ACD301 Practice Exam Questions and Answers

Comprehensive and Detailed In-Depth Explanation:

Data volume testing ensures an Appian application performs efficiently under realistic data loads, especially when interacting with external databases like MySQL. As an Appian Lead Developer with administrative access, the focus is on scalability, performance, and iterative validation. The two key considerations are:

Option C (The amount of data that needs to be populated should be determined by the project sponsor and the stakeholders based on their estimation):Determining the appropriate data volume is critical to simulate real-world usage. Appian’s Performance Testing Best Practices recommend collaborating with stakeholders (e.g., project sponsors, business analysts) to define expected data sizes based on production scenarios. This ensures the test reflects actual requirements—like peak transaction volumes or record counts—rather than arbitrary guesses. For example, if the application will handle 1 million records in production, stakeholders must specify this to guide test data preparation.

Option D (Testing with the correct amount of data should be in the definition of done as part of each sprint):Appian’s Agile Development Guide emphasizes incorporating performance testing (including data volume) into the Definition of Done (DoD) for each sprint. This ensures that features are validated under realistic conditions iteratively, preventing late-stage performance issues. With admin access, you can query/write to MySQL and assess query performance or write latency with the specified data volume, aligning with Appian’s recommendation to “test early and often.”

Option A (Data from previous tests needs to remain in the testing environment prior to loading prepopulated data): This is impractical and risky. Retaining old test data can skew results, introduce inconsistencies, or violate data integrity (e.g., duplicate keys in MySQL). Best practices advocate for a clean, controlled environment with fresh, prepopulated data per test cycle.

Option B (Large datasets must be loaded via Appian processes): While Appian processes can load data, this is not a requirement. With database admin access, you can use SQL scripts or tools like MySQL Workbench for faster, more efficient data population, bypassing Appian process overhead. Appian documentation notes this as a preferred method for large datasets.

Option E (Data model changes must wait until towards the end of the project): Delaying data model changes contradicts Agile principles and Appian’s iterative design approach. Changes should occur as needed throughout development to adapt to testing insights, not be deferred.

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.

Comprehensive and Detailed In-Depth Explanation:

As an Appian Lead Developer, addressing a client’s concerns about integrating legacy systems with Appian requires accurately identifying supported authentication methods for system-to-system communication or user access. The question focuses on Appian’s integration capabilities, likely for both user authentication (e.g., SSO) and API authentication, as legacy system integration often involves both. Appian’s documentation outlines supported methods in its Connected Systems and security configurations. Let’s evaluate each option:

A.  

API Keys:API Key authentication involves a static key sent in requests (e.g., via headers). Appian supports this for outbound integrations in Connected Systems (e.g., HTTP Authentication with an API key), allowing legacy systems to authenticate Appian calls. However, it’s not a user authentication method for Appian’s platform login—it’s for system-to-system integration. While supported, it’s less common for legacy system SSO or enterprise use cases compared to other options, making it a lower-priority choice here.

B.  

Biometrics:Biometrics (e.g., fingerprint, facial recognition) isn’t natively supported by Appian for platform authentication or integration. Appian relies on standard enterprise methods (e.g., username/password, SSO), and biometric authentication would require external identity providers or custom clients, not Appian itself. Documentation confirms no direct biometric support, ruling this out as an Appian-supported method.

C.  

SAML:Security Assertion Markup Language (SAML) is fully supported by Appian for user authentication via Single Sign-On (SSO). Appian integrates with SAML 2.0 identity providers (e.g., Okta, PingFederate), allowing users to log in using credentials from legacy systems that support SAML-based SSO. This is a key enterprise method, widely used for integrating with existing identity management systems, and explicitly listed in Appian’s security configuration options—making it a top choice.

D.  

CAC:Common Access Card (CAC) authentication, often used in government contexts with smart cards, isn’t natively supported by Appian as a standalone method. While Appian can integrate with CAC via SAML or PKI (Public Key Infrastructure) through an identity provider, it’s not a direct Appian authentication option. Documentation mentions smart card support indirectly via SSO configurations, but CAC itself isn’t explicitly listed, making it less definitive than other methods.

