Appian ACD200 Exam Dumps & Practice Test Questions

Question 1:

You are troubleshooting a failed SAML connection attempt to an identity provider. To gain deeper insight, you decide to enable more detailed logging that includes trace-level information about the authentication process.

Which configuration file should you edit to adjust the logging level for this purpose?

A. commons-logging.properties
B. appian_log4j.properties
C. logging.properties
D. custom.properties

Correct Answer: C

Explanation:

When encountering SAML authentication issues, increasing the verbosity of logging is often necessary to uncover low-level problems such as malformed assertions, misconfigured endpoints, or SSL errors. To accomplish this in Java-based applications—especially those using standard application servers like Tomcat—you typically need to adjust the logging.properties file.

The logging.properties file is a default configuration used by Java Util Logging (JUL). It allows administrators to define the behavior of the logging system, including which loggers are active, what their levels are (e.g., INFO, DEBUG, TRACE), and how output is formatted or directed. By increasing the log level to FINE, FINER, or FINEST (equivalent to trace/debug levels), you can capture detailed logs, which are essential for pinpointing issues in the SAML handshake or token validation process.

Why this is the correct file:
The logging.properties file directly controls how logging is handled within many Java-based application servers. Because SAML interactions typically pass through built-in authentication modules that integrate with the Java security stack, increasing the logging level here allows full visibility into the lifecycle of the authentication process. This includes HTTP request headers, response codes, and stack traces—vital data points when troubleshooting SAML failures.

Why the other options are incorrect:

• A. commons-logging.properties: While Apache Commons Logging is used in many Java apps as an abstraction layer for logging, this file only defines the underlying logging framework (like Log4j or JUL). It doesn't set detailed log levels for specific events like SAML connections.

• B. appian_log4j.properties: This file is specific to applications using Appian and Log4j. Unless you are working within an Appian-based environment using Log4j explicitly, this file will not control authentication logs at the application server level.

• D. custom.properties: This is often a placeholder for application-specific configuration values. It is not a recognized standard for logging configuration and wouldn't affect how authentication logs are generated.

For applications needing trace-level diagnostic output during SAML authentication, logging.properties is the most appropriate file to update.

Correct answer: C. logging.properties

Question 2:

While setting up a Web API, you need to configure key components in the Administration Console to manage both access security and external integrations.

Which two of the following should be configured during this setup? (Choose two.)

A. LDAP Authentication
B. API Key
C. Connected System
D. Service Account

Correct Answers: B, C

Explanation:

When creating a Web API, proper configuration ensures that only authorized clients can access it and that it can communicate with external systems. Two critical components configured in the Administration Console during Web API setup are the API Key and the Connected System.

Why B. API Key is correct:
An API Key acts as a secure identifier that clients use when interacting with the Web API. It plays a vital role in enforcing authentication and authorization. Without an API key or another authentication mechanism in place, the API would be publicly accessible, posing significant security risks. In the Administration Console, you can create and manage these keys, define usage scopes, expiration policies, and monitor usage metrics.

Why C. Connected System is correct:
A Connected System represents the external service or system your Web API interacts with. Whether it's a third-party REST API, a database, or another internal service, defining a Connected System allows your API to know where to send requests and how to authenticate with those services. The Administration Console provides an interface to configure the endpoint URL, authentication credentials (e.g., OAuth tokens or basic auth), and other connection settings required for seamless integration.

Why the other options are incorrect:

• A. LDAP Authentication: LDAP is used primarily for user authentication within internal enterprise applications. It plays no direct role in defining or configuring access to a Web API. It is more relevant when managing user login policies rather than API-specific permissions or settings.

• D. Service Account: While a Service Account might be used by automated processes or background jobs that consume the Web API, these are typically managed outside of the Administration Console during API creation. They are part of user or access control management, but not a core setup element in defining the API itself.

In conclusion, to create a secure and well-integrated Web API, defining an API Key for authentication and a Connected System for integration are two essential steps.
Correct answers: B. API Key and C. Connected System

Question 3:

You're generating a report using a database View through Appian and encounter the error message:
“a!queryEntity: An error occurred while retrieving the data.”

