Appian ACD300 Exam With Confidence Using Practice Dumps

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”.

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]

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”.