E.  

OAuth:OAuth (specifically OAuth 2.0) is supported by Appian for both outbound integrations (e.g., Authorization Code Grant, Client Credentials) and inbound API authentication (e.g., securing Appian Web APIs). For legacy system integration, Appian can use OAuth to authenticate with APIs (e.g., Google, Salesforce) or allow legacy systems to call Appian services securely. Appian’s Connected System framework includes OAuth configuration, making it a versatile, standards-based method highly relevant to the client’s needs.

F.  

Active Directory:Active Directory (AD) integration via LDAP (Lightweight Directory Access Protocol) is supported for user authentication in Appian. It allows synchronization of users and groups from AD, enabling SSO or direct login with AD credentials. For legacy systems using AD as an identity store, this is a seamless integration method. Appian’s documentation confirms LDAP/AD as a core authentication option, widely adopted in enterprise environments—making it a strong fit.

Conclusion: The three supported authentication methods are C (SAML), E (OAuth), and F (Active Directory). These align with Appian’s enterprise-grade capabilities for legacy system integration: SAML for SSO, OAuth for API security, and AD for user management. API Keys (A) are supported but less prominent for user authentication, CAC (D) is indirect, and Biometrics (B) isn’t supported natively. This selection reassures the client of Appian’s flexibility with common legacy authentication standards.

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.

Comprehensive and Detailed In-Depth Explanation:

As an Appian Lead Developer in a Post-Production Maintenance phase, troubleshooting a failed integration (HTTP 500 Internal Server Error) requires a systematic approach to isolate the root cause—whether it’s Appian-side, API-side, or environmental. A 500 error typically indicates an issue on the server (API) side, but the developer must confirm Appian’s contribution and collaborate with the customer. The goal is to select three steps that efficiently diagnose the specific scenario while adhering to Appian’s best practices. Let’s evaluate each option:

A.  

Send the same payload to the test API to ensure the issue is not related to the API environment:This is a critical step. Replicating the failure by sending the exact payload (from the failed Production call) to a test API environment helps determine if the issue is environment-specific (e.g., Production-only configuration) or inherent to the payload/API logic. Appian’s Integration troubleshooting guidelines recommend testing in a non-Production environment first to isolate variables. If the test API succeeds, the Production environment or API state is implicated; if it fails, the payload or API logic is suspect. This step leverages Appian’s Integration object logging (e.g., request/response capture) and is a standard diagnostic practice.

B.  

Send a test case to the Production API to ensure the service is still up and running:While verifying Production API availability is useful, sending an arbitrary test case risks further Production disruption during maintenance and may not replicate the specific scenario. A generic test might succeed (e.g., with simpler data), masking the issue tied to the complex JSON. Appian’s Post-Production guidelines discourage unnecessary Production interactions unless replicating the exact failure is controlled and justified. This step is less precise than analyzing existing behavior (C) and is not among the top three priorities.

C.  

Analyze the behavior of subsequent calls to the Production API to ensure there is no global issue, and ask the customer to analyze the API logs to understand the nature of the issue:This is essential. Reviewing subsequent Production calls (via Appian’s Integration logs or monitoring tools) checks if the 500 error is isolated or systemic (e.g., API outage). Since Appian can’t access API server logs, collaborating with the customer to review their logs is critical for a 500 error, which often stems from server-side exceptions (e.g., unhandled data). Appian Lead Developer training emphasizes partnership with API owners and using Appian’s Process History or Application Monitoring to correlate failures—making this a key troubleshooting step.

D.  

Obtain the JSON sent to the API and validate that there is no difference between the expected JSON format and the sent one:This is a foundational step. The complex JSON payload is central to the integration, and a 500 error could result from malformed data (e.g., missing fields, invalid types) that the API can’t process. In Appian, you can retrieve the sent JSON from the Integration object’s execution logs (if enabled) or Process Instance details. Comparing it against the API’s documented schema (e.g., via Postman or API specs) ensures Appian’s output aligns with expectations. Appian’s documentation stresses validating payloads as a first-line check for integration failures, especially in specific scenarios.

