Practice Free ACD300 Exam Online Questions – Page 2

Question #11

You are planning a strategy around data volume testing for an Appian application that queries and writes to MySQL database.

You have administrator access to the Appian application and to the database.

What are two key considerations when designing a data volume testing strategy?

A . Data from previous tests needs to remain in the testing environment prior to loading prepopulated data
B . large datasets must be loaded via Applan processes
C . The amount of data that needs to be populated should be determined by the project sponsor and the stakeholders based on their estimation
D . Testing with the correct amount of data should be in the definition of done as part of each sprint.
E . Data model changes must wait until towards the end of the protect.

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Correct Answer: D E
D E

Explanation:

When designing a data volume testing strategy for an Appian application that queries and writes to MySQL database, you should consider two key considerations:

– Testing with the correct amount of data should be in the definition of done as part of each sprint. Data volume testing is a type of testing that verifies how well an application performs when handling large amounts of data. Data volume testing is important to ensure that the application meets the performance and quality requirements of the users and stakeholders. By including data volume testing in the definition of done as part of each sprint, you can ensure that each feature or functionality of your application is tested with realistic data volumes before being delivered to production. This way, you can identify and resolve any potential issues or bottlenecks early in the development cycle, and avoid any surprises or delays later on.

– Data model changes must wait until towards the end of the project. Data model changes are changes that affect the structure or schema of your database, such as adding, modifying, or deleting tables, columns, indexes, or constraints. Data model changes are risky and costly to make, especially when dealing with large amounts of data. Data model changes can affect the performance, functionality, or integrity of your application and database. Therefore, data model changes must wait until towards the end of the project, when you have finalized your requirements and design decisions, and have minimized your data volume testing efforts. By waiting until towards the end of the project to make data model changes, you can reduce the impact and complexity of those changes, and avoid any unnecessary rework or regression.

The other options are not as effective. Option A, data from previous tests needs to remain in the testing environment prior to loading prepopulated data, is not a key consideration for designing a data volume testing strategy, but rather a best practice for preparing your testing environment. Option B, large datasets must be loaded via Appian processes, is not a key consideration for designing a data volume testing strategy, but rather a technical implementation detail that may or may not be suitable for your application. Option C, the amount of data that needs to be populated should be determined by the project sponsor and the stakeholders based on their estimation, is not a key consideration for designing a data volume testing strategy, but rather an input or assumption that you need to validate before conducting your data volume testing.

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