Appian ACD300 Appian Certified Lead Developer Exam Practice Test

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”.

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.

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”.

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.

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.

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”.

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”.

The best environment to use for load testing is acme, which is the production environment. This is because load testing should be performed on an environment that is as close as possible to the actual production environment, in terms of hardware, software, configuration, data, and user behavior. This way, the results of the load testing will be more realistic and reliable, and can help to identify and resolve any performance issues or bottlenecks before increasing the number of active users. Verified References: Appian Documentation, section “Load Testing”.