Question 1

You are reviewing the Engine Performance Logs in Production for a single application that has been
live for six months. This application experiences concurrent user activity and has a fairly sustained
load during business hours. The client has reported performance issues with the application during
business hours.
During your investigation, you notice a high Work Queue - Java Work Queue Size value in the logs.
You also notice unattended process activities, including timer events and sending notification emails,
are taking far longer to execute than normal.
The client increased the number of CPU cores prior to the application going live.
What is the next recommendation?

Accepted Answer

A

Explanation: The key symptoms described—a high Work Queue - Java Work Queue Size and slow execution of unattended process activities—are direct indicators of an overloaded process execution engine tier. The Java work queue represents tasks waiting for an engine thread to become available. A persistently high value means the rate of incoming work exceeds the system's processing capacity. Adding more engine replicas is the standard Appian scaling strategy to increase parallel processing throughput for background activities, thereby distributing the load and reducing the queue size and execution times.

Question 2

As part of your implementation workflow, users need to retrieve data stored in a third-party Oracle
database on an interface. You need to design a way to query this information.
How should you set up this connection and query the data?

Accepted Answer

D

Explanation: The standard and most direct Appian method for connecting to an external relational database, such as Oracle, is to configure it as a data source in the Administration Console. This establishes the JDBC connection. Once the data source is configured, you can create a data store that points to it and define data store entities that map to the database tables or views. The a!queryEntity() function can then be used directly within an interface expression to query this data in real-time, providing an efficient and scalable solution for displaying external data to users.

Question 3

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).
ACD301 practice exam questions

Which three column should be indexed?

Accepted Answer

A, B, E

Explanation: To ensure the record grid performs efficiently, database indexes should be created on columns used for filtering and joining. The requirements state that users must filter by priority and status, making it essential to index these two columns. The record's data source is a view that joins the case and site tables on the siteid column. Indexing the foreign key column (siteid in the case table) is a critical best practice to accelerate the performance of this join operation. Therefore, siteid, status, and priority are the most important columns to index.

Question 4

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?

Accepted Answer

C, D

Explanation: A successful data volume testing strategy requires establishing realistic data targets and integrating testing into the development lifecycle. The volume of data for testing should be based on current and projected production usage, which must be defined by business stakeholders and project sponsors to ensure the tests are meaningful. Furthermore, incorporating performance testing with this data volume into the "Definition of Done" for each sprint ensures that performance issues are identified and addressed early and iteratively, preventing major problems late in the project.

Question 5

HOTSPOT
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.
ACD301 practice exam questions

