ACD200 Questions and Answers

 The metric from the data_store_details.csv file that will help you understand the number of operations against data store is Total Count. This metric represents the total number of queries, inserts, updates, deletes, and executes performed against the data store during the specified time period. You can use this metric to identify which data stores are heavily used and may need performance tuning or scaling. References: [Data Store Details Report], [Data Store Metrics]

When creating a Web API, you need to configure two items in the Administration Console: an API key and a service account. An API key is a unique identifier that is used to authenticate requests to your Web API. You can create and manage API keys in the Administration Console under Integration > API Keys. A service account is a user account that is used to execute the Web API expression rule. You can select a service account in the Administration Console under Integration > Web APIs when you create or edit a Web API. References: [Web APIs], [API Keys], [Service Accounts]

In Scrum, the product owner is the person who represents the voice of the customer and the stakeholders. The product owner is responsible for defining and prioritizing the product backlog, which is a list of features, requirements, enhancements, and fixes that need to be delivered by the team. The product owner collaborates with the team and the scrum master to ensure that the product backlog is clear, valuable, and aligned with the product vision and goals.

The xsd:key element is not supported within an Appian CDT. This element is used to define a key constraint for an element or a group of elements. Appian does not support key constraints in CDTs, as they are not relevant for data storage or manipulation. Instead, Appian uses primary keys and foreign keys to enforce uniqueness and referential integrity in CDTs. These keys are specified using the @Id and @JoinColumn JPA annotations, respectively. References: Supported XSD Elements and JPA Annotations

The lowest permission required on the data store in order to query, write, and delete data is Viewer. This is because the data store permissions only control the ability to view and modify the data store object in Appian Designer, not the actual data in the database. To query, write, and delete data, the user needs to have the appropriate SQL permissions on the database tables or views that are mapped to the data store entities. References: Data Store Permissions

The likely cause for a task on a newly-launched process in Production being slow to respond to user input is having too many SAIL components on the task. SAIL components are the UI elements that make up an interface in Appian. Having too many SAIL components on a task can negatively impact the performance and user experience of the interface, as it increases the rendering time and complexity of the interface. It can also cause browser compatibility issues or memory leaks. It is recommended to limit the number of SAIL components on a task to less than 100, and to use grids, collapsible layouts, or tabs to organize and display data efficiently. References: SAIL Components Overview, SAIL Performance Tips

Activity chaining is a feature that allows users to complete multiple tasks in a row without returning to their task list. However, activity chaining can also have some drawbacks, such as increased memory consumption, longer transaction times, and reduced user feedback. Therefore, some actions can be taken to make the process model more efficient, such as:

• Shorten long process chains by combining transactions into the smallest number of nodes. This can reduce the memory footprint and the database load of the process model.
• Avoid chaining system nodes like Integrations. These nodes can have unpredictable execution times and can cause timeouts or errors if chained. It is better to use asynchronous integrations or message events to decouple the process flow from external systems.
• Move transactions that can be done asynchronously outside of the chain. This can improve the responsiveness of the process model and allow parallel processing of independent tasks.

Therefore, the correct answers are B, C, and D.

References:

• Activity Chaining
• Performance Best Practices

A joining table can be used to hold instances of Student/Class relationships, so that it can be supported in Appian and remain in at least First Normal Form. A joining table is a table that contains only foreign keys that reference the primary keys of other tables. A joining table can be used to model a Many-to-Many relationship between two entities, such as Student and Class. For example, a joining table called Enrollment could have two columns: Student_ID and Class_ID, which reference the primary keys of the Student and Class tables respectively. Each row in the Enrollment table represents an instance of a student enrolled in a class. References: [Joining Tables], [Many-to-Many Relationships], [First Normal Form]

 Two places to get information on process model memory usage are:

• Process monitoring tab. The process monitoring tab is a feature in the Appian Designer that allows you to monitor and manage the performance and status of process models and instances. You can use the process monitoring tab to view various metrics and statistics about your processes, such as memory usage, execution time, node count, error count, etc. You can also filter, sort, and export the data for further analysis. The process monitoring tab can help you identify which processes have the highest memory footprint and potential performance issues.
• Appian Health Check report. The Appian Health Check report is a tool that provides insights into application design patterns and performance risks in your environment. The report covers four areas of your environment: design, user experience, infrastructure, and configuration. The report also includes graphs highlighting historical trends, such as user activity, average response times, and resource utilization. The Appian Health Check report can help you identify which processes have the highest memory consumption and suggestions for mitigating them. References: Process Monitoring Tab, Understanding the Health Check Report

The performance log that should be used to see the mean evaluation time of a slow-performing expression rule every hour is expressions_details.csv. This log records information about how expression rules are performing in the system, such as the number of evaluations, the total time, the mean total time, and the standard deviation of the total time. The log is written to once every hour, and it provides fine-grain aggregation by type of expression rule. The mean total time column shows the average time it took to evaluate an expression rule in milliseconds. Therefore, the correct answer is A.