What is the most probable reason for this error occurring?

A. The View is too large, and the query takes excessive time to execute.
B. The Custom Data Type (CDT) linked to the View does not define a Primary Key.
C. One or more required inputs to the query were not provided.
D. The expression rule has a syntax error.

Correct Answer: B

Explanation:

The most likely cause of the error message “a!queryEntity: An error occurred while retrieving the data” in Appian is due to the absence of a Primary Key mapping in the CDT associated with the database View. This is a common structural misconfiguration that disrupts how Appian handles data queries at runtime.

In Appian, the a!queryEntity() function is used to retrieve data from a Data Store Entity (DSE), which is typically backed by a database Table or View. This function depends on a Custom Data Type (CDT) that represents the schema of the source. Even though a database View does not enforce a primary key by default, Appian mandates a primary key definition in the corresponding CDT for reliable and predictable data retrieval. Without a defined primary key (marked with @Id), Appian cannot uniquely identify records, making pagination and efficient querying unreliable — ultimately leading to the kind of runtime error described.

Let’s explore why option B is correct:

• Appian enforces that any DSE, even if based on a View, must have a primary key field in its CDT.

• When a primary key is not defined, Appian throws an error while attempting to execute a!queryEntity() because it cannot distinguish between rows or ensure consistent pagination and sorting behavior.

• This is not a performance or syntax-related error but a structural issue within the data model.

Why the other options are incorrect:

• A. Large data volume (performance issue): A large View may cause slowness or timeouts, but it would not result in this particular error. If the issue were purely performance-related, Appian would return timeout or resource usage warnings instead.

• C. Missing inputs: If parameters were missing, Appian would raise a different error during expression evaluation, such as “Required parameter not provided” — not during the a!queryEntity() execution phase.

• D. Syntax errors: Syntax issues are caught during design time when writing or validating the rule, not at runtime. Appian would show a “syntax error” or “invalid expression” message if this were the case.

Therefore, the error is most accurately attributed to the absence of a primary key in the CDT linked to the database View, making B the correct and best answer.

Question 4:

While reviewing system performance, you notice that certain expression rules interacting with a Data Store are running slowly. You decide to investigate the frequency of operations on this data store using the data_store_details.csv file.

Which metric should you examine to determine the total number of operations performed on a data store?

A. Transform Count
B. Query Count
C. Total Count
D. Execute Count

Correct Answer: C

Explanation:

In Appian's performance monitoring suite, the data_store_details.csv file serves as a vital source of insight into how Data Store Entities (DSEs) are being accessed. When assessing slow expression rules or interfaces, examining how frequently data is being read or written can lead you to the root of performance bottlenecks. The best metric to assess the overall number of operations performed against a data store is the Total Count.

Why Total Count is the correct answer:

• Total Count reflects the cumulative number of all operations (including reads, writes, updates, deletes) performed on a specific Data Store Entity.

• This gives developers a holistic view of interaction volume with the database, which is key to identifying whether excessive usage is affecting application speed.

• For instance, a high Total Count on a DSE may indicate that expression rules are unnecessarily querying or writing to the database too often, leading to slowness or resource contention.

• This metric enables data architects to make informed decisions on where to optimize queries, use caching, or redesign CDTs.

Why the other options are incorrect:

• A. Transform Count: This metric relates to the transformation logic applied after the data is retrieved (e.g., mapping, conversion). It doesn’t show how many times the data store was accessed — it just tracks internal data manipulation.

• B. Query Count: While this may seem relevant, it only tracks read operations. It excludes inserts, updates, and deletes. Therefore, it does not provide a complete view of the operational load on the data store.

• D. Execute Count: This typically refers to the execution frequency of rules or expressions, not specific database operations. It can help understand how often a rule runs but doesn’t clarify how often the data store is accessed.