Accepted Answer

Explanation: Archive processes 2 days after completion or cancellation. → Processes that need to be available for 2 days after completion or cancellation, after which are no longer required nor accessible. Use system default (currently: auto-archive processes 7 days after completion or cancellation). → Processes that remain available for 7 days after completion or cancellation, after which remain accessible. Delete processes 2 days after completion or cancellation. → Processes that need to be available for 2 days after completion or cancellation, after which remain accessible. Do not automatically clean-up processes. → Processes that need remain available without the need to unarchive. Comprehensive and Detailed In-Depth Explanation: Appian provides process model data management strategies to manage the lifecycle of completed or canceled processes, balancing storage efficiency and accessibility. These strategies—archiving, using system defaults, deleting, and not cleaning up—are configured via the Appian Administration Console or process model settings. The Appian Process Management Guide outlines their purposes, enabling accurate matching. Archive processes 2 days after completion or cancellation → Processes that need to be available for 2 days after completion or cancellation, after which are no longer required nor accessible: Archiving moves processes to a compressed, off-line state after a specified period, freeing up active resources. The description "available for 2 days, then no longer required nor accessible" matches this strategy, as archived processes are stored but not immediately accessible without unarchiving, aligning with the intent to retain data briefly before purging accessibility. Use system default (currently: auto-archive processes 7 days after completion or cancellation) → Processes that remain available for 7 days after completion or cancellation, after which remain accessible: The system default auto-archives processes after 7 days, as specified. The description "remain available for 7 days, then remain accessible" fits this, indicating that processes are kept in an active state for 7 days before being archived, after which they can still be accessed (e.g., via unarchiving), matching the default behavior. Delete processes 2 days after completion or cancellation → Processes that need to be available for 2 days after completion or cancellation, after which remain accessible: Deletion permanently removes processes after the specified period. However, the description "available for 2 days, then remain accessible" seems contradictory since deletion implies no further access. This appears to be a misinterpretation in the options. The closest logical match, given the constraint of using each strategy once, is to assume a typo or intent to mean "no longer accessible" after deletion. However, strictly interpreting the image, no perfect match exists. Based on context, "remain accessible" likely should be "no longer accessible," but I’ll align with the most plausible intent: deletion after 2 days fits the "no longer required" aspect, though accessibility is lost post- deletion. Do not automatically clean-up processes → Processes that need remain available without the need to unarchive: Not cleaning up processes keeps them in an active state indefinitely, avoiding archiving or deletion. The description "remain available without the need to unarchive" matches this strategy, as processes stay accessible in the system without additional steps, ideal for long-term retention or audit purposes. Matching Rationale: Each strategy is used once, as required. The matches are based on Appian’s process lifecycle management: archiving for temporary retention with eventual inaccessibility, system default for a 7- day accessible period, deletion for permanent removal (adjusted for intent), and no cleanup for indefinite retention. The mismatch in Option 3’s description ("remain accessible" after deletion) suggests a possible error in the question’s options, but the assignment follows the most logical interpretation given the constraint. Reference: Appian Documentation - Process Management Guide, Appian Administration Console - Process Model Settings, Appian Lead Developer Training - Data Management Strategies.

Question 6

HOTSPOT
You are reviewing log files that can be accessed in Appian to monitor and troubleshoot platform-
based issues.
For each type of log file, match the corresponding Information that it provides. Each description will
either be used once, or not at all.
Note: To change your responses, you may deselect your response by clicking the blank space at the
top of the selection list.
ACD301 practice exam questions

Accepted Answer

Explanation: design_errors.csv → Errors in start forms, task forms, record lists, enabled environments devops_infrastructure.csv → Metrics such as the total time spent evaluating a plug-in function login-audit.csv → Inbound requests using HTTP basic authentication Comprehensive and Detailed In-Depth Explanation: Appian provides various log files to monitor and troubleshoot platform issues, accessible through the Administration Console or exported as CSV files. These logs capture different aspects of system performance, security, and user interactions. The Appian Monitoring and Troubleshooting Guide details the purpose of each log file, enabling accurate matching. design_errors.csv → Errors in start forms, task forms, record lists, enabled environments: The design_errors.csv log file is specifically designed to track errors related to the design and runtime behavior of Appian objects such as start forms, task forms, and record lists. It also includes information about issues in enabled environments, making it the appropriate match. This log helps developers identify and resolve UI or configuration errors, aligning with its purpose of capturing design-time and runtime issues. devops_infrastructure.csv → Metrics such as the total time spent evaluating a plug-in function: The devops_infrastructure.csv log file provides infrastructure and performance metrics for Appian Cloud instances. It includes data on system performance, such as the time spent evaluating plug-in functions, which is critical for optimizing custom integrations. This matches the description, as it focuses on operational metrics rather than errors or security events, consistent with Appian’s infrastructure monitoring approach. login-audit.csv → Inbound requests using HTTP basic authentication: The login-audit.csv log file tracks user authentication and login activities, including details about inbound requests using HTTP basic authentication. This log is used to monitor security events, such as successful and failed login attempts, making it the best fit for this description. Appian’s security logging emphasizes audit trails for authentication, aligning with this use case. Unused Description: Number of enabled environments: This description is not matched to any log file. While it could theoretically relate to system configuration logs, none of the listed files (design_errors.csv, devops_infrastructure.csv, login-audit.csv) are specifically designed to report the number of enabled environments. This might be tracked in a separate administrative report or configuration log not listed here. Matching Rationale: Each description is either used once or not at all, as specified. The matches are based on Appian’s documented log file purposes: design_errors.csv for design-related errors, devops_infrastructure.csv for performance metrics, and login-audit.csv for authentication details. The unused description suggests the question allows for some descriptions to remain unmatched, reflecting real-world variability in log file content. Reference: Appian Documentation - Monitoring and Troubleshooting Guide, Appian Administration Console - Log File Reference, Appian Lead Developer Training - Platform Diagnostics.

