Appian ACD300 Dumps

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”.

• 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.

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 complex Appian integration to call a RESTful API, you need to have some prerequisites, such as:

• Define the HTTP method that the integration will use. The HTTP method is the action that the integration will perform on the API, such as GET, POST, PUT, PATCH, or DELETE. The HTTP method determines how the data will be sent and received by the API, and what kind of response will be expected.
• Understand the content of the expected body, including each field type and their limits. The body is the data that the integration will send to the API, or receive from the API, depending on the HTTP method. The body can be in different formats, such as JSON, XML, or form data. You need to understand how to structure the body according to the API specification, and what kind of data types and values are allowed for each field.
• Understand the different error codes managed by the API and the process of error handling in Appian. The error codes are the status codes that indicate whether the API request was successful or not, and what kind of problem occurred if not. The error codes can range from 200 (OK) to 500 (Internal Server Error), and each code has a different meaning and implication. You need to understand how to handle different error codes in Appian, and how to display meaningful messages to the user or log them for debugging purposes.

The other two options are not prerequisites for designing the integration, but rather considerations for implementing it.

• Understand whether this integration will be used in an interface or in a process model. This is not a prerequisite, but rather a decision that you need to make based on your application requirements and design. You can use an integration either in an interface or in a process model, depending on where you need to call the API and how you want to handle the response. For example, if you need to update a case in real-time based on user input, you may want to use an integration in an interface. If you need to update a case periodically based on a schedule or an event, you may want to use an integration in a process model.
• Understand the business rules to be applied to ensure the business logic of the data. This is not a prerequisite, but rather a part of your application logic that you need to implement after designing the integration. You need to apply business rules to validate, transform, or enrich the data that you send or receive from the API, according to your business requirements and logic. For example, you may need to check if the case status is valid before updating it in the legacy system, or you may need to add some additional information to the case data before displaying it in Appian.

The two formats that are available for data generation using an out-of-the-box Appian smart service are:

• A. CSV. This is a comma-separated values format that can be used to export data in a tabular form, such as records, reports, or grids. CSV files can be easily opened and manipulated by spreadsheet applications such as Excel or Google Sheets.
• C. Excel. This is a format that can be used to export data in a spreadsheet form, with multiple worksheets, formatting, formulas, charts, and other features. Excel files can be opened by Excel or other compatible applications.

The other options are incorrect for the following reasons:

• B. XML. This is a format that can be used to export data in a hierarchical form, using tags and attributes to define the structure and content of the data. XML files can be opened by text editors or XML parsers, but they are not supported by the out-of-the-box Appian smart service for data generation.
• D. JSON. This is a format that can be used to export data in a structured form, using objects and arrays to represent the data. JSON files can be opened by text editors or JSON parsers, but they are not supported by the out-of-the-box Appian smart service for data generation. Verified References: Appian Documentation, section “Write to Data Store Entity” and “Write to Multiple Data Store Entities”.

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.

To build a large scale acquisition application for a prominent customer, you should design for multiple processes and forms, while minimizing repeated code. One way to do this is to create a common objects application, which is a shared application that contains reusable components, such as rules, constants, interfaces, integrations, or data types, that can be used by multiple applications. This way, you can avoid duplication and inconsistency of code, and make it easier to maintain and update your applications. You can also use the common objects application to define common standards and best practices for your application development teams, such as naming conventions, coding styles, or documentation guidelines. Verified References: [Appian Best Practices], [Appian Design Guidance]

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 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”.