Quiz Appian Certified Lead Developer

As an Appian Lead Developer, analyzing Engine Performance Logs to address performance issues in a
Production application requires understanding Appian’s architecture and the specific metrics
described. The scenario indicates a high “Work Queue - Java Work Queue Size,” which reflects a
backlog of tasks in the Java Work Queue (managed by Appian engines), and delays in unattended
process activities (e.g., timer events, email notifications). These symptoms suggest the Appian
engines are overloaded, despite the client increasing CPU cores. Let’s evaluate each option:
A . Add more engine replicas:
This is the correct recommendation. In Appian, engine replicas (part of the Appian Engine cluster)
handle process execution, including unattended tasks like timers and notifications. A high Java Work
Queue Size indicates the engines are overwhelmed by concurrent activity during business hours,
causing delays. Adding more engine replicas distributes the workload, reducing queue size and
improving performance for both user-driven and unattended tasks. Appian’s documentation
recommends scaling engine replicas to handle sustained loads, especially in Production with high
concurrency. Since CPU cores were already increased (likely on application servers), the bottleneck is
likely the engine capacity, not the servers.
B . Optimize slow-performing user interfaces:
While optimizing user interfaces (e.g., SAIL forms, reports) can improve user experience, the
scenario highlights delays in unattended activities (timers, emails), not UI performance. The Java
Work Queue Size issue points to engine-level processing, not UI rendering, so this doesn’t address
the root cause. Appian’s performance tuning guidelines prioritize engine scaling for queue-related
issues, making this a secondary concern.
C . Add more application servers:
Application servers handle web traffic (e.g., SAIL interfaces, API calls), not process execution or
unattended tasks managed by engines. Increasing application servers would help with UI
concurrency but wouldn’t reduce the Java Work Queue Size or speed up timer/email processing, as
these are engine responsibilities. Since the client already increased CPU cores (likely on application
servers), this is redundant and unrelated to the issue.
D . Add execution and analytics shards:
Execution shards (for process data) and analytics shards (for reporting) are part of Appian’s data
fabric for scalability, but they don’t directly address engine workload or Java Work Queue Size.
Shards optimize data storage and query performance, not real-time process execution. The logs
indicate an engine bottleneck, not a data storage issue, so this isn’t relevant. Appian’s
documentation confirms shards are for long-term scaling, not immediate performance fixes.
Conclusion: Adding more engine replicas (A) is the next recommendation. It directly resolves the high
Java Work Queue Size and delays in unattended tasks, aligning with Appian’s architecture for
handling concurrent loads in Production. This requires collaboration with system administrators to
configure additional replicas in the Appian cluster.
Reference:
Appian Documentation: "Engine Performance Monitoring" (Java Work Queue and Scaling Replicas).
Appian Lead Developer Certification: Performance Optimization Module (Engine Scaling Strategies).
Appian Best Practices: "Managing Production Performance" (Work Queue Analysis).

Indexing columns can improve the performance of queries that use those columns in filters, joins, or
order by clauses. In this case, the columns that should be indexed are site_id, status, and priority,
because they are used for filtering or joining the tables. Site_id is used to join the case and site
tables, so indexing it will speed up the join operation. Status and priority are used to filter the cases
by the user’s input, so indexing them will reduce the number of rows that need to be scanned.
Name, modified_date, and case_id do not need to be indexed, because they are not used for filtering
or joining. Name and modified_date are only used for displaying information in the record grid, and
case_id is only used as a unique identifier for each record. Verified Reference: Appian Records
Tutorial, Appian Best Practices
As an Appian Lead Developer, optimizing a database view for an entity-backed record grid requires
indexing columns frequently used in queries, particularly for filtering and joining. The scenario
involves a record grid displaying cases with site information, filtered by “priority level” and “status,”
and joined via the site_id foreign key. The image shows two tables (site and case) with a relationship
via site_id. Let’s evaluate each column based on Appian’s performance best practices and query
patterns:
A . site_id:
This is a primary key in the site table and a foreign key in the case table, used for joining the tables in
the view. Indexing site_id in the case table (and ensuring it’s indexed in site as a PK) optimizes JOIN
operations, reducing query execution time for the record grid. Appian’s documentation recommends
indexing foreign keys in large datasets to improve query performance, especially for entity-backed
records. This is critical for the join and must be included.