Question 7

HOTSPOT 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. https://kxbjsyuhceggsyvxdkof.supabase.co/storage/v1/object/public/file-images/Appian_ACD301/page_15_img_1.jpg

Accepted Answer

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. Reference: Appian Documentation - Data Integration and Database Management, Appian Process Model Guide - Smart Services, Appian Lead Developer Training - Database Optimization.

Question 8

DRAG DROP You are selling up a new cloud environment. The customer already has a system of record for Its employees and doesn't want to re-create them in Appian. so you are going to Implement LDAP authentication. What are the next steps to configure LDAP authentication? To answer, move the appropriate steps from the Option list to the Answer List area, and arrange them in the correct order. You may or may not use all the steps. https://kxbjsyuhceggsyvxdkof.supabase.co/storage/v1/object/public/file-images/Appian_ACD301/page_26_img_1.jpg

Accepted Answer

Explanation: The configuration of LDAP authentication in Appian is a linear process performed within the Administration Console . Navigation: First, you must access the specific settings page by navigating to Admin Console > Authentication and selecting the LDAP tab. Configuration: You must toggle the Enable LDAP authentication checkbox to activate the feature. Once enabled, you define the required connection parameters (e.g., Provider URL, Bind DN, and Password). The option mentioning "two parameters" is incorrect because it misses the critical "Enable" step and oversimplifies the required fields (which often include Base DN, User Search Filter, etc., in addition to URL and credentials). Verification: Finally, standard Appian best practice dictates that you use the Test Connection button to verify connectivity and credentials before committing the changes with Save .

Question 9

As part of an upcoming release of an application, a new nullable field is added to a table that
contains customer dat
a. The new field is used by a report in 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?

Accepted Answer

B, C

Explanation: A robust deployment strategy for database changes involves two key components. First, the forward-migration script must bring the database to the required state for the new application version. This includes not only adding the new field (schema change) but also populating it with the necessary calculated data (data change), ensuring the new report functions correctly immediately after deployment. Second, a corresponding rollback script is crucial. This script should completely reverse the changes—in this case, by removing the newly added field—to restore the database to its previous state if the deployment fails or needs to be reverted. This ensures a clean and predictable state for the older application version.

Question 10

Review the following result of an explain statement: https://kxbjsyuhceggsyvxdkof.supabase.co/storage/v1/object/public/file-images/Appian_ACD301/page_33_img_1.jpg Which two conclusions can you draw from this?

Accepted Answer

B, D

Explanation: The EXPLAIN plan reveals a significant performance issue. The join to the product table (aliased as p) has a type of ALL, indicating a full table scan. For each of the 1,000 rows from orderdetail, the database scans all 1,000 rows of the product table, resulting in 1,000,000 row examinations for this step alone. This makes the join between orderdetail and product the clear bottleneck. Therefore, this join needs to be "fine-tuned due to indices" (D). Furthermore, this is unequivocally the "worst join" in the query. Option (B) states the worst join is with the order table, which is factually incorrect as that join is highly efficient (type: eqref). This is a known flawed question, and (B) is considered correct by assuming a typo where "order" should be "product".

Free Appian ACD301 Actual Exam Questions