ACD300 Appian Certified Lead Developer Free Practice Exam Questions (2025 Updated)

The two advantages of having High Availability (HA) for Appian Cloud applications are:

B. Data and transactions are continuously replicated across the active nodes to achieve redundancy and avoid single points of failure. This is an advantage of having HA, as it ensures that there is always a backup copy of data and transactions in case one of the nodes fails or becomes unavailable. This also improves data integrity and consistency across the nodes, as any changes made to one node are automatically propagated to the other node.

D. In the event of a system failure, your Appian instance will be restored and available to your users in less than 15 minutes, having lost no more than the last 1 minute worth of data. This is an advantage of having HA, as it guarantees a high level of service availability and reliability for your Appian instance. If one of the nodes fails or becomes unavailable, the other node will take over and continue to serve requests without any noticeable downtime or data loss for your users.

The other options are incorrect for the following reasons:

A. An Appian Cloud HA instance is composed of multiple active nodes running in different availability zones in different regions. This is not an advantage of having HA, but rather a description of how HA works in Appian Cloud. An Appian Cloud HA instanceconsists of two active nodes running in different availability zones within the same region, not different regions.

C. A typical Appian Cloud HA instance is composed of two active nodes. This is not an advantage of having HA, but rather a description of how HA works in Appian Cloud. A typical Appian Cloud HA instance consists of two active nodes running in different availability zones within the same region, but this does not necessarily provide any benefit over having one active node. Verified References: Appian Documentation, section “High Availability”.

The three things that might help to address the findings of the Health Check report are:

B. Optimize the database execution using standard database performance troubleshooting methods and tools (such as query execution plans). This can help to identify and eliminate any bottlenecks or inefficiencies in the database queries that are causing slow query rules or slow write to data store nodes.

C. Reduce the size and complexity of the inputs. If you are passing in a list, consider whether the data model can be redesigned to pass single values instead. This can help to reduce the amount of data that needs to be transferred or processed by the database, which can improve the performance and speed of the queries or writes.

E. Use smaller CDTs or limit the fields selected in a!queryEntity(). This can help to reduce the amount of data that is returned by the queries, which can improve the performance and speed of the rules that use them.

The other options are incorrect for the following reasons:

A. Reduce the batch size for database queues to 10. This might not help to address the findings, as reducing the batch size could increase the number of transactions and overhead for the database, which could worsen the performance and speed of the queries or writes.

D. Optimize the database execution. Replace the new with a materialized view. This might not help to address the findings, as replacing a view with a materialized view could increase the storage space and maintenance cost for the database, which could affect the performance and speed of the queries or writes. Verified References: Appian Documentation, section “Performance Tuning”.

The best way to ensure consistency across the platform is to create a rule that can be used across the platform for section headers. This rule can be created in the common application, and then each application can be updated to reference this rule. This will ensure that all of the applications use the same color and size for the header, which will provide a consistent user experience.

The other options are not as effective. Option A, creating constants for text size and color, and updating each section to reference these values, would require updating each section in each application. This would be a lot of work, and it would be easy to make mistakes. Option C, creating one rule for each application, would also require updating each application. This would be less work than option A, but it would still be a lot of work, and it would be easy to make mistakes. Option D, creating a rule in each individual application, would not ensure consistency across the platform. Each application would have its own rule, and the rules could be different. This would not provide a consistent user experience.

Best Practices:

When designing a platform, it is important to consider the user experience. A consistent user experience will make it easier for users to learn and use the platform.

When creating rules, it is important to use them consistently across the platform. This will ensure that the platform has a consistent look and feel.

When updating the platform, it is important to test the changes to ensure that they do not break the user experience.

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 @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”.

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.

The error message indicates that the object with UUID ending in 18028821 has a dependency on another object with UUID ending in 7t00000i4e7a, which is missing from the target environment. Therefore, the first action to investigate the issue is to check whether the object with UUID ending in 7t00000i4e7a is included in this package or not. If not, then it should be added to the package or imported separately before importing the current package. Verified References: Appian Certified Lead Developer study guide, page 17, section “Importing and Exporting Applications”.

To create an integration from your Appian cloud instance to an application hosted within a customer’s self-managed environment, you need to ensure that there is a secure and reliable connection between the two systems. One way to do this is to set up a VPN tunnel, which is a virtual private network that encrypts and transports data over the internet. A VPN tunnel allows you to access the customer’s internal network and API endpoint from your Appian cloud instance, without exposing them to the public internet. This way, you can ensure the security and privacy of the data that is exchanged between the two systems. Verified References: [Appian Cloud VPN], [Appian Integration Guide]

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”.