References:

• Expression Performance Logs
• Performance Best Practices

Three possible reasons for high memory consumption of a process model are:

• Too many process variables. Process variables are used to store data that is needed throughout the process execution. However, having too many process variables can increase the memory usage of the process engine, especially if the variables store large or complex data types, such as documents or CDTs. It is recommended to use local variables whenever possible, and to delete or nullify process variables that are no longer needed.
• Nested CDTs with large numbers of fields. CDTs are custom data types that define the structure and validation of business data in Appian. CDTs can be nested within other CDTs to create complex data models. However, nesting CDTs with large numbers of fields can result in high memory consumption and performance degradation, as each field requires additional memory allocation and processing. It is recommended to limit the number of fields and nesting levels of CDTs, and to use references instead of embedding whenever possible.
• Too many nodes. Nodes are the graphical elements that represent the activities and events in a process model. Having too many nodes in a process model can increase the memory usage and complexity of the process execution. It can also make the process model harder to read and maintain. It is recommended to simplify the process model by using subprocesses, smart services, or expression rules to encapsulate common or reusable logic. References: Process Variables, Custom Data Types, Process Model Best Practices

 The load time of this interface component can be improved by reducing the number of queries performed and the amount of data transferred. One way to achieve this is to prefetch the transaction types and use the displayvalue() function to display the Transaction Type for each transaction. This way, only one query is performed to get the transactions, and the transaction types are retrieved from a local variable instead of a separate query. This reduces the network latency and the database load, which can improve the performance of the interface. Therefore, the best answer is D.

References:

• Prefetching Data
• displayvalue() Function

 The question is about troubleshooting slow response times on a SAIL interface. The following are two potential causes of the performance issues:

• Multiple a!save operations: This means that the interface has multiple expressions that use a!save to store data in variables or update components. This can cause performance issues, as each a!save operation triggers a server round-trip and refreshes all dependent components on the interface. You should minimize the use of a!save operations and use local variables or component parameters instead.
• Configuration of refresh variables: This means that the interface has components that use refresh variables to control when they should be refreshed based on changes in other components or variables. This can cause performance issues, as some components may be refreshed unnecessarily or too frequently due to incorrect or excessive refresh variables. You should optimize the refresh variables and use them only when needed.

The following are not likely causes of slow response times on a SAIL interface:

• Multiple rule inputs: This means that the interface has expressions that use rule inputs to pass data or parameters to expression rules. This does not affect the performance of the interface, as rule inputs are evaluated on the server and do not cause additional server round-trips or refreshes.
• Stacked button layout: This means that the interface has a layout component that arranges buttons in a vertical stack. This does not affect the performance of the interface, as it is a simple layout component that does not require any server interaction or refresh logic.

References:

• a!save()
• Refresh Variables
• Rule Inputs
• Stacked Button Layout

 A materialized view is the best option to show joined data from 5 tables that contain a large number of rows and could generate a large result set after executing the joins. A materialized view is a physical table that holds the results of the SQL that a view would normally be constructed from and can be generated periodically. A materialized view can improve performance by reducing the execution time of complex queries that involve multiple joins, aggregations, or calculations. A materialized view can also reduce the load on the database server by storing the query results in advance. A materialized view can be refreshed at regular intervals or on demand to reflect the changes in the underlying tables. References: [Materialized Views], [View Performance]

The best process model design for a case management application is to use a case record and a related action. This way, the process flow does not have to wait for the reviewer to complete the task, which can take up to 7 days. This reduces the memory impact of the process, as well as the risk of process timeouts or errors. The case record also provides a better user experience, as the reviewer can access the case details and the related action from one place. Therefore, the best answer is A.

References:

• Case Management
• Related Actions

Three methods that could be used to launch a test run for expression rule test cases when required are:

• A process model invoked via an API. A process model can be designed to run test cases for expression rules using the a!testRule() function or the Test Rule smart service. This process model can be exposed as a web API with an HTTP method such as POST or PUT, allowing external systems or applications to invoke it through an HTTP request.
• A process model exposed to users as an action. A process model can also be designed to run test cases for expression rules using the same function or smart service as above. This process model can be exposed to users as an action on an interface, such as a button or a link, allowing users to trigger it manually when needed.
• A SAIL interface embedded in a report. A SAIL interface can be created to run test cases for expression rules using the a!testRule() function. This interface can be embedded in a report, such as a grid or a chart, allowing users to view the test results interactively on an interface. References: Automated Testing for Expression Rules, a!testRule() Function, Test Rule Smart Service, Web APIs, SAIL Interfaces

 The question is about improving the memory usage of a process model. The following are three things you can do for this purpose:

• Reduce the number of process variables. This means using fewer or smaller process variables to store data in the process model, as each process variable consumes memory and resources. You should only use process variables that are necessary for the process logic or data flow, and avoid storing redundant or temporary data in process variables.
• Avoid storing large blocks of text in process variables. This means using other methods to store or handle large blocks of text, such as documents, constants, or expression rules, rather than process variables. This can reduce the memory usage of the process model, as large blocks of text can consume a lot of memory and resources, and affect the performance or stability of Appian.
• Pass data directly to activity nodes and smart services. This means using parameters or inputs to pass data to activity nodes and smart services, rather than process variables. This can reduce the memory usage of the process model, as parameters or inputs are only evaluated when needed, and do not persist in memory.

The following are not things you can do to improve the memory usage of a process model:

• Configure process variables to be parameters. This is not possible, as process variables and parameters are different types of data elements in a process model. Process variables are used to store data throughout the process model, while parameters are used to pass data to or from a subprocess or an expression rule.
• Split large nodes into many smaller nodes. This is not advisable, as it can increase the complexity and maintenance overhead of the process model, and may not reduce the memory usage significantly. You should only split nodes when it makes sense for the process logic or functionality, and not for the sake of reducing node size.

References:

• Process Variables
• Documents
• Constants
• Expression Rules
• Parameters