To effectively analyze database interaction trends and troubleshoot system slowness, Total Count offers the most reliable metric. It allows for cross-comparison across entities and is crucial when planning optimizations such as batching operations, adding indexes, or reworking high-frequency queries.

Thus, the correct choice is C. Total Count.

Question 5:

You’ve built a process model that includes a Send E-Mail node to deliver notifications to recipients. However, when the process executes, it throws the error:

To understand why this failure occurred, where should you look first to obtain diagnostic details?
(Choose the best answer.)

A. Submit a support ticket through My Appian for cloud team analysis
B. Check the system.csv log for errors
C. Generate and analyze the Health Check report
D. Examine the application server’s stdout log

Correct Answer: D

Explanation:

When an email fails to send within a process model using Appian’s built-in Send E-Mail node, your first priority should be pinpointing the root cause using runtime diagnostic data. The most appropriate and effective place to start is the application server’s stdout log.

This stdout log (sometimes referred to as server.log depending on how Appian is hosted) captures detailed real-time server activity, including stack traces, error codes, exceptions, and more importantly, SMTP-level communication issues. These logs can immediately highlight problems like:

• Misconfigured SMTP settings

• Authentication errors

• Unreachable mail servers

• Invalid email addresses

• Network or firewall issues affecting mail delivery

The stdout log is preferred because it offers precise technical insight into what the server was doing at the moment of failure. Instead of guesswork, developers can review the exact reason the mail service failed, significantly reducing the time to resolution.

Let’s explore why the other choices are less effective as first steps:

• A. Submit a support case through My Appian: While involving Appian Support is sometimes necessary, it should be a last resort after internal troubleshooting. Support teams will often request log files, so it’s more productive to review them first.

• B. Check the system.csv log: This log tracks high-level system performance metrics but does not capture error messages or SMTP-related failures, making it irrelevant for this issue.

• C. Generate and analyze the Health Check report: Health Check is a best-practice diagnostic used for design evaluation and performance monitoring. It doesn’t provide real-time node-level error details like those needed here.

To resolve email errors quickly and independently, the stdout log provides the most immediate, detailed, and actionable information. It’s the first and most effective tool in your debugging toolkit for this kind of failure.

Question 6:

You’re looking to enhance both your team’s coding standards and their overall skill development. 

Which of the following review methods is best suited to coach developers while also boosting code quality?
(Choose the best answer.)

A. Peer Development Review
B. Automated Code Scanning
C. Project Retrospectives
D. User Acceptance Testing

Correct Answer: A

Explanation:

Improving software quality isn’t just about catching bugs—it’s about building a team culture where developers learn continuously and adhere to consistent standards. Among all review formats listed, Peer Development Reviews (also known as code reviews) are the most efficient and educational.

Peer reviews offer immediate, context-aware feedback. A more experienced developer walks through another’s code, highlighting issues, recommending improvements, and explaining why changes are needed. This interaction forms the foundation for technical mentorship and real-time knowledge sharing.

Here's why Peer Reviews are the best:

• Skill Development: Less experienced developers learn not just what to change, but why. This accelerates their growth and understanding of clean coding principles.

• Error Prevention: Bugs and inefficiencies are spotted early—before they become more expensive to fix downstream.

• Consistency: Peer reviews ensure the entire team aligns with organizational coding standards and conventions.

• Culture of Collaboration: These reviews promote open communication, trust, and shared code ownership, all of which are essential for long-term project success.

Now, let’s contrast that with the other options:

• B. Automated Code Scanning: Tools like SonarQube are great at identifying technical issues (like security flaws or formatting violations), but they lack human context. They can’t explain why something’s wrong or offer nuanced advice, which limits their value as coaching tools.

• C. Retrospectives: Held at the end of sprints, retros focus on process improvement, not specific code quality. While important, they’re not a substitute for hands-on, line-by-line feedback on actual code.

• D. User Acceptance Testing (UAT): UAT checks if the application meets business requirements. It’s performed too late in the development cycle to influence code quality directly or provide meaningful coaching to developers.