B . status:
Users filter cases by “status” (a varchar column in the case table). Indexing status speeds up filtering
queries (e.g., WHERE status = 'Open') in the record grid, particularly with large datasets. Appian
emphasizes indexing columns used in WHERE clauses or filters to enhance performance, making this
a key column for optimization. Since status is a common filter, it’s essential.
C . name:
This is a varchar column in the site table, likely used for display (e.g., site name in the grid). However,
the scenario doesn’t mention filtering or sorting by name, and it’s not part of the join or required
filters. Indexing name could improve searches if used, but it’s not a priority given the focus on
priority and status filters. Appian advises indexing only frequently queried or filtered columns to
avoid unnecessary overhead, so this isn’t necessary here.
D . modified_date:
This is a date column in the case table, tracking when cases were last updated. While useful for
sorting or historical queries, the scenario doesn’t specify filtering or sorting by modified_date in the
record grid. Indexing it could help if used, but it’s not critical for the current requirements. Appian’s
performance guidelines prioritize indexing columns in active filters, making this lower priority than
site_id, status, and priority.
E . priority:
Users filter cases by “priority level” (a varchar column in the case table). Indexing priority optimizes
filtering queries (e.g., WHERE priority = 'High') in the record grid, similar to status. Appian’s
documentation highlights indexing columns used in WHERE clauses for entity-backed records,
especially with large datasets. Since priority is a specified filter, it’s essential to include.
F . case_id:
This is the primary key in the case table, already indexed by default (as PKs are automatically indexed
in most databases). Indexing it again is redundant and unnecessary, as Appian’s Data Store
configuration relies on PKs for unique identification but doesn’t require additional indexing for
performance in this context. The focus is on join and filter columns, not the PK itself.
Conclusion: The three columns to index are A (site_id), B (status), and E (priority). These optimize the
JOIN (site_id) and filter performance (status, priority) for the record grid, aligning with Appian’s
recommendations for entity-backed records and large datasets. Indexing these columns ensures
efficient querying for user filters, critical for the application’s performance.
Reference:
Appian Documentation: "Performance Best Practices for Data Stores" (Indexing Strategies).
Appian Lead Developer Certification: Data Management Module (Optimizing Entity-Backed Records).
Appian Best Practices: "Working with Large Data Volumes" (Indexing for Query Performance).

Comprehensive and Detailed In-Depth As an Appian Lead Developer, managing a deployment from TEST to PROD requires careful handling
of dependencies, especially when objects from another team’s application are involved. The scenario
describes a dependency issue during deployment, signaling a need for collaboration and governance.
Let’s evaluate each option:
A . Create your own object with the same code base, replace the dependent object in the
application, and deploy to PROD:
This approach involves duplicating the object, which introduces redundancy, maintenance risks, and
potential version control issues. It violates Appian’s governance principles, as objects should be
owned and managed by their respective teams to ensure consistency and avoid conflicts. Appian’s
deployment best practices discourage duplicating objects unless absolutely necessary, making this an
unsustainable and risky solution.
B . Halt the production deployment and contact the other team for guidance on promoting the object
to PROD:
This is the correct step. When an object from another application (owned by a separate team) is a
dependency, Appian’s deployment process requires coordination to ensure both applications’
objects are deployed in sync. Halting the deployment prevents partial deployments that could break
functionality, and contacting the other team aligns with Appian’s collaboration and governance
guidelines. The other team can provide the necessary object version, adjust their deployment
timeline, or resolve the dependency, ensuring a stable PROD environment.
C . Check the dependencies of the necessary object. Deploy to PROD if there are few dependencies
and it is low risk:
This approach risks deploying an incomplete or unstable application if the dependency isn’t fully
resolved. Even with “few dependencies” and “low risk,” deploying without the other team’s object
could lead to runtime errors or broken functionality in PROD. Appian’s documentation emphasizes
thorough dependency management during deployment, requiring all objects (including those from
other applications) to be promoted together, making this risky and not recommended.
