to necessary compliance standards, making it the most effective design principle in this context.

For instance, if the API returns a 401 Unauthorized error, the connector should inform the user that their authentication token may be invalid or expired. Similarly, if a 404 Not Found error occurs, the connector should indicate that the requested resource does not exist. This level of error handling not only improves user experience but also aligns with best practices in API design, which emphasize the importance of clear communication regarding errors. In contrast, the other options present significant drawbacks. For example, limiting the connector’s functionality to only support GET requests (option c) would severely restrict its usability, as many APIs require POST, PUT, or DELETE methods for full functionality. Additionally, using hardcoded values for authentication tokens (option d) poses a security risk, as it exposes sensitive information and does not allow for token refresh mechanisms. Lastly, not allowing data transformation or filtering (option b) would hinder the connector’s ability to adapt to various application needs, making it less versatile and effective. Overall, a well-designed custom connector must prioritize error handling, support multiple HTTP methods, and allow for dynamic data manipulation to ensure it meets the diverse requirements of the applications it serves.

Using Power Automate for this purpose allows for automation of the discount application process while maintaining control over the conditions under which discounts are granted. The flow can be designed to include conditional logic that applies the appropriate discount percentage based on the total order amount. This approach not only streamlines the order processing but also ensures that the business rules are adhered to without manual intervention. The other options present flawed approaches. For instance, running a daily flow to apply discounts (option b) disregards the real-time nature of order processing and could lead to incorrect discounts being applied. Option c, which focuses solely on checking for restricted items, fails to consider the critical aspect of the total order amount, leading to potential revenue loss. Lastly, option d introduces unnecessary complexity by requiring manual approvals for discounts, which contradicts the goal of automating the process and could slow down order fulfillment. Thus, the correct implementation involves a comprehensive business process flow that integrates both the discount logic and item restrictions effectively.

Furthermore, the decision to implement complex navigation menus that require multiple taps to access essential features can lead to frustration and decreased usability. Users should be able to navigate through an application intuitively and efficiently. A well-designed app should minimize the number of actions required to reach important functionalities, adhering to the principle of user-centered design. In contrast, while aesthetic appeal, user engagement, and performance optimization are important aspects of app design, they do not directly address the fundamental need for accessibility. A visually appealing app that is difficult to navigate or unusable for individuals with disabilities fails to meet the core requirements of effective app design. Therefore, the primary violation in this scenario is the neglect of accessibility principles, which are essential for creating an inclusive and user-friendly application.

This method is crucial because it enhances the bot’s responsiveness and adaptability, ensuring that customers feel heard and valued. By recognizing specific phrases that suggest frustration or confusion, the bot can take proactive measures rather than providing a generic response, which may further frustrate the customer. On the other hand, simply responding with a generic message (option b) does not address the customer’s needs and can lead to a poor user experience. Ignoring inquiries that do not match predefined keywords (option c) limits the bot’s effectiveness and can result in lost opportunities for customer engagement. While developing a complex algorithm to analyze sentiment (option d) may seem advanced, it can introduce unnecessary complexity and may not be as reliable as a straightforward keyword recognition system. In summary, implementing a fallback mechanism that recognizes specific dissatisfaction indicators allows the bot to maintain a high level of customer service and ensures that inquiries are handled appropriately, thereby improving overall customer satisfaction and engagement.

Furthermore, configuring role-based access control (RBAC) within the application is crucial. RBAC enables the company to define specific roles and permissions for different user groups, ensuring that only authorized users can access sensitive data. This method allows for granular control over who can view or edit data, aligning with best practices for data governance. On the other hand, deploying the application as a public app (option b) would expose it to all users, increasing the risk of unauthorized access to sensitive information. Utilizing a single sign-on (SSO) solution without additional security measures (option c) may simplify user access but does not provide adequate protection against potential security threats. Lastly, implementing a basic authentication method without user verification (option d) is insufficient for protecting sensitive data, as it does not verify user identities effectively. In summary, the combination of AAD for authentication and RBAC for access control not only secures the application but also ensures that it meets the compliance and governance requirements necessary for handling sensitive data. This approach balances accessibility for authorized users with the need for stringent security measures, making it the most suitable deployment method in this context.

