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Appian Certified Lead Developer Sample Questions:

1. You are planning a strategy around data volume testing for an Appian application that queries and writes to a 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) Testing with the correct amount of data should be in the definition of done as part of each sprint.
B) Large datasets must be loaded via Appian processes.
C) Data model changes must wait until towards the end of the project.
D) The amount of data that needs to be populated should be determined by the project sponsor and the stakeholders based on their estimation.
E) Data from previous tests needs to remain in the testing environment prior to loading prepopulated data.

2. For each scenario outlined, match the best tool to use to meet expectations. Each tool will be used once Note: To change your responses, you may deselected your response by clicking the blank space at the top of the selection list.

3. You add an index on the searched field of a MySQL table with many rows (>100k). The field would benefit greatly from the index in which three scenarios?

A) The field contains big integers, above and below 0.
B) The field contains long unstructured text such as a hash.
C) The field contains a structured JSON.
D) The field contains many datetimes, covering a large range.
E) The field contains a textual short business code.

4. On the latest Health Check report from your Cloud TEST environment utilizing a MongoDB add-on, you note the following findings:
Category: User Experience, Description: # of slow query rules, Risk: High Category: User Experience, Description: # of slow write to data store nodes, Risk: High Which three things might you do to address this, without consulting the business?

A) Reduce the batch size for database queues to 10.
B) Optimize the database execution using standard database performance troubleshooting methods and tools (such as query execution plans).
C) Reduce the size and complexity of the inputs. If you are passing in a list, consider whether the data model can be redesigned to pass single values instead.
D) Use smaller CDTs or limit the fields selected in a!queryEntity().
E) Optimize the database execution. Replace the view with a materialized view.

5. Review the following result of an explain statement:

Which two conclusions can you draw from this?

A) The join between the tables order_detail, order and customer needs to be tine-tuned due to indices.
B) The worst join is the one between the table order_detail and customer
C) The join between the tables 0rder_detail and product needs to be fine-tuned due to Indices
D) 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
E) The worst join is the one between the table order_detail and order.

Solutions: