[Mar 20, 2024] Pass Your ACD300 Dumps Free Latest Appian Practice Tests [Q21-Q37]

NEW QUESTION 21
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.

NEW QUESTION 22
As part of an upcoming release of an application, a new nullable field is added to a table that contains customer dataThe new field is used by a reportin the upcoming release, and is calculated using data from another table.
Which two actions should you consider when creating the script to add the new field?

Create a script thatadds the held and leaves it null.

Create a rollback script that removes the field.

Create a script that adds the field and then populate it

Create a rollback script that clears the data from the field

Add a view that joins the customer data to the data used in calculation

NEW QUESTION 23
You are the lead developer for an Appian project, in a backlog refinement meeting You are presented with the following user story.
As a restaurant customer. I need to be able to place my food order online to avoid waiting in line for take out.’ Which two functional acceptance criteria would you consider ‘good’?

The user will click Save, and the order information will be saved in the ORDER table and have audit history

The user will receive an email notification when their order is completed.

The system mutt handle up to 500 unique orders per day

The user cannot submit the form without filling out all required fields.

NEW QUESTION 24
What are two advantages of having High Availability (HA) for Applan Cloud applications?

An Applan Cloud HA instance is composed of multiple active nodes running in different availability zones in differentregions.

Data andtransactions are continuouslyreplicated across the active nodes to achieve redundancy and avoid single points offailure.

A typical Appian Cloud HA instance is composed of two active nodes.

In the event of a system failure, your Appian instance will fie restored and available to your users in less than 15 minutes.having lost no more than the last 1minute worth of data.

NEW QUESTION 25
You are running an inspection as a part of the first deployment process from TEST to PROD. You receive a notice that one of your objects will not deploy because it is dependent on an object from an application owned by a separate team.
What should be your next step?

Create your own object with the same code base, replace (he dependent object in the application. and deploy to PROO.

Halt the production deployment and contact the other team tor guidance on promoting the object to PROD

Check the dependencies of the necessary object Deploy w PROO if there are few dependencies and it is low risk

Push a functionally viable package to PROD without the dependencies, and plan the rest o! the deployment accordingly with the other team’s constraints

NEW QUESTION 26
Your Agile Scrum project requires you to manage two teams, with three developers per team. Both teams are to work on the same application In parallel.
How should the work be divided between the teams, avoiding issues caused by cross-dependency?

Have each team choose the stones they would like to work on based on personal preference

Group epics and stones by technical difficulty, and allocate one team the more challenging stories

Group epics and stones by feature, and allocate work between each team by feature.

Allocate stories to each team based on the cumulative years of experience of the team members.

NEW QUESTION 27
You have 5 applications on your Appian platform in production. Users are now beginning to use multiple applications across the platform, and the client wants to ensure a consistent user experience across all applications You notice that some applications use rich text some use section layouts, and others use box layouts. The result is that each application has a different color and size for the header.
What would you recommend to ensure consistency across the platform?

Create constants for text size and color, and update each section lo reference these values.

In the common application, create a rule that can be used across the platform for section headers, and update each application to reference this new rule

In the common application, create one rule for each application, and update each application to reference its respective rule

In each individual application, create a rule that can be used lot tot section headers, and update each application lo reference its respective rule

NEW QUESTION 28
You have an active development team (Team A) building enhancements for an application (App X’). and ate currently using the TEST environment for UAT.
A separate operations team (‘Team B) discovers a critical error in the Production instance of App X that they must remediate However. Team 6 does not have a hotfix stream for which to accomplish this The available environments are DEV. TEST, and PROD Which risk mitigation effort should both teams employ to ensure Team AS capital project is only minorly interrupted, and Team B s critical fix can be completed and deployed quickly to end users?

Team 8 must communicate to Team A which component will be addressed in the hotfix to avoid overlap of changes It overlap exists, the component must be versioned to its PROD state before being remediated and deployed, and then versioned back to its latest development state If overlap does not exist, the component may be remediated and deployed without any version changes

Team A must analyze their current codebase in OEV lo merge the hotfix changes into their latest enhancements. Team B is then requited to wait for the hotfix to follow regular deployment protocols from DEV to the PROO environment.

Team 8 must address changes in the TEST environment These changes can then be tested and deployed directly to PROD. Once the deployment is complete. Team B can then communicate their changes to Teams to ensure they are Incorporated as a part of the next release.

Team 8 must address the changes directly in PROD. As there is no hotfix stream, and OEV and TEST are being utilized for active development it is best to avoid a conflict of components. Once Team A has completed their enhancements work. Team 6 can update DEV and TEST accordingly.

NEW QUESTION 29
You are deciding the appropriate process model data management strategy.
For each requirement. match the appropriate strategies to implement. Each strategy will be used once.
Note: To change your responses, you may deselect your response by clicking the blank space at the top of the selection list.

Explanation

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.

NEW QUESTION 30
You are developing a case management application to manage support cases for a large set of sites. One of the tabs in this application s site Is a record grid of cases, along with Information about the site corresponding to that case. Users must be able to filter cases by priority level and status.
You decide to create a view as the source of your entity-backed record, which joins the separate case/site tables (as depicted in the following Image).

Which three column should be indexed?

site_id

status

name

modified_date

priority

case_id

NEW QUESTION 31
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?

The field contains a textual shot Business code.

The field contains long unstructured text such as a hash

The field contains many datetimes, covering a large range

The Add contains Dig integers, above and below 0.

The field contains a structured JSON.

Explanation
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”.

NEW QUESTION 32
You are taskedto build a large scale acquisition application for a prominent customer. The acquisition process tracks the time it takes is fulfill a purchase request with an award.
The customer has structured the contract so that there are multiple application dev teams.
How should you design for multiple processes and forms, while minimizing repeated code?

Create a Center of Excellence (CoE)

Create a common objects application.

Create a Scrum of Scrums sprint meeting for the team leads

Create duplicate processes and forms as needed

NEW QUESTION 33
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?

Data from previous tests needs to remain in the testing environment prior to loading prepopulated data

large datasets must be loaded via Applan processes

The amount of data that needs to be populated should be determined by the project sponsor and the stakeholders based on their estimation

Testing with the correct amount of data should be in the definition of done as part of each sprint.

Data model changes must wait until towards the end of the protect.

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.

NEW QUESTION 34
Your application contains a process model that Is scheduled to run daily at a certain time, which kicks off a user input task to a specified user on the 1ST time zone for morning data collection The time zone is set to the (default) pm!timezone.
In this situation, what does the pm!tinezone reflect?

The time zone of the server where Applan is intuited

The line zone of the user who most recently published the process model

The default time zone for the environment as specified in the Administration Console

The time zone of the user who is completing the input task.

NEW QUESTION 35
You need to export data using an out-of-the-box Appian smart service. Which two formats are available (or data generation?

CSV

XML

Excel

JSDN