Cascading deletes automatically remove all related records in the “Order” entity when a record in the “Customer” entity is deleted. This method not only simplifies the deletion process but also ensures that there are no orphaned records left in the database, which can lead to data integrity issues. On the other hand, using a manual process to delete orders after a customer is deleted (option b) is prone to human error and can lead to situations where orders are not deleted, resulting in orphaned records. Creating a trigger to check for associated orders before deleting a customer (option c) adds unnecessary complexity and can still lead to issues if the trigger fails or is not executed properly. Lastly, setting the relationship to restrict deletion of customers with existing orders (option d) prevents the deletion of customers altogether, which is not the desired outcome in this scenario. Thus, implementing cascading deletes is the most efficient and reliable method to maintain data integrity in this relationship, ensuring that all related orders are automatically removed when a customer is deleted. This approach aligns with best practices in database management and helps maintain a clean and consistent data model.

When the flow is triggered by the creation of a new support ticket, the condition action will check the value of the ticket priority field. If the condition evaluates to true (i.e., the priority is “High”), the flow will then execute the email notification action, ensuring that only relevant notifications are sent to the support team. This method not only optimizes the workflow by reducing unnecessary emails but also enhances the team’s responsiveness to critical issues. On the other hand, setting the email notification action to always execute would lead to notifications being sent for all ticket priorities, which defeats the purpose of having a conditional check. Using a parallel branch to send the email notification while checking the ticket priority simultaneously could lead to confusion and unnecessary complexity, as it does not effectively control the flow of actions based on the priority. Lastly, creating a separate flow for high-priority tickets would complicate the process and require additional maintenance, making it less efficient than simply incorporating a condition within the existing flow. Thus, the implementation of a condition action is the most logical and efficient solution to ensure that the email notification aligns with the specified criteria of ticket priority. This approach adheres to best practices in workflow automation, promoting clarity and efficiency in the process.

Conditional formatting in Power Apps allows developers to set visibility rules for controls based on the values of other controls. In this case, when a user selects “Dissatisfied,” the developer can configure the form to display additional fields for comments and suggestions. This not only streamlines the user experience by presenting relevant fields but also reduces the cognitive load on users, as they are only prompted to provide information that is pertinent to their feedback. Creating separate forms for each possible selection (option b) would lead to unnecessary complexity and maintenance challenges, as the developer would need to manage multiple forms and ensure consistency across them. A static form (option c) would not meet the requirement for dynamic interaction, as it would present all fields regardless of user input, potentially overwhelming users with irrelevant questions. Lastly, while implementing a JavaScript function (option d) could manipulate the DOM, it is not a standard practice within Power Apps, which is designed to minimize custom code and leverage its built-in functionalities for better performance and maintainability. In summary, using conditional formatting aligns with best practices in Power Apps development, ensuring a responsive and intuitive user interface that adapts to user input effectively. This approach not only enhances user satisfaction but also adheres to the principles of efficient application design.

The correct formula uses `CALCULATE` to sum `SalesData[SalesAmount]` while applying two filters on `SalesData[SaleDate]`. The first filter ensures that only sales from October 1 of the previous year onward are included, while the second filter restricts the data to before January 1 of the current year. This effectively filters out any sales that occurred in the last quarter of the previous year, which is crucial for the analysis. The other options do not apply the necessary filters correctly. Option b simply sums all sales amounts without any date restrictions, which would include unwanted data. Option c applies a filter to exclude sales from the current year but does not specifically exclude the last quarter of the previous year. Option d uses `SUMX`, which iterates over a table but does not apply the necessary date filters, leading to an inaccurate total. Thus, the correct approach is to use `CALCULATE` with the appropriate date filters to ensure the analysis reflects the desired timeframe accurately.

