ACD301 VALID TEST ONLINE | ACD301 PREMIUM EXAM

ACD301 Valid Test Online | ACD301 Premium Exam

ACD301 Valid Test Online | ACD301 Premium Exam

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Our Appian Lead Developer (ACD301) practice exam simulator mirrors the Appian Lead Developer (ACD301) exam experience, so you know what to anticipate on Appian Lead Developer (ACD301) certification exam day. Our Appian Lead Developer (ACD301) practice test software features various question styles and levels, so you can customize your Appian ACD301 exam questions preparation to meet your needs.

Appian Lead Developer Sample Questions (Q35-Q40):

NEW QUESTION # 35
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?

  • A. case_id
  • B. name
  • C. priority
  • D. modified_date
  • E. site_id
  • F. status

Answer: C,E,F

Explanation:
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 References: 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.
References:
* 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).


NEW QUESTION # 36
You have an active development team (Team A) building enhancements for an application (App X) and are currently using the TEST environment for User Acceptance Testing (UAT).
A separate operations team (Team B) discovers a critical error in the Production instance of App X that they must remediate. However, Team B 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 A's capital project is only minorly interrupted, and Team B's critical fix can be completed and deployed quickly to end users?

  • A. 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.
  • B. 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.
  • C. 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.
  • D. 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.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation: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 nohotfix 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.
References:
* 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).


NEW QUESTION # 37
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 takeout." Which two functional acceptance criteria would you consider 'good'?

  • A. The user cannot submit the form without filling out all required fields.
  • B. The system must handle up to 500 unique orders per day.
  • C. The user will receive an email notification when their order is completed.
  • D. The user will click Save, and the order information will be saved in the ORDER table and have audit history.

Answer: A,D

Explanation:
Comprehensive and Detailed In-Depth Explanation:As an Appian Lead Developer, defining "good" functional acceptance criteria for a user story requires ensuring they are specific, testable, and directly tied to the user's need (placing an online food order to avoid waiting in line). Good criteria focus on functionality, usability, and reliability, aligning with Appian's Agile and design best practices. Let's evaluate each option:
* A. The user will click Save, and the order information will be saved in the ORDER table and have audit history:This is a "good" criterion. It directly validates the core functionality of the user story-placing an order online. Saving order data in the ORDER table (likely via a process model or Data Store Entity) ensures persistence, and audit history (e.g., using Appian's audit logs or database triggers) tracks changes, supporting traceability and compliance. This is specific, testable (e.g., verify data in the table and logs), and essential for the user's goal, aligning with Appian's data management and user experience guidelines.
* B. The user will receive an email notification when their order is completed:While useful, this is a
"nice-to-have" enhancement, not a core requirement of the user story. The story focuses on placing an order online to avoid waiting, not on completion notifications. Email notifications add value but aren't essential for validating the primary functionality. Appian's user story best practices prioritize criteria tied to the main user need, making this secondary and not "good" in this context.
* C. The system must handle up to 500 unique orders per day:This is a non-functional requirement (performance/scalability), not a functional acceptance criterion. It describes system capacity, not specific user behavior or functionality. While important for design, it's not directly testable for the user story's outcome (placing an order) and isn't tied to the user's experience. Appian's Agile methodologies separate functional and non-functional requirements, making this less relevant as a
"good" criterion here.
* D. The user cannot submit the form without filling out all required fields:This is a "good" criterion. It ensures data integrity and usability by preventing incomplete orders, directly supporting the user's ability to place a valid online order. In Appian, this can be implemented using form validation (e.g., required attributes in SAIL interfaces or process model validations), making it specific, testable (e.g., verify form submission fails with missing fields), and critical for a reliable user experience. This aligns with Appian's UI design and user story validation standards.
Conclusion: The two "good" functional acceptance criteria are A (order saved with audit history) and D (required fields enforced). These directly validate the user story's functionality (placing a valid order online), are testable, and ensure a reliable, user-friendly experience-aligning with Appian's Agile and design best practices for user stories.
References:
* Appian Documentation: "Writing Effective User Stories and Acceptance Criteria" (Functional Requirements).
* Appian Lead Developer Certification: Agile Development Module (Acceptance Criteria Best Practices).
* Appian Best Practices: "Designing User Interfaces in Appian" (Form Validation and Data Persistence).


NEW QUESTION # 38
You are asked to design a case management system for a client. In addition to storing some basic metadata about a case, one of the client's requirements is the ability for users to update a case. The client would like any user in their organization of 500 people to be able to make these updates. The users are all based in the company's headquarters, and there will be frequent cases where users are attempting to edit the same case.
The client wants to ensure no information is lost when these edits occur and does not want the solution to burden their process administrators with any additional effort. Which data locking approach should you recommend?