D . Push a functionally viable package to PROD without the dependencies, and plan the rest of the
deployment accordingly with the other team’s constraints:
Deploying without dependencies creates an incomplete solution, potentially leaving the application
non-functional or unstable in PROD. Appian’s deployment process ensures all dependencies are
included to maintain application integrity, and partial deployments are discouraged unless explicitly
planned (e.g., phased rollouts). This option delays resolution and increases risk, contradicting
Appian’s best practices for Production stability.
Conclusion: Halting the production deployment and contacting the other team for guidance (B) is the
next step. It ensures proper collaboration, aligns with Appian’s governance model, and prevents
deployment errors, providing a safe and effective resolution.
Reference:
Appian Documentation: "Deployment Best Practices" (Managing Dependencies Across Applications).
Appian Lead Developer Certification: Application Management Module (Cross-Team Collaboration).
Appian Best Practices: "Handling Production Deployments" (Dependency Resolution).

Comprehensive and Detailed In-Depth As an Appian Lead Developer, designing a complex integration to a RESTful API for updating a case in
a legacy system requires a structured approach to ensure reliability, performance, and alignment
with business needs. The integration involves sending a JSON payload (implied by the context) and
handling responses, so the focus is on technical and functional prerequisites. Let’s evaluate each
option:
A . Define the HTTP method that the integration will use:
This is a primary prerequisite. RESTful APIs use HTTP methods (e.g., POST, PUT, GET) to define the
operation—here, updating a case likely requires PUT or POST. Appian’s Connected System and
Integration objects require specifying the method to configure the HTTP request correctly.
Understanding the API’s method ensures the integration aligns with its design, making this essential
for design. Appian’s documentation emphasizes choosing the correct HTTP method as a foundational
step.
B . Understand the content of the expected body, including each field type and their limits:
This is also critical. The JSON payload for updating a case includes fields (e.g., text, dates, numbers),
and the API expects a specific structure with field types (e.g., string, integer) and limits (e.g., max
length, size constraints). In Appian, the Integration object requires a dictionary or CDT to construct
the body, and mismatches (e.g., wrong types, exceeding limits) cause errors (e.g., 400 Bad Request).
Appian’s best practices mandate understanding the API schema to ensure data compatibility, making
this a key prerequisite.
C . Understand whether this integration will be used in an interface or in a process model:
While knowing the context (interface vs. process model) is useful for design (e.g., synchronous vs.
asynchronous calls), it’s not a prerequisite for the integration itself—it’s a usage consideration.
Appian supports integrations in both contexts, and the integration’s design (e.g., HTTP method,
body) remains the same. This is secondary to technical API details, so it’s not among the top three
prerequisites.
D . Understand the different error codes managed by the API and the process of error handling in
Appian:
This is essential. RESTful APIs return HTTP status codes (e.g., 200 OK, 400 Bad Request, 500 Internal
Server Error), and the customer’s API likely documents these for failure scenarios (e.g., invalid data,
server issues). Appian’s Integration objects can handle errors via error mappings or process models,
and understanding these codes ensures robust error handling (e.g., retry logic, user notifications).
Appian’s documentation stresses error handling as a core design element for reliable integrations,
making this a primary prerequisite.
E . Understand the business rules to be applied to ensure the business logic of the data:
While business rules (e.g., validating case data before sending) are important for the overall
application, they aren’t a prerequisite for designing the integration itself—they’re part of the
application logic (e.g., process model or interface). The integration focuses on technical interaction
with the API, not business validation, which can be handled separately in Appian. This is a secondary
concern, not a core design requirement for the integration.
Conclusion: The three prerequisites are A (define the HTTP method), B (understand the body content
and limits), and D (understand error codes and handling). These ensure the integration is technically
sound, compatible with the API, and resilient to errors—critical for a complex RESTful API integration
in Appian.
Reference:
Appian Documentation: "Designing REST Integrations" (HTTP Methods, Request Body, Error
Handling).
Appian Lead Developer Certification: Integration Module (Prerequisites for Complex Integrations).
Appian Best Practices: "Building Reliable API Integrations" (Payload and Error Management).
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.

