IFSE Institute ACD300 Appian Certified Lead Developer Exam Practice Test

Deploying an object that is dependent on another object from a different application can cause errors and inconsistencies in the production environment. The best practice is to halt the production deployment and contact the other team for guidance on how to promote the object to PROD. The other team may have a different deployment schedule, or they may have some dependencies or customizations that need to be considered. By communicating with the other team, you can ensure that the object is deployed in a safe and coordinated manner, and avoid any potential conflicts or issues. Verified References: [Appian Deployment Guide], [Appian Best Practices]

The first step in troubleshooting the degradation of the views is to run an explain statement on the views, identify critical areas of improvement that can be remediated without business knowledge. An explain statement is a tool that shows how a database executes a query or a view, and provides information about the cost, plan, and steps involved in the execution. By running an explain statement on the views, you can identify any inefficiencies or bottlenecks that are causing the degradation, such as missing indices, full table scans, nested loops, or hash joins. You can then apply some basic optimization techniques that do not require business knowledge, such as creating indices, limiting the number of columns or rows returned, using joins instead of subqueries, or using materialized views. Verified References: Appian Documentation, section “Query Optimization”.

The @Version annotation is a feature of Appian that allows for optimistic locking of CDTs. Optimistic locking assumes that concurrent updates to the same data are rare and does not lock the data until it is saved. If two users try to save changes to the same data, the user who saves first will succeed, while the user who saves second will get an error message and will have to resolve the conflict manually. This approach is suitable for the client’s requirement, as itallows any user to update a case without locking it, ensures no information is lost when concurrent edits occur, and does not require any additional effort from the process administrators. Verified References: [Appian Documentation], section “Optimistic Locking”.

In this situation, pm!timezone reflects the default time zone for the environment as specified in the Administration Console. pm!timezone is a process variable that returns the time zone of the process. If the time zone is not explicitly set in the process model, then pm!timezone returns the default time zone for the environment, which can be configured in the Administration Console. In this case, the time zone is set to the (default) pm!timezone, which means that the process model does not have a specific time zone, and therefore uses the default time zone for the environment.

The other options are not correct. Option A, the time zone of the server where Appian is installed, is not what pm!timezone reflects, as the server time zone may not be the same as the default time zone for the environment. Option B, the time zone of the user who most recently published the process model, is not what pm!timezone reflects, as the user’s time zone may not be the same as the default time zone for the environment. Option D, the time zone of the user who is completing the input task, is not what pm!timezone reflects, as the user’s time zone may not be the same as the default time zone for the environment.

• 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.

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 three required steps that will allow you to connect both systems are:

• A. Create a Web API object and set up the correct security. This will allow you to define an endpoint in Appian that can receive requests from Jira via webhook. You will also need to configure the security settings for the Web API object, such as authentication method, allowed origins, and access control.
• B. Configure the connection in Jira specifying the URL and credentials. This will allow you to set up a webhook in Jira that can send requests to Appian when specific tickets change. You will need to specify the URL of the Web API object in Appian, as well as any credentials required for authentication.
• C. Create a new API Key and associate a service account. This will allow you to generate a unique token that can be used for authentication between Jira and Appian. You will also need to create a service account in Appian that has permissions to access or update data related to Jira tickets.

The other options are incorrect for the following reasons:

• D. Give the service account system administrator privileges. This is not required and could pose a security risk, as giving system administrator privileges to a service account could allow it to perform actions that are not related to Jira tickets, such as modifying system settings or accessing sensitive data.
• E. Create an integration object from Appian to Jira to periodically check the ticket status. This is not required and could cause unnecessary overhead, as creating an integration object from Appian to Jira would involve polling Jira for ticket status changes, which could consume more resources than using webhook notifications. Verified References: Appian Documentation, section “Web API” and “API Keys”.

• uk.co.certification.simulator.questionpool.PList@187d4e90

• uk.co.certification.simulator.questionpool.PList@187d4ef0

• uk.co.certification.simulator.questionpool.PList@187d4f70

This is the best risk mitigation effort that both teams can employ to ensure that Team A’s capital project is only minorly interrupted, and Team B’s critical fix can be completed and deployed quickly to end users. By communicating with Team A, Team B can identify which component is causing the critical error in PROD, and check if there is any overlap of changes with Team A’s enhancements. If there is an overlap, Team B can version the component to its PROD state, which is the last stable version, before making any changes to fix the error. Then, Team B can deploy the fixed component to PROD, and version it back to its latest development state, which includes Team A’s enhancements. This way, Team B can avoid overwriting or losing any of Team A’s work, and ensure that the component is consistent across all environments. If there is no overlap, Team B can simply make the changes to the component and deploy it to PROD, without affecting Team A’s work.

The other options are not as effective. Option B, having Team A analyze their current codebase in DEV to merge the hotfix changes into their latest enhancements, would delay the deployment of the critical fix, as Team B would have to wait for Team A to finish their analysis and merge. Option C, having Team B address the changes in TEST, would interrupt Team A’s UAT process, and could cause conflicts or errors in TEST or PROD. Option D, having Team B address the changes directly in PROD, would be risky and not recommended, as it could introduce new errors or inconsistencies in PROD.

Verified References: [Appian Deployment Guide], [Appian Best Practices]

The field would benefit greatly from the index in the following scenarios:

• A. The field contains a textual short Business code. This is a scenario where an index can improve the performance of queries that search for exact matches or ranges of values in the field. A textual short Business code is likely to have high cardinality, meaning that it has many distinct values and low duplication. This makes the index more selective and efficient, as it can quickly narrow down the results based on the search criteria.
• C. The field contains many datetimes, covering a large range. This is a scenario where an index can improve the performance of queries that search for exact matches or ranges of values in the field. A datetime field is likely to have high cardinality, meaning that it has many distinct values and low duplication. This makes the index more selective and efficient, as it can quickly narrow down the results based on the search criteria.
• D. The field contains big integers, above and below 0. This is a scenario where an index can improve the performance of queries that search for exact matches or ranges of values in the field. A big integer field is likely to have high cardinality, meaning that it has many distinct values and low duplication. This makes the index more selective and efficient, as it can quickly narrow down the results based on the search criteria.

The other options are incorrect for the following reasons:

• B. The field contains long unstructured text such as a hash. This is a scenario where an index might not improve the performance of queries that search for exact matches or ranges of values in the field. A long unstructured text field is likely to have low cardinality, meaning that it has few distinct values and high duplication. This makes the index less selective and efficient, as it cannot quickly narrow down the results based on the search criteria. Moreover, indexing a long unstructured text field could increase thestorage space and maintenance cost for the database, which could affect the overall performance.
• E. The field contains a structured JSON. This is a scenario where an index might not improve the performance of queries that search for exact matches or ranges of values in the field. A structured JSON field is not a native data type in MySQL, and it requires special functions or operators to access or manipulate its elements. Indexing a structured JSON field could increase the complexity and overhead for the database, which could affect the overall performance. Verified References: Appian Documentation, section “Query Optimization”.