E.  

Ensure there were no network issues when the integration was sent:While network issues (e.g., timeouts, DNS failures) can cause integration errors, a 500 Internal Server Error indicates the request reached the API and triggered a server-side failure—not a network issue (which typically yields 503 or timeout errors). Appian’s Connected System logs can confirm HTTP status codes, and network checks (e.g., via IT teams) are secondary unless connectivity is suspected. This step is less relevant to the 500 error and lower priority than A, C, and

D.  

Conclusion: The three best steps are A (test API with same payload), C (analyze subsequent calls and customer logs), and D (validate JSON payload). These steps systematically isolate the issue—testing Appian’s output (D), ruling out environment-specific problems (A), and leveraging customer insights into the API failure (C). This aligns with Appian’s Post-Production Maintenance strategies: replicate safely, analyze logs, and validate data.

Comprehensive and Detailed In-Depth Explanation:

As an Appian Lead Developer, recommending a test strategy for a large, complex application undergoing a database migration (e.g., from Oracle to PostgreSQL) and interface/process changes requires focusing on ensuring system stability, functionality, and the specific updates. The strategy must address risks tied to the scope—database technology shift, interface modifications, and process updates—while aligning with Appian’s testing best practices. Let’s evaluate each option:

A.  

Internationalization testing of the Appian platform:Internationalization testing verifies that the application supports multiple languages, locales, and formats (e.g., date formats). While valuable for global applications, the scenario doesn’t indicate a change in localization requirements tied to the database migration, interfaces, or processes. Appian’s platform handles internationalization natively (e.g., via locale settings), and this isn’t impacted by database technology or UI/process changes unless explicitly stated. This is out of scope for the given context and not a priority.

B.  

A regression test of all existing system functionality:This is a critical inclusion. A database migration between technologies can affect data integrity, queries (e.g., a!queryEntity), and performance due to differences in SQL dialects, indexing, or drivers. Regression testing ensures that all existing functionality—records, reports, processes, and integrations—works as expected post-migration. Appian Lead Developer documentation mandates regression testing for significant infrastructure changes like this, as unmapped edge cases (e.g., datatype mismatches) could break the application. Given the “large, complex” nature, full-system validation is essential to catch unintended impacts.

C.  

Penetration testing of the Appian platform:Penetration testing assesses security vulnerabilities (e.g., injection attacks). While security is important, the changes described—database migration, interface, and process updates—don’t inherently alter Appian’s security model (e.g., authentication, encryption), which is managed at the platform level. Appian’s cloud or on-premise security isn’t directly tied to database technology unless new vulnerabilities are introduced (not indicated here). This is a periodic concern, not specific to this migration, making it less relevant than functional validation.

D.  

Tests for each of the interfaces and process changes:This is also essential. The project includes explicit “interface and process changes” alongside the migration. Interface updates (e.g., SAIL forms) might rely on new data structures or queries, while process changes (e.g., modified process models) could involve updated nodes or logic. Testing each change ensures these components function correctly with the new database and meet business requirements. Appian’s testing guidelines emphasize targeted validation of modified components to confirm they integrate with the migrated data layer, making this a primary focus of the strategy.

E.  

Tests that ensure users can still successfully log into the platform:Login testing verifies authentication (e.g., SSO, LDAP), typically managed by Appian’s security layer, not the business database. A database migration affects application data, not user authentication, unless the database stores user credentials (uncommon in Appian, which uses separate identity management). While a quick sanity check, it’s narrow and subsumed by broader regression testing (B), making it redundant as a standalone item.

Conclusion: The two key items are B (regression test of all existing system functionality) and D (tests for each of the interfaces and process changes). Regression testing (B) ensures the database migration doesn’t disrupt the entire application, while targeted testing (D) validates the specific interface and process updates. Together, they cover the full scope—existing stability and new functionality—aligning with Appian’s recommended approach for complex migrations and modifications.

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.