For real-time coaching, fostering shared accountability, and enforcing coding standards, Peer Development Reviews offer a highly effective approach that balances learning with quality assurance. They remain an industry best practice in modern Agile development workflows.

Question 7:

A lead designer has been tasked with implementing a solution to record every change made to a record for auditing purposes. 

What is the most effective method to accomplish this without heavily impacting Appian's performance?

A. Develop a custom plugin that logs changes to a file
B. Add a trigger to the database table to log changes into an audit table
C. Build an Appian process model to log changes into the database
D. Configure a web API to send audit data to an external system

Correct Answer: B

Explanation:

When building an audit system in Appian, performance efficiency and minimal application overhead are critical. The objective is to reliably track every change made to a record while ensuring that the application's core performance is not degraded.

Option B, which involves creating a database-level trigger, is the most efficient solution. A trigger is a database construct that automatically executes specified actions in response to events such as INSERT, UPDATE, or DELETE. These operations occur at the database engine level and are executed instantly, independent of Appian’s application layer. This ensures that every change is captured—even those not made through the Appian interface, such as direct database updates or third-party integrations.

This method is non-intrusive to Appian’s logic. Developers don't need to modify process models, interface rules, or expression logic to account for audit logging. Since the trigger operates at the database layer, the approach is scalable, efficient, and maintains system performance.

Additionally, the audit data can be stored in a dedicated audit table, including columns for change_type, timestamp, user_id, record_id, and before/after values. This structure provides a comprehensive and query-friendly audit trail.

Why the other options are less ideal:

• Option A (custom plugin): While technically possible, it requires custom Java development, plugin deployment, and ongoing maintenance. Logging to a file can also make querying historical changes difficult, as log files are not structured for analytics or reporting.

• Option C (Appian process model): This solution introduces processing overhead for every transaction. Managing additional process instances for every record update adds complexity and may negatively impact application scalability and speed.

• Option D (web API call): This introduces potential latency and dependency on external services. If the external system fails or is slow to respond, the entire update operation may be delayed or unsuccessful.

In summary, using a database trigger is the cleanest and most reliable way to meet auditing requirements while keeping Appian performant and uncluttered. It supports full audit traceability with minimal maintenance.

Question 8:

You are modeling a college's database where students can enroll in multiple classes, and each class can have multiple students. 

What is the most appropriate way to implement this Many-to-Many relationship in Appian while adhering to First Normal Form?

A. Use a join table to represent Student/Class relationships
B. Add an array of Class IDs to the Student table
C. Add an array of Student IDs to the Class table
D. Many-to-Many relationships are not supported in Appian

Correct Answer: A

Explanation:

Modeling Many-to-Many (M:N) relationships is a common requirement in relational databases and must be done carefully to maintain First Normal Form (1NF). In the case of a college system, each student can enroll in multiple classes, and each class can accept multiple students. Storing this relationship correctly ensures scalability, data integrity, and ease of use within Appian.

Option A, which involves creating a join table, is the standard and correct approach. This join table (often called Enrollment) contains two primary foreign keys: student_id and class_id. Each row in this table represents a single enrollment, linking one student to one class.

This method resolves the M:N relationship by converting it into two One-to-Many relationships:

• A student can appear in many rows in the Enrollment table (one-to-many).

• A class can also appear in many rows (one-to-many).

This design ensures compliance with 1NF by maintaining atomic values—each column contains a single, indivisible piece of data. It also aligns well with Appian's Complex Data Types (CDTs) and Data Store Entities (DSEs), allowing for clean integration and easy querying within Appian applications.

Why other options fall short:

• Option B (array of Class IDs in Student table): This structure breaks 1NF because it stores non-atomic values in a single field. Furthermore, Appian does not natively support storing arrays of foreign keys in a way that can be directly mapped in a relational schema.

• Option C (array of Student IDs in Class table): Similar to Option B, this design also violates normalization rules and creates difficulties in querying and maintaining data integrity.

• Option D (unsupported in Appian): This is incorrect. While Appian doesn’t support M:N relationships natively in CDTs like some ORMs (Object-Relational Mapping tools), the join-table approach is a well-accepted and fully functional workaround.