Comprehensive and Detailed In-Depth As an Appian Lead Developer, ensuring a consistent user experience across multiple applications on
the Appian platform involves centralizing reusable components and adhering to Appian’s design
governance principles. The client’s concern about inconsistent headers (e.g., different colors, sizes,
layouts) across applications using rich text, section layouts, and box layouts requires a scalable,
maintainable solution. Let’s evaluate each option:
A . Create constants for text size and color, and update each section to reference these values:
Using constants (e.g., cons!TEXT_SIZE and cons!HEADER_COLOR) is a good practice for managing
values, but it doesn’t address layout consistency (e.g., rich text vs. section layouts vs. box layouts).
Constants alone can’t enforce uniform header design across applications, as they don’t encapsulate
layout logic (e.g., a!sectionLayout() vs. a!richTextDisplayField()). This approach would require manual
updates to each application’s components, increasing maintenance overhead and still risking
inconsistency. Appian’s documentation recommends using rules for reusable UI components, not just
constants, making this insufficient.
B . 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:
This is the best recommendation. Appian supports a “common application” (often called a shared or
utility application) to store reusable objects like expression rules, which can define consistent header
designs (e.g., rule!CommonHeader(size: "LARGE", color: "PRIMARY")). By creating a single rule for
headers and referencing it across all 5 applications, you ensure uniformity in layout, color, and size
(e.g., using a!sectionLayout() or a!boxLayout() consistently). Appian’s design best practices
emphasize centralizing UI components in a common application to reduce duplication, enforce
standards, and simplify maintenance—perfect for achieving a consistent user experience.
C . In the common application, create one rule for each application, and update each application to
reference its respective rule:
This approach creates separate header rules for each application (e.g., rule!App1Header,
rule!App2Header), which contradicts the goal of consistency. While housed in the common
application, it introduces variability (e.g., different colors or sizes per rule), defeating the purpose.
Appian’s governance guidelines advocate for a single, shared rule to maintain uniformity, making
this less efficient and unnecessary.
D . In each individual application, create a rule that can be used for section headers, and update each
application to reference its respective rule:
Creating separate rules in each application (e.g., rule!App1Header in App 1, rule!App2Header in App
2) leads to duplication and inconsistency, as each rule could differ in design. This approach increases
maintenance effort and risks diverging styles, violating the client’s requirement for a “consistent user
experience.” Appian’s best practices discourage duplicating UI logic, favoring centralized rules in a
common application instead.
Conclusion: Creating a rule in the common application for section headers and referencing it across
the platform (B) ensures consistency in header design (color, size, layout) while minimizing
duplication and maintenance. This leverages Appian’s application architecture for shared objects,
aligning with Lead Developer standards for UI governance.
Reference:
Appian Documentation: "Designing for Consistency Across Applications" (Common Application Best
Practices).
Appian Lead Developer Certification: UI Design Module (Reusable Components and Rules).
Appian Best Practices: "Maintaining User Experience Consistency" (Centralized UI Rules).
The best way to ensure consistency across the platform is to create a rule that can be used across the
platform for section headers. This rule can be created in the common application, and then each
application can be updated to reference this rule. This will ensure that all of the applications use the
same color and size for the header, which will provide a consistent user experience.
The other options are not as effective. Option A, creating constants for text size and color, and
updating each section to reference these values, would require updating each section in each
application. This would be a lot of work, and it would be easy to make mistakes. Option C, creating
one rule for each application, would also require updating each application. This would be less work
than option A, but it would still be a lot of work, and it would be easy to make mistakes. Option D,
creating a rule in each individual application, would not ensure consistency across the platform. Each
application would have its own rule, and the rules could be different. This would not provide a
consistent user experience.
Best Practices:
When designing a platform, it is important to consider the user experience. A consistent user
experience will make it easier for users to learn and use the platform.
When creating rules, it is important to use them consistently across the platform. This will ensure
that the platform has a consistent look and feel.
When updating the platform, it is important to test the changes to ensure that they do not break the
user experience.

Comprehensive and Detailed In-Depth As an Appian Lead Developer, designing a large-scale acquisition application with multiple
development teams requires a strategy to manage processes, forms, and code reuse effectively. The
goal is to minimize repeated code (e.g., duplicate interfaces, process models) while ensuring
scalability and maintainability across teams. Let’s evaluate each option:
A . Create a Center of Excellence (CoE):
A Center of Excellence is an organizational structure or team focused on standardizing practices,
training, and governance across projects. While beneficial for long-term consistency, it doesn’t
directly address the technical design of minimizing repeated code for processes and forms. It’s a
strategic initiative, not a design solution, and doesn’t solve the immediate need for code reuse.
