ACD300 Appian Certified Lead Developer Questions and Answers

The best practice for reusing common UI components across multiple applications is to create shared objects, which are objects that can be referenced by other applications without being copied or duplicated. This way, any changes made to the shared object will be reflected in all applications that use it, ensuring consistency and maintainability. Therefore, instead of creating a duplicate version of the customer section for the new application, the developer should convert the original customer section into a shared object and reference it from both applications. Verified References: Appian Documentation, section “Shared Objects”.

When creating a script to add a new field to a table, you should consider two actions:

• Create a rollback script that removes the field. A rollback script is a script that can undo the changes made by the original script, in case something goes wrongor the changes need to be reverted. A rollback script is a good practice to have, as it can help to restore the previous state of the database and avoid any errors or inconsistencies. In this case, the rollback script should remove the new field from the table, and any other changes that were made by the original script.
• Create a script that adds the field and then populate it. A script that adds the field and then populate it is a script that can create the new field in the table, and then fill it with data from another table or source. This way, you can ensure that the new field has valid and consistent data, and that it can be used by the report in the upcoming release. In this case, the script should add the new field to the customer table, and then populate it with data from another table that contains the data used in the calculation.

• 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

The option B involves the lowest additional server cost and the least code retrofit effort, as it only requires one additional environment (DEV2) for the production support work stream. The production support work stream can use the existing STAGE environment for testing and user acceptance testing, as it is shared with the phase 2 development work stream. This way, there is no need to create a separate TEST2 environment or to retrofit any code from TEST to STAGE or from STAGE to PROD. Verified References: [Appian Certified Lead Developer study guide], page 16, section “Environment Strategy”.

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.