Appian Certified Lead Developer: ACD300

Correct Answer: CD

To ensure that this story can be developed effectively, you should take two recommended steps: Maintain a communication schedule with the third-party resources. Communication is key when working on an integration involving a third-party system, as it can help to clarify the requirements, expectations, and dependencies of both parties. By maintaining a communication schedule, you can ensure that you have regular and timely updates on the progress, issues, and feedback of the integration. You can also use communication tools, such as email, chat, or video conferencing, to facilitate the communication and collaboration between your Appian team and the third-party resources. Break down the item into smaller stories. Breaking down a large and complex story into smaller and simpler stories can help to make the development process more manageable and efficient. By breaking down the item into smaller stories, you can reduce the scope and complexity of each story, and focus on delivering one feature or functionality at a time. You can also prioritize and assign the stories to different developers, and track their status and completion more easily. The other options are not as effective. Option A, acquiring testing steps from QA resources, is not a step to ensure that the story can be developed effectively, but rather a step to ensure that the story can be tested effectively. Option B, identifying subject matter experts (SMEs) to perform user acceptance testing (UAT), is also not a step to ensure that the story can be developed effectively, but rather a step to ensure that the story can be validated effectively. Option E, adding a view that joins the customer data to the data used in calculation, is not a step to ensure that the story can be developed effectively, but rather a design decision that may or may not be appropriate for the integration.

Correct Answer: D

To meet the application requirement of allowing users to navigate throughout the application while maintaining complete visibility in the application structure, and easily navigate to previous locations, you should include a breadcrumbs pattern on applicable interfaces to show the organizational hierarchy. A breadcrumbs pattern is a user interface component that displays the current location of the user within the application, and provides links to the previous levels of the hierarchy. For example, if the user is viewing a product details page, the breadcrumbs pattern could show something like "Home > Products > Product Details". This way, the user can see where they are in the application, and easily go back to any previous level by clicking on the corresponding link. 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 the organizational hierarchy or allow users to navigate to previous locations. 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 the organizational hierarchy or allow users to navigate to previous locations. Option C, implementing a drilldown report pattern to show detailed information about report data, would provide a way for users to explore different levels of data in a report, but it would not show the organizational hierarchy or allow users to navigate to previous locations.

Correct Answer: DE

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.