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D. The join between the tables order_detail and product needs to be fine-tuned due to Indices. This is correct because the result of the explain statement showsthat the join between these two tables has a high cost of 0.99, which indicates that it is inefficient and needs to be fine-tuned. One possible reason for the high cost is that there are no indices on the columns that are used for joining these two tables, which leads to a full table scan. Therefore, creating indices on these columns could improve the performance of this join.

E. The worst join is the one between the table order_detail and customer. This is correct because the result of the explain statement shows that the join between these two tables has a very high cost of 1.00, which indicates that it is the worst join in terms of efficiency and needs to be fine-tuned. One possible reason for the high cost is that there are no indices on the columns that are used for joining these two tables, which leads to a full table scan. Therefore, creating indices on these columns could improve the performance of this join.

The other options are incorrect for the following reasons:

A. The request is good enough to support a high volume of data, but could demonstrate some limitations if the developer queries information related to the product. This is incorrect because the request is not good enough to support a high volume of data, as it has two joins with very high costs that need to be fine-tuned. Moreover, querying information related to the product would not necessarily cause any limitations, as long as the join between order_detail and product is optimized.

B. The worst join is the one between the table order_detail and order. This is incorrect because the result of the explain statement shows that the join between these two tables has a low cost of 0.01, which indicates that it is efficient and does not need to be fine-tuned.

C. The join between the tables order_detail, order and customer needs to be fine-tuned due to indices. This is incorrect because there is no such join between three tables in the result of the explain statement. There are only two joins: one between order_detail and order, and another between order_detail and customer. Each of these joins needs to be fine-tuned separately due to indices.

Adding more engine replicas will increase the number of threads available to execute unattended process activities, such as timer events and sending notification emails. This will reduce the Java Work Queue Size and improve the performance of the application. Verified References: Appian Engine Performance Logs, Appian Engine Configuration

To design a process that is anticipated to be executed multiple times a day, that retrieves data from an external system and then calls various utility processes as needed, you should use Process Messaging to start the utility process and minimize the load on the execution engines. Process Messaging is a feature that allows you to send and receive messages between processes in Appian. By using Process Messaging, you can start the utility process asynchronously, which means that the main process does not have to wait for the utility process to finish before continuing. This way, you can improve the performance and scalability of your process design, and reduce the load on the execution engines.

The other options are not as effective. Option A, using the Start Process Smart Service to start the utility processes, would also start the utility process asynchronously, but it would require more configuration and maintenance than Process Messaging. Option B, starting the utility processes via a subprocess synchronously, would start the utility process as a part of the main process flow, which means that the main process would have to wait for the utility process to finish before continuing. This would reduce the performance and scalability of your process design, and increase the load on the execution engines. Option D, starting the utility processes via a subprocess asynchronously, would also start the utility process as a part of the main process flow,but it would not wait for the utility process to finish before continuing. However, this option would still create more overhead than Process Messaging, as it would create more instances of processes in Appian.

The best way to divide the work between the teams, avoiding issues caused by cross-dependency, is to group epics and stories by feature, and allocate work between each team by feature. This way, each team can focus on a specific feature and minimize the need for coordination or integration with the other team. This also reduces the risk of conflicts or errors when merging the code from both teams. Verified References: Appian Documentation, section “Agile Development”.

Requirement: Read barcode values from images containing barcodes and QR codes. Correct approach: C. Smart Service plug-in Exact explanation of correct approach taken from Appian Documentation: A smart service plug-in is a type of plug-in that allows you to create custom smart services that can be used in process models. A smart service can perform complex logic, interact with external systems, or manipulate data in Appian. A smart service plug-in can also leverage Java code to implement the functionality of the smart service. A smart service plug-in would be suitable for reading barcode values from images, as it can use Java libraries or APIs that can scan and decode barcodes and QR codes from image files. A smart service plug-in can also return the barcode values as outputs that can be used by other nodes or processes in Appian. A smart service plug-in can also be configured with input parameters, such as the image file, the barcode type, or the output format, that can customize the behavior of the smart service. A smart service plug-in can also have error handling and logging features that can handle any exceptions or failures that might occur during the barcode reading process.

