Exam ACD301 Duration | ACD301 Exam Flashcards

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Appian ACD301 Exam Syllabus Topics:

Topic	Details
Topic 1	Platform Management: This section of the exam measures skills of Appian System Administrators and covers the ability to manage platform operations such as deploying applications across environments, troubleshooting platform-level issues, configuring environment settings, and understanding platform architecture. Candidates are also expected to know when to involve Appian Support and how to adjust admin console configurations to maintain stability and performance.
Topic 2	Proactively Design for Scalability and Performance: This section of the exam measures skills of Application Performance Engineers and covers building scalable applications and optimizing Appian components for performance. It includes planning load testing, diagnosing performance issues at the application level, and designing systems that can grow efficiently without sacrificing reliability.
Topic 3	Data Management: This section of the exam measures skills of Data Architects and covers analyzing, designing, and securing data models. Candidates must demonstrate an understanding of how to use Appian’s data fabric and manage data migrations. The focus is on ensuring performance in high-volume data environments, solving data-related issues, and implementing advanced database features effectively.
Topic 4	Application Design and Development: This section of the exam measures skills of Lead Appian Developers and covers the design and development of applications that meet user needs using Appian functionality. It includes designing for consistency, reusability, and collaboration across teams. Emphasis is placed on applying best practices for building multiple, scalable applications in complex environments.
Topic 5	Extending Appian: This section of the exam measures skills of Integration Specialists and covers building and troubleshooting advanced integrations using connected systems and APIs. Candidates are expected to work with authentication, evaluate plug-ins, develop custom solutions when needed, and utilize document generation options to extend the platform’s capabilities.

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Appian Lead Developer Sample Questions (Q43-Q48):

NEW QUESTION # 43
An Appian application contains an integration used to send a JSON, called at the end of a form submission, returning the created code of the user request as the response. To be able to efficiently follow their case, the user needs to be informed of that code at the end of the process. The JSON contains case fields (such as text, dates, and numeric fields) to a customer's API. What should be your two primary considerations when building this integration?

A. The size limit of the body needs to be carefully followed to avoid an error.
B. The request must be a multi-part POST.
C. A dictionary that matches the expected request body must be manually constructed.
D. A process must be built to retrieve the API response afterwards so that the user experience is not impacted.

Answer: A,C

Explanation:
Comprehensive and Detailed In-Depth Explanation:
As an Appian Lead Developer, building an integration to send JSON to a customer's API and return a code to the user involves balancing usability, performance, and reliability. The integration is triggered at form submission, and the user must see the response (case code) efficiently. The JSON includes standard fields (text, dates, numbers), and the focus is on primary considerations for the integration itself. Let's evaluate each option based on Appian's official documentation and best practices:
A . A process must be built to retrieve the API response afterwards so that the user experience is not impacted:
This suggests making the integration asynchronous by calling it in a process model (e.g., via a Start Process smart service) and retrieving the response later, avoiding delays in the UI. While this improves user experience for slow APIs (e.g., by showing a "Processing" message), it contradicts the requirement that the user is "informed of that code at the end of the process." Asynchronous processing would delay the code display, requiring additional steps (e.g., a follow-up task), which isn't efficient for this use case. Appian's default integration pattern (synchronous call in an Integration object) is suitable unless latency is a known issue, making this a secondary-not primary-consideration.
B . The request must be a multi-part POST:
A multi-part POST (e.g., multipart/form-data) is used for sending mixed content, like files and text, in a single request. Here, the payload is a JSON containing case fields (text, dates, numbers)-no files are mentioned. Appian's HTTP Connected System and Integration objects default to application/json for JSON payloads via a standard POST, which aligns with REST API norms. Forcing a multi-part POST adds unnecessary complexity and is incompatible with most APIs expecting JSON. Appian documentation confirms this isn't required for JSON-only data, ruling it out as a primary consideration.
C . The size limit of the body needs to be carefully followed to avoid an error:
This is a primary consideration. Appian's Integration object has a payload size limit (approximately 10 MB, though exact limits depend on the environment and API), and exceeding it causes errors (e.g., 413 Payload Too Large). The JSON includes multiple case fields, and while "hundreds of thousands" isn't specified, large datasets could approach this limit. Additionally, the customer's API may impose its own size restrictions (common in REST APIs). Appian Lead Developer training emphasizes validating payload size during design-e.g., testing with maximum expected data-to prevent runtime failures. This ensures reliability and is critical for production success.
D . A dictionary that matches the expected request body must be manually constructed:
This is also a primary consideration. The integration sends a JSON payload to the customer's API, which expects a specific structure (e.g., { "field1": "text", "field2": "date" }). In Appian, the Integration object requires a dictionary (key-value pairs) to construct the JSON body, manually built to match the API's schema. Mismatches (e.g., wrong field names, types) cause errors (e.g., 400 Bad Request) or silent failures. Appian's documentation stresses defining the request body accurately-e.g., mapping form data to a CDT or dictionary-ensuring the API accepts the payload and returns the case code correctly. This is foundational to the integration's functionality.
Conclusion: The two primary considerations are C (size limit of the body) and D (constructing a matching dictionary). These ensure the integration works reliably (C) and meets the API's expectations (D), directly enabling the user to receive the case code at submission end. Size limits prevent technical failures, while the dictionary ensures data integrity-both are critical for a synchronous JSON POST in Appian. Option A could be relevant for performance but isn't primary given the requirement, and B is irrelevant to the scenario.
Reference:
Appian Documentation: "Integration Object" (Request Body Configuration and Size Limits).
Appian Lead Developer Certification: Integration Module (Building REST API Integrations).
Appian Best Practices: "Designing Reliable Integrations" (Payload Validation and Error Handling).

