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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”.

• 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.

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.

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 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”.