Requirement: Display an externally hosted geolocation mapping applications interface within Appian to allow users of Appian to see where a customer (stored within Appian)is located. Correct approach: A. Web-content field Exact explanation of correct approach taken from Appian Documentation: A web-content field is a type of user interface component that allows you to display web content from an external source in a SAIL interface. A web-content field would be suitable for displaying an externally hosted geolocation mapping applications interface, as it can embed the web content in an iframe and render it within the Appian interface. You can also pass parameters to the web content, such as the customer’s location, using the url parameter of the web-content field. A web-content field can also interact with other components in the Appian interface, such as buttons, grids, or forms, using the postMessage API. This way, you can create a seamless user experience that integrates the external geolocation mapping applications interface with the Appian functionality.

Requirement: Display an externally hosted geolocation mapping applications interface within Appian to allow users of Appian to select where a customer is located and store the selected address in Appian. Correct approach: A. Web-content field and C. Smart Service plug-in Exact explanation of correct approach taken from Appian Documentation: A web-content field and a smart service plug-in would be suitable for displaying an externally hosted geolocation mapping applications interface within Appian to allow users of Appian to select where a customer is located and store the selected address in Appian. A web-content field would be suitable for displaying the external geolocation mapping applications interface, as explained above. A smart service plug-in would be suitable for storing the selected address in Appian, as it can use Java code to receive the address data from the web content, validate it, and write it to a data store entity or a process variable.

Requirement: Generate a barcode image file based on values entered by users. Correct approach: B. Component plug-in Exact explanation of correct approach taken from Appian Documentation: A component plug-in is a type of plug-in that allows you to create custom user interface components that can be used in SAIL interfaces. A component plug-in can also leverage Java code to implement the functionality of the component. A component plug-in would be suitable for generating a barcode image file, as it can use Java libraries or APIs that can encode values into barcode formats and generate image files. A component plug-in can also display the barcode image file in the Appian interface and allow users to download or print it. A component plug-in can also interact with other components in the Appian interface, such as text fields, buttons, or forms, using the a!refreshVariable() function. This way, you can create a dynamic user experience that updates the barcode image file based on the values entered by users.

When designing a data volume testing strategy for an Appian application that queries and writes to MySQL database, you should consider two key considerations:

Testing with the correct amount of data should be in the definition of done as part of each sprint. Data volume testing is a type of testing that verifies how well an application performs when handling large amounts of data. Data volume testing is important to ensure that the application meets the performance and quality requirements of the users and stakeholders. By including data volume testing in the definition of done as part of each sprint, you can ensure that each feature or functionality of your application is tested with realistic data volumes before being delivered to production. This way, you can identify and resolve any potential issues or bottlenecks early in the development cycle, and avoid any surprises or delays later on.

Data model changes must wait until towards the end of the project. Data model changes are changes that affect the structure or schema of your database, such as adding, modifying, or deleting tables, columns, indexes, or constraints. Data model changes are risky and costly to make, especially when dealing with large amounts of data. Data model changes can affect the performance, functionality, or integrity of your application and database. Therefore, data model changes must wait until towards the end of the project, when you have finalized your requirements and design decisions, and have minimized your data volume testing efforts. By waiting until towards the end of the project to make data model changes, you can reduce the impact and complexity of those changes, and avoid any unnecessary rework or regression.

The other options are not as effective. Option A, data from previous tests needs to remain in the testing environment prior to loading prepopulated data, is not a key consideration for designing a data volume testing strategy, but rather a best practice for preparing your testing environment. Option B, large datasets must be loaded via Appian processes, is not a key consideration for designing a data volume testing strategy, but rather a technical implementation detail that may or may not be suitable for your application. Option C, the amount of data that needs to be populated should be determined by the project sponsor and the stakeholders based on their estimation, is not a key consideration for designing a data volume testing strategy, but rather an input or assumption that you need to validate before conducting your data volume testing.