NEW QUESTION # 44
You need to connect Appian with LinkedIn to retrieve personal information about the users in your application. This information is considered private, and users should allow Appian to retrieve their information. Which authentication method would you recommend to fulfill this request?

A. Basic Authentication with dedicated account's login information
B. API Key Authentication
C. OAuth 2.0: Authorization Code Grant
D. Basic Authentication with user's login information

Answer: C

NEW QUESTION # 45
You need to connect Appian with LinkedIn to retrieve personal information about the users in your application. This information is considered private, and users should allow Appian to retrieve their information. Which authentication method would you recommend to fulfill this request?

A. Basic Authentication with dedicated account's login information
B. API Key Authentication
C. OAuth 2.0: Authorization Code Grant
D. Basic Authentication with user's login information

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation:As an Appian Lead Developer, integrating with an external system like LinkedIn to retrieve private user information requires a secure, user-consented authentication method that aligns with Appian's capabilities and industry standards. The requirement specifies that users must explicitly allow Appian to access their private data, which rules out methods that don't involve user authorization. Let's evaluate each option based on Appian's official documentation and LinkedIn's API requirements:
* A. API Key Authentication:API Key Authentication involves using a single static key to authenticate requests. While Appian supports this method via Connected Systems (e.g., HTTP Connected System with an API key header), it's unsuitable here. API keys authenticate the application, not the user, and don't provide a mechanism for individual user consent. LinkedIn's API for private data (e.g., profile information) requires per-user authorization, which API keys cannot facilitate. Appian documentation notes that API keys are best for server-to-server communication without user context, making this option inadequate for the requirement.
* B. Basic Authentication with user's login information:This method uses a username and password (typically base64-encoded) provided by each user. In Appian, Basic Authentication is supported in Connected Systems, but applying it here would require users to input their LinkedIn credentials directly into Appian. This is insecure, impractical, and against LinkedIn's security policies, as it exposes user passwords to the application. Appian Lead Developer best practices discourage storing or handling user credentials directly due to security risks (e.g., credential leakage) and maintenance challenges.
Moreover, LinkedIn's API doesn't support Basic Authentication for user-specific data access-it requires OAuth 2.0. This option is not viable.
* C. Basic Authentication with dedicated account's login information:This involves using a single, dedicated LinkedIn account's credentials to authenticate all requests. While technically feasible in Appian's Connected System (using Basic Authentication), it fails to meet the requirement that "users should allow Appian to retrieve their information." A dedicated account would access data on behalf of all users without their individual consent, violating privacy principles and LinkedIn's API terms.
LinkedIn restricts such approaches, requiring user-specific authorization for private data. Appian documentation advises against blanket credentials for user-specific integrations, making this option inappropriate.
* D. OAuth 2.0: Authorization Code Grant:This is the recommended choice. OAuth 2.0 Authorization Code Grant, supported natively in Appian's Connected System framework, is designed for scenarios where users must authorize an application (Appian) to access their private data on a third-party service (LinkedIn). In this flow, Appian redirects users to LinkedIn's authorization page, where they grant permission. Upon approval, LinkedIn returns an authorization code, which Appian exchanges for an access token via the Token Request Endpoint. This token enables Appian to retrieve private user data (e.