Moreover, the JSON response from the API may require transformation to fit the data model used within the Power Platform. This transformation could involve mapping fields from the API response to the expected schema in Power Apps or Power Automate. Without proper error handling and transformation logic, users may encounter unexpected behaviors or data inconsistencies, leading to a poor user experience. In contrast, options that suggest limiting the connector’s functionality to only read operations or neglecting error handling entirely overlook the importance of a comprehensive approach to API integration. Security implications, such as ensuring that sensitive employee data is accessed securely and that unauthorized access is prevented, are paramount. Therefore, a well-designed custom connector must prioritize authentication and error management to ensure reliability, security, and user satisfaction in the integration process.

The other options present significant drawbacks. Implementing a manual input step would introduce human error and slow down the process, as it relies on the finance manager to correctly enter the amount each time. Using a predefined template may not be feasible if invoices come in various formats, leading to inconsistencies in data extraction. Relying on a third-party application adds unnecessary complexity and potential points of failure, as it requires additional integration and may not be as seamless as using built-in Power Automate functionalities. Once the invoice amount is successfully extracted, calculating the 10% tax can be easily achieved using a simple expression in Power Automate, such as multiplying the extracted amount by 0.10. This ensures that the workflow remains efficient and minimizes the risk of errors, ultimately leading to a smoother invoice processing experience. Therefore, utilizing the “Extract Text” action is essential for the flow to function correctly and achieve the desired automation goals.

This mechanism is fundamental to creating an effective conversational experience, as it allows the bot to provide relevant information and assistance tailored to the user’s needs. The effectiveness of triggers relies on the bot’s ability to accurately interpret user input, which can involve natural language processing (NLP) techniques to understand variations in phrasing and context. In contrast, the other options present misconceptions about triggers. For example, triggers are not random events; they are systematically defined to ensure predictable and relevant interactions. Additionally, triggers are not based solely on the time of day, as they depend on the content of the user’s message. Lastly, while manual commands can be part of bot operations, they do not define the concept of triggers, which are inherently automated responses based on user input. Understanding the role of triggers is essential for developers to create responsive and user-friendly bots that enhance customer interactions.

Limiting the chatbot to only respond to the exact phrase “availability” would severely restrict its functionality and frustrate users who phrase their questions differently. This approach would lead to a poor user experience, as many legitimate inquiries would go unrecognized. Removing the topic entirely and relying on default fallback responses would not address the issue and would likely result in a lack of relevant answers, further diminishing user satisfaction. While increasing the complexity of the chatbot’s language model might seem like a viable solution, it is not the most practical approach in this scenario. Power Virtual Agents is designed to be user-friendly and accessible, and often, simpler solutions like expanding trigger phrases yield better results without the need for advanced configurations. Therefore, enhancing the trigger phrases with synonyms and variations is the most effective strategy for improving the chatbot’s understanding of product availability inquiries. This method aligns with best practices in chatbot development, ensuring that the system is responsive to the diverse ways users may express their needs.

In this scenario, when the user attempts to update the field through a Power Apps form, they will be able to see the field and its current value. However, since their role does not allow them to write to that field, any attempt to save changes will trigger an error message indicating that they do not have the necessary permissions to modify that field. This behavior is consistent with the principles of field-level security, which is designed to protect sensitive data by ensuring that only authorized users can make changes. Moreover, it is important to note that field-level security settings are enforced at the database level, meaning that even if the user interacts with the form and attempts to submit changes, the underlying security model will prevent any unauthorized modifications from being saved. This ensures data integrity and compliance with organizational policies regarding sensitive information. Understanding how field-level security interacts with user roles and permissions is essential for developers working with the Power Platform, as it directly impacts how applications are designed and how users interact with data. Therefore, it is crucial for developers to carefully configure security roles and test user access to ensure that sensitive information is adequately protected while still allowing necessary access for legitimate users.