  • A. Allow edits without locking the case CDI.
  • B. Add an @Version annotation to the case CDT to manage the locking.
  • C. Design a process report and query to determine who opened the edit form first.
  • D. Use the database to implement low-level pessimistic locking.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation:The requirement involves a case management system where 500 users may simultaneously edit the same case, with a need to prevent data loss and minimize administrative overhead. Appian's data management and concurrency control strategies are critical here, especially when integrating with an underlying database.
* Option C (Add an @Version annotation to the case CDT to manage the locking):This is the recommended approach. In Appian, the @Version annotation on a Custom Data Type (CDT) enables optimistic locking, a lightweight concurrency control mechanism. When a user updates a case, Appian checks the version number of the CDT instance. If another user hasmodified it in the meantime, the update fails, prompting the user to refresh and reapply changes. This prevents data loss without requiring manual intervention by process administrators. Appian's Data Design Guide recommends
@Version for scenarios with high concurrency (e.g., 500 users) and frequent edits, as it leverages the database's native versioning (e.g., in MySQL or PostgreSQL) and integrates seamlessly with Appian's process models. This aligns with the client's no-burden requirement.
* Option A (Allow edits without locking the case CDI):This is risky. Without locking, simultaneous edits could overwrite each other, leading to data loss-a direct violation of the client's requirement.
Appian does not recommend this for collaborative environments.
* Option B (Use the database to implement low-level pessimistic locking):Pessimistic locking (e.g., using SELECT ... FOR UPDATE in MySQL) locks the record during the edit process, preventing other users from modifying it until the lock is released. While effective, it can lead to deadlocks or performance bottlenecks with 500 users, especially if edits are frequent. Additionally, managing this at the database level requires custom SQL and increases administrative effort (e.g., monitoring locks), which the client wants to avoid. Appian prefers higher-level solutions like @Version over low-level database locking.
* Option D (Design a process report and query to determine who opened the edit form first):This is impractical and inefficient. Building a custom report and query to track form opens adds complexity and administrative overhead. It doesn't inherently prevent data loss and relies on manual resolution, conflicting with the client's requirements.
The @Version annotation provides a robust, Appian-native solution that balances concurrency, data integrity, and ease of maintenance, making it the best fit.
References:Appian Documentation - Data Types and Concurrency Control, Appian Data Design Guide - Optimistic Locking with @Version, Appian Lead Developer Training - Case Management Design.


NEW QUESTION # 39
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.

Answer:

Explanation:

Explanation:
* As a user, if I update an object of type "Customer", the value of the given field should be displayed on the "Company" Record List. # Database Complex View
* As a user, if I update an object of type "Customer", a simple data transformation needs to be performed on related objects of the same type (namely, all the customers related to the same company). # Database Trigger
* As a user, if I update an object of type "Customer", some complex data transformations need to be performed on related objects of type "Customer", "Company", and "Contract". # Database Stored Procedure
* As a user, if I update an object of type "Customer", some simple data transformations need to be performed on related objects of type "Company", "Address", and "Contract". # Write to Data Store Entity smart service Comprehensive and Detailed In-Depth Explanation:Appian integrates with external databases to handle data updates and transformations, offering various tools depending on the complexity and context of the task.
The scenarios involve updating a "Customer" object and triggering actions on related data, requiring careful selection of the best tool. Appian's Data Integration and Database Management documentation guides these decisions.
* As a user, if I update an object of type "Customer", the value of the given field should be displayed on the "Company" Record List # Database Complex View:This scenario requires displaying updated customer data on a "Company" Record List, implying a read-only operation to join or aggregate data across tables. A Database Complex View (e.g., a SQL view combining "Customer" and "Company" tables) is ideal for this. Appian supports complex views to predefine queries that can be used in Record Lists, ensuring the updated field value is reflected without additional processing. This tool is best for read operations and does not involve write logic.
* As a user, if I update an object of type "Customer", a simple data transformation needs to be performed on related objects of the same type (namely, all the customers related to the same company) # Database Trigger:This involves a simple transformation (e.g., updating a flag or counter) on related "Customer" records after an update. A Database Trigger, executed automatically on the database side when a "Customer" record is modified, is the best fit. It can perform lightweight SQL updates on related records (e.g., via a company ID join) without Appian process overhead. Appian recommends triggers for simple, database-level automation, especially when transformations are confined to the same table type.
* As a user, if I update an object of type "Customer", some complex data transformations need to be performed on related objects of type "Customer", "Company", and "Contract" # Database Stored Procedure:This scenario involves complex transformations across multiple related object types, suggesting multi-step logic (e.g., recalculating totals or updating multiple tables). A Database Stored Procedure allows you to encapsulate this logic in SQL, callable from Appian, offering flexibility for complex operations. Appian supports stored procedures for scenarios requiring transactional integrity and intricate data manipulation across tables, making it the best choice here.
* As a user, if I update an object of type "Customer", some simple data transformations need to be performed on related objects of type "Company", "Address", and "Contract" # Write to Data Store Entity smart service:This requires simple transformations on related objects, which can be handled within Appian's process model. The "Write to Data Store Entity" smart service allows you to update multiple related entities (e.g., "Company", "Address", "Contract") based on the "Customer" update, using Appian's expression rules for logic. This approach leverages Appian's process automation, is user-friendly for developers, and is recommended for straightforward updates within the Appian environment.
Matching Rationale:
* Each tool is used once, covering the spectrum of database integration options: Database Complex View for read/display, Database Trigger for simple database-side automation, Database Stored Procedure for complex multi-table logic, and Write to Data Store Entity smart service for Appian-managed simple updates.
* Appian's guidelines prioritize using the right tool based on complexity and context, ensuring efficiency and maintainability.
References:Appian Documentation - Data Integration and Database Management, Appian Process Model Guide - Smart Services, Appian Lead Developer Training - Database Optimization.


NEW QUESTION # 40
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