g., profile details) securely and per user. Appian's documentation explicitly recommends this method for integrations requiring user consent, such as LinkedIn, and provides tools like a!authorizationLink() to handle authorization failures gracefully. LinkedIn's API (e.g., v2 API) mandates OAuth 2.0 for personal data access, aligning perfectly with this approach.
Conclusion: OAuth 2.0: Authorization Code Grant (D) is the best method. It ensures user consent, complies with LinkedIn's API requirements, and leverages Appian's secure integration capabilities. In practice, you'd configure a Connected System in Appian with LinkedIn's Client ID, Client Secret, Authorization Endpoint (e.
g., https://www.linkedin.com/oauth/v2/authorization), and Token Request Endpoint (e.g., https://www.
linkedin.com/oauth/v2/accessToken), then use an Integration object to call LinkedIn APIs with the access token. This solution is scalable, secure, and aligns with Appian Lead Developer certification standards for third-party integrations.
References:
* Appian Documentation: "Setting Up a Connected System with the OAuth 2.0 Authorization Code Grant" (Connected Systems).
* Appian Lead Developer Certification: Integration Module (OAuth 2.0 Configuration and Best Practices).
* LinkedIn Developer Documentation: "OAuth 2.0 Authorization Code Flow" (API Authentication Requirements).

NEW QUESTION # 46
You have created a Web API in Appian with the following URL to call it: https://exampleappiancloud.com/suite/webapi/user_management/users?username=john.smith. Which is the correct syntax for referring to the username parameter?

A. httpRequest.queryParameters.username
B. httpRequest.formData.username
C. httpRequest.queryParameters.users.username
D. httpRequest.users.username

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:
In Appian, when creating a Web API, parameters passed in the URL (e.g., query parameters) are accessed within the Web API expression using the httpRequest object. The URL https://exampleappiancloud.com/suite/webapi/user_management/users?username=john.smith includes a query parameter username with the value john.smith. Appian's Web API documentation specifies how to handle such parameters in the expression rule associated with the Web API.
Option D (httpRequest.queryParameters.username):
This is the correct syntax. The httpRequest.queryParameters object contains all query parameters from the URL. Since username is a single query parameter, you access it directly as httpRequest.queryParameters.username. This returns the value john.smith as a text string, which can then be used in the Web API logic (e.g., to query a user record). Appian's expression language treats query parameters as key-value pairs under queryParameters, making this the standard approach.
Option A (httpRequest.queryParameters.users.username):
This is incorrect. The users part suggests a nested structure (e.g., users as a parameter containing a username subfield), which does not match the URL. The URL only defines username as a top-level query parameter, not a nested object.
Option B (httpRequest.users.username):
This is invalid. The httpRequest object does not have a direct users property. Query parameters are accessed via queryParameters, and there's no indication of a users object in the URL or Appian's Web API model.
Option C (httpRequest.formData.username):
This is incorrect. The httpRequest.formData object is used for parameters passed in the body of a POST or PUT request (e.g., form submissions), not for query parameters in a GET request URL. Since the username is part of the query string (?username=john.smith), formData does not apply.
The correct syntax leverages Appian's standard handling of query parameters, ensuring the Web API can process the username value effectively.

NEW QUESTION # 47
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. name
B. priority
C. site_id
D. case_id
E. status
F. modified_date

Answer: B,C,E

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 # 48
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EXAM ACD301 DURATION | ACD301 EXAM FLASHCARDS

Exam ACD301 Duration | ACD301 Exam Flashcards