Appian’s documentation mentions CoEs for governance but not as a primary design approach,
making this less relevant here.
B . Create a common objects application:
This is the best recommendation. In Appian, a “common objects application” (or shared application)
is used to store reusable components like expression rules, interfaces, process models, constants,
and data types (e.g., CDTs). For a large-scale acquisition application with multiple teams, centralizing
shared objects (e.g., rule!CommonForm, pm!CommonProcess) ensures consistency, reduces
duplication, and simplifies maintenance. Teams can reference these objects in their applications,
adhering to Appian’s design best practices for scalability. This approach minimizes repeated code
while allowing team-specific customizations, aligning with Lead Developer standards for large
projects.
C . Create a Scrum of Scrums sprint meeting for the team leads:
A Scrum of Scrums meeting is a coordination mechanism for Agile teams, focusing on aligning sprint
goals and resolving cross-team dependencies. While useful for collaboration, it doesn’t address the
technical design of minimizing repeated code—it’s a process, not a solution for code reuse. Appian’s
Agile methodologies support such meetings, but they don’t directly reduce duplication in processes
and forms, making this less applicable.
D . Create duplicate processes and forms as needed:
Duplicating processes and forms (e.g., copying interface!PurchaseForm for each team) leads to
redundancy, increased maintenance effort, and potential inconsistencies (e.g., divergent logic). This
contradicts the goal of minimizing repeated code and violates Appian’s design principles for
reusability and efficiency. Appian’s documentation strongly discourages duplication, favoring shared
objects instead, making this the least effective option.
Conclusion: Creating a common objects application (B) is the recommended design. It centralizes
reusable processes, forms, and other components, minimizing code duplication across teams while
ensuring consistency and scalability for the large-scale acquisition application. This leverages
Appian’s application architecture for shared resources, aligning with Lead Developer best practices
for multi-team projects.
Reference:
Appian Documentation: "Designing Large-Scale Applications" (Common Application for Reusable
Objects).
Appian Lead Developer Certification: Application Design Module (Minimizing Code Duplication).
Appian Best Practices: "Managing Multi-Team Development" (Shared Objects Strategy).
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 Reference: [Appian Best Practices], [Appian
Design Guidance]

Comprehensive and Detailed In-Depth As an Appian Lead Developer, managing concurrent development and operations (hotfix) activities
across limited environments (DEV, TEST, PROD) requires minimizing disruption to Team A’s
enhancements while ensuring Team B’s critical fix reaches PROD quickly. The scenario highlights no
hotfix stream, active UAT in TEST, and a critical PROD issue, necessitating a strategic approach. Let’s
evaluate each option:
A . Team B must communicate to Team A which component will be addressed in the hotfix to avoid
overlap of changes. If 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:
This is the best approach. It ensures collaboration between teams to prevent conflicts, leveraging
Appian’s version control (e.g., object versioning in Appian Designer). Team B identifies the critical
component, checks for overlap with Team A’s work, and uses versioning to isolate changes. If no
overlap exists, the hotfix deploys directly; if overlap occurs, versioning preserves Team A’s work,
allowing the hotfix to deploy and then reverting the component for Team A’s continuation. This
minimizes interruption to Team A’s UAT, enables rapid PROD deployment, and aligns with Appian’s
change management best practices.
B . Team A must analyze their current codebase in DEV to merge the hotfix changes into their latest
enhancements. Team B is then required to wait for the hotfix to follow regular deployment protocols
from DEV to the PROD environment:
This delays Team B’s critical fix, as regular deployment (DEV → TEST → PROD) could take weeks,
violating the need for “quick deployment to end users.” It also risks introducing Team A’s untested
enhancements into the hotfix, potentially destabilizing PROD. Appian’s documentation discourages
mixing development and hotfix workflows, favoring isolated changes for urgent fixes, making this
inefficient and risky.
C . Team B 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 Team A to ensure they are incorporated as part of the next release:
Using TEST for hotfix development disrupts Team A’s UAT, as TEST is already in use for their
enhancements. Direct deployment from TEST to PROD skips DEV validation, increasing risk, and
doesn’t address overlap with Team A’s work. Appian’s deployment guidelines emphasize separate
streams (e.g., hotfix streams) to avoid such conflicts, making this disruptive and unsafe.