In conclusion, the use of a join table not only satisfies relational database principles but also integrates cleanly with Appian's data modeling tools. It’s the most efficient, scalable, and standards-compliant method for implementing M:N relationships.

Question 9:

You are working with a large dataset involving five different tables that need to be joined. Each of these tables has a significant number of records, and querying them together could result in performance issues due to the heavy data load. However, the business does not need real-time data and is comfortable with a 2-hour data refresh window. 

Given that performance is the primary concern, what solution should you use?

A. Table
B. View
C. Stored procedure
D. Materialized view

Correct Answer: D

Explanation:

When you're dealing with complex joins across multiple large tables, and live or real-time data isn't required, the best solution to maintain performance is a Materialized View. A materialized view is essentially a precomputed, physical snapshot of a query result. Unlike regular views, which execute the SQL each time they are accessed, materialized views store data persistently and are only refreshed on a scheduled basis.

In this case, since the business has explicitly stated that a two-hour refresh cycle is acceptable, using a materialized view is the most optimal approach. You can configure the view to refresh every two hours, ensuring it reflects fairly recent data while avoiding the performance hit of re-executing large joins every time the data is accessed.

Another key advantage is that materialized views improve query speed dramatically, as the database does not need to perform expensive joins and aggregations at query time. The results are already computed and stored, so users benefit from fast, consistent performance.

Let’s briefly consider the other options:

• A. Table: While you could manually create a table to store joined data, it would require additional logic (e.g., triggers or batch jobs) to populate and maintain it. This introduces maintenance overhead and is less efficient than using a native materialized view.

• B. View: A regular view executes the underlying SQL every time it is queried, leading to poor performance with large datasets and complex joins. Not ideal here since performance is the top concern.

• C. Stored procedure: A stored procedure can perform the join logic, but it doesn’t persist data unless you explicitly write the output to another table. This approach adds complexity and doesn’t benefit from the native optimization of materialized views.

In conclusion, materialized views strike the right balance between performance, data accuracy, and maintainability—making them the best choice for this use case.

Question 10:

You are designing a data table to store library book records. Each book entry will have a reference number (ISBN_ID) and a system-generated unique identifier (BOOK_ID). 

To ensure that your Custom Data Type (CDT) can correctly store and manage this BOOK_ID as a primary key, which data type should you assign to it?

A. Number (Integer)
B. Number (Decimal)
C. Date
D. Boolean

Correct Answer: A

Explanation:

The best practice for assigning a primary key or unique identifier such as BOOK_ID is to use the Integer data type. An integer is simple, lightweight, and highly efficient for indexing and querying, making it ideal for large datasets like a library catalog.

Here’s why Number (Integer) is the most appropriate choice:

• Efficient indexing: Integer fields are easier and faster for database systems to index and retrieve, especially when performing searches, joins, or lookups.

• Auto-increment support: Most relational databases (e.g., MySQL, PostgreSQL, SQL Server) support auto-increment functionality using integers. This makes it easy to automatically generate unique IDs for each book record.

• Appian CDT compatibility: In Appian, Integer values are often used to define identifiers in CDTs, particularly for primary keys, to ensure compatibility with common database design patterns.

Let’s review the other options:

• B. Number (Decimal): Decimals are typically reserved for values that require precision, like currency or scientific data. Using a decimal for a primary key is inefficient and introduces unnecessary complexity. Databases are optimized to handle integers as identifiers, not decimals.

• C. Date: While dates are useful for tracking events such as acquisition or publication dates, they are not suitable as primary keys. They cannot guarantee uniqueness and are less efficient for querying and indexing.

• D. Boolean: A Boolean field can only store two values—true or false—so it clearly cannot serve as a unique identifier for multiple book records. It’s completely unsuitable for primary key usage.

In summary, using an Integer for BOOK_ID ensures that the database performs well, maintains integrity through unique values, and aligns with industry-standard practices for primary keys in structured data tables.