Requirement: Archive processes 2 days after completion or cancellation. Correct match: A. Processes that need to be available for 2 days after completion or cancellation, after which are no longer required nor accessible Exact explanation of correct match taken from Appian Documentation: This strategy is called “Archive after 2 days” and it is one of the options for process model data management in Appian. This strategy means that processes that complete or cancel will remain available for 2 days, after which they will be archived and no longer accessible. This strategy can help reduce the size of the process database and improve the performance of process reporting.

Requirement: Use system default (currently auto-archive processes 7 days after completion or cancellation). Correct match: C. Processes that remain available for 7 days after completion or cancellation, after which are archived when accessed Exact explanation of correct match taken from Appian Documentation: This strategy is called “Use system default” and it is one of the options for process model data management in Appian. This strategy means that processes that complete or cancel will remain available for 7 days, after which they will be archived when accessed. This strategy can help balance the availability and performance of process data, as it allows processes to be archived on demand rather than on a fixed schedule.

Requirement: Delete processes 2 days after completion or cancellation. Correct match: B. Processes that need to be available for 2 days after completion or cancellation, after which remain accessible Exact explanation of correct match taken from Appian Documentation: This strategy is called “Delete after 2 days” and it is one of the options for process model data management in Appian. This strategy means that processes that complete or cancel will remain available for 2 days, after which they will be deleted and no longer accessible. This strategy can help reduce the size of the process database and improve the performance of process reporting, but it also means that process data will be permanently lost.

Requirement: Do not automatically clean-up processes. Correct match: D. Processes that need to remain available without the need to unarchive Exact explanation of correct match taken from Appian Documentation: This strategy is called “Do not automatically clean-up” and it is one of the options for process model data management in Appian. This strategy means that processes that complete or cancel will remain available indefinitely without being archived or deleted. This strategy can help ensure the availability and integrity of process data, but it also means that the process database will grow over time and affect the performance of process reporting.

To meet the application requirement of allowing users to view summary and detailed information about report data, you should implement a drilldown report pattern to show detailed information about report data. A drilldown report pattern is a user interface component that displays data in a hierarchical structure, and allows users to expand or collapse different levels of data. For example, if the user is viewing a sales report by region, the drilldown report pattern could show something like “North America > USA > California > Los Angeles”. The user can click on any level of data to see more or less details. This way, the user can see both summary and detailed information about report data, and explore different aspects of the data.

The other options are not as effective. Option A, using Tiles as Cards pattern on the home page to prominently display application choices, would provide a way for users to access different parts of the application from the home page, but it would not show summary or detailed information about report data. Option B, implementing an Activity History pattern to track an organization’s activity measures, would provide a way for users to see the recent actions performed by themselves or others in the application, but it would not show summary or detailed information about report data. Option D, including a breadcrumbs pattern on applicable interfaces to show the organizational hierarchy, would provide a way for users to see where they are in the application, and easily go back to any previous level by clicking on the corresponding link, but it would not show summary or detailed information about report data.

Functional acceptance criteria are the conditions that a user story must satisfy to be accepted by the user or stakeholder. They should be specific, measurable, achievable, relevant, and testable. In this case, two functional acceptance criteria that would be considered ‘good’ are:

The user will receive an email notification when their order is completed. This is a specific, measurable, achievable, relevant, and testable criterion that describes a feature that the user needs to be informed of their order status.

The user cannot submit the form without filling out all required fields. This is a specific, measurable, achievable, relevant, and testable criterion that describes afeature that the user needs to provide valid and complete information for their order.

The other options are not as good. Option A, the user will click Save, and the order information will be saved in the ORDER table and have audit history, is not a functional acceptance criterion, but rather a technical implementation detail that is not relevant or visible to the user. Option C, the system must handle up to 500 unique orders per day, is not a functional acceptance criterion, but rather a non-functional requirement that describes a performance or quality attribute of the system.