D . Team B must address the changes directly in PROD. As there is no hotfix stream, and DEV 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 B can update DEV and TEST accordingly:
Making changes directly in PROD is highly discouraged in Appian due to lack of testing, version
control, and rollback capabilities, risking further instability. This violates Appian’s Production
governance and security policies, and delays Team B’s updates until Team A finishes, contradicting
the need for a “quick deployment.” Appian’s best practices mandate using lower environments for
changes, ruling this out.
Conclusion: Team B communicating with Team A, versioning components if needed, and deploying
the hotfix (A) is the risk mitigation effort. It ensures minimal interruption to Team A’s work, rapid
PROD deployment for Team B’s fix, and leverages Appian’s versioning for safe, controlled changes—
aligning with Lead Developer standards for multi-team coordination.
Reference:
Appian Documentation: "Managing Production Hotfixes" (Versioning and Change Management).
Appian Lead Developer Certification: Application Management Module (Hotfix Strategies).
Appian Best Practices: "Concurrent Development and Operations" (Minimizing Risk in Limited
Environments).

Comprehensive and Detailed In-Depth As an Appian Lead Developer, adding a new nullable field to a database table for an upcoming
release requires careful planning to ensure data integrity, report functionality, and rollback capability.
The field is used in a report and calculated from another table, so the script must handle both
deployment and potential reversibility. Let’s evaluate each option:
A . Create a script that adds the field and leaves it null:
Adding a nullable field and leaving it null is technically feasible (e.g., using ALTER TABLE ADD
COLUMN in SQL), but it doesn’t address the report’s need for calculated data. Since the field is used
in a report and calculated from another table, leaving it null risks incomplete or incorrect reporting
until populated, delaying functionality. Appian’s data management best practices recommend
populating data during deployment for immediate usability, making this insufficient as a standalone
action.
B . Create a rollback script that removes the field:
This is a critical action. In Appian, database changes (e.g., adding a field) must be reversible in case of
deployment failure or rollback needs (e.g., during testing or PROD issues). A rollback script that
removes the field (e.g., ALTER TABLE DROP COLUMN) ensures the database can return to its original
state, minimizing risk. Appian’s deployment guidelines emphasize rollback scripts for schema
changes, making this essential for safe releases.
C . Create a script that adds the field and then populates it:
This is also essential. Since the field is nullable, calculated from another table, and used in a report,
populating it during deployment ensures immediate functionality. The script can use SQL (e.g.,
UPDATE table SET new_field = (SELECT calculated_value FROM other_table WHERE condition)) to
populate data, aligning with Appian’s data fabric principles for maintaining data consistency.
Appian’s documentation recommends populating new fields during deployment for reporting
accuracy, making this a key action.
D . Create a rollback script that clears the data from the field:
Clearing data (e.g., UPDATE table SET new_field = NULL) is less effective than removing the field
entirely. If the deployment fails, the field’s existence with null values could confuse reports or
processes, requiring additional cleanup. Appian’s rollback strategies favor reverting schema changes
completely (removing the field) rather than leaving it with nulls, making this less reliable and
unnecessary compared to B.
E . Add a view that joins the customer data to the data used in calculation:
Creating a view (e.g., CREATE VIEW customer_report AS SELECT ... FROM customer_table JOIN
other_table ON ...) is useful for reporting but isn’t a prerequisite for adding the field. The scenario
focuses on the field addition and population, not reporting structure. While a view could optimize
queries, it’s a secondary step, not a primary action for the script itself. Appian’s data modeling best
practices suggest views as post-deployment optimizations, not script requirements.
Conclusion: The two actions to consider are B (create a rollback script that removes the field) and C
(create a script that adds the field and then populates it). These ensure the field is added with data
for immediate report usability and provide a safe rollback option, aligning with Appian’s deployment
and data management standards for schema changes.
Reference:
Appian Documentation: "Database Schema Changes" (Adding Fields and Rollback Scripts).
Appian Lead Developer Certification: Data Management Module (Schema Deployment Strategies).
Appian Best Practices: "Managing Data Changes in Production" (Populating and Rolling Back Fields).