Updated Jan 16, 2024 Certification Exam ACD300 Dumps - Practice Test Questions
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NEW QUESTION # 14
You ate in a backlog refinement meeting with the development team and the product owner. You review a story for an integration Involving a third-party system. A payload will be sent from the Appian system through the integration to the third-party system. The story is 21 points on a Fibonacci scale, and requires development from your Appian learn, as well as the technical resources from the third-party system. This item is crucial to your project s success.
What are the two recommended steps to ensure this story can be developed effectively?
- A. Maintain a communication schedule with the third-party resources
- B. Identify subject matter experts (SMEs) to perform user acceptance testing (UAT)
- C. Break down the item into smaller stones
- D. Acquire testing steps from QA resources
Answer: A,C
Explanation:
Explanation
To ensure that this story can be developed effectively, you should take two recommended steps:
* Maintain a communication schedule with the third-party resources. Communication is key when working on an integration involving a third-party system, as it can help to clarify the requirements, expectations, and dependencies of both parties. By maintaining a communication schedule, you can ensure that you have regular and timely updates on the progress, issues, and feedback of the integration.
You can also use communication tools, such as email, chat, or video conferencing, to facilitate the communication and collaboration between your Appian team and the third-party resources.
* Break down the item into smaller stories. Breaking down a large and complex story into smaller and simpler stories can help to make the development process more manageable and efficient. By breaking down the item into smaller stories, you can reduce the scope and complexity of each story, and focus on delivering one feature or functionality at a time. You can also prioritize and assign the stories to different developers, and track their status and completion more easily.
The other options are not as effective. Option A, acquiring testing steps from QA resources, is not a step to ensure that the story can be developed effectively, but rather a step to ensure that the story can be tested effectively. Option B, identifying subject matter experts (SMEs) to perform user acceptance testing (UAT), is also not a step to ensure that the story can be developed effectively, but rather a step to ensure that the story can be validated effectively. Option E, adding a view that joins the customer data to the data used in calculation, is not a step to ensure that the story can be developed effectively, but rather a design decision that may or may not be appropriate for the integration.
NEW QUESTION # 15
Review the following resultof an explain statement:
Which two conclusions can you draw from this?
- A. The request is good enough to support a high volume of data. but could demonstrate some limitations if the developer queries information related to the product
- B. The join between the tables order_detail, order and customerneeds to be tine-tuned due to indices.
- C. The join between the tables 0rder_detail and productneeds to be fine-tuned due to Indices
- D. The worst join isthe one between the table order_detail and order.
- E. The worst join is the one between the table order_detail and customer
Answer: C,E
Explanation:
Explanation
* D. The join between the tables order_detail and product needs to be fine-tuned due to Indices. This is correct because the result of the explain statement showsthat the join between these two tables has a high cost of 0.99, which indicates that it is inefficient and needs to be fine-tuned. One possible reason for the high cost is that there are no indices on the columns that are used for joining these two tables, which leads to a full table scan. Therefore, creating indices on these columns could improve the performance of this join.
* E. The worst join is the one between the table order_detail and customer. This is correct because the result of the explain statement shows that the join between these two tables has a very high cost of 1.00, which indicates that it is the worst join in terms of efficiency and needs to be fine-tuned. One possible reason for the high cost is that there are no indices on the columns that are used for joining these two tables, which leads to a full table scan. Therefore, creating indices on these columns could improve the performance of this join.
The other options are incorrect for the following reasons:
* A. The request is good enough to support a high volume of data, but could demonstrate some limitations if the developer queries information related to the product. This is incorrect because the request is not good enough to support a high volume of data, as it has two joins with very high costs that need to be fine-tuned. Moreover, querying information related to the product would not necessarily cause any limitations, as long as the join between order_detail and product is optimized.
* B. The worst join is the one between the table order_detail and order. This is incorrect because the result of the explain statement shows that the join between these two tables has a low cost of 0.01, which indicates that it is efficient and does not need to be fine-tuned.
* C. The join between the tables order_detail, order and customer needs to be fine-tuned due to indices.
This is incorrect because there is no such join between three tables in the result of the explain statement.
There are only two joins: one between order_detail and order, and another between order_detail and customer. Each of these joins needs to be fine-tuned separately due to indices.
NEW QUESTION # 16
You have an active development team (Team A) building enhancements for an application (App X'). and ate currently using the TEST environment for UAT.
A separate operations team ('Team B) discovers a critical error in the Production instance of App X that they must remediate However. Team 6 does not have a hotfix stream for which to accomplish this The available environments are DEV. TEST, and PROD Which risk mitigation effort should both teams employ to ensure Team AS capital project is only minorly interrupted, and Team B s critical fix can be completed and deployed quickly to end users?
- A. Team 8 must address the changes directly in PROD. As there is no hotfix stream, and OEV and TEST are being utilized for active development it is best to avoid a conflict of components. Once Team A has completed their enhancements work. Team 6 can update DEV and TEST accordingly.
- B. Team 8 must address changes in the TEST environment These changes can then be tested and deployed directly to PROD. Once the deployment is complete. Team B can then communicate their changes to Teams to ensure they are Incorporated as a part of the next release.
- C. Team A must analyze their current codebase in OEV lo merge the hotfix changes into their latest enhancements. Team B is then requited to wait for the hotfix to follow regular deployment protocols from DEV to the PROO environment.
- D. Team 8 must communicate to Team A which component will be addressed in the hotfix to avoid overlap of changes It overlap exists, the component must be versioned to its PROD state before being remediated and deployed, and then versioned back to its latest development state If overlap does not exist, the component may be remediated and deployed without any version changes
Answer: D
Explanation:
Explanation
This is the best risk mitigation effort that both teams can employ to ensure that Team A's capital project is only minorly interrupted, and Team B's critical fix can be completed and deployed quickly to end users. By communicating with Team A, Team B can identify which component is causing the critical error in PROD, and check if there is any overlap of changes with Team A's enhancements. If there is an overlap, Team B can version the component to its PROD state, which is the last stable version, before making any changes to fix the error. Then, Team B can deploy the fixed component to PROD, and version it back to its latest development state, which includes Team A's enhancements. This way, Team B can avoid overwriting or losing any of Team A's work, and ensure that the component is consistent across all environments. If there is no overlap, Team B can simply make the changes to the component and deploy it to PROD, without affecting Team A's work.
The other options are not as effective. Option B, having Team A analyze their current codebase in DEV to merge the hotfix changes into their latest enhancements, would delay the deployment of the critical fix, as Team B would have to wait for Team A to finish their analysis and merge. Option C, having Team B address the changes in TEST, would interrupt Team A's UAT process, and could cause conflicts or errors in TEST or PROD. Option D, having Team B address the changes directly in PROD, would be risky and not recommended, as it could introduce new errors or inconsistencies in PROD.
Verified References: [Appian Deployment Guide], [Appian Best Practices]
NEW QUESTION # 17
You add an index on the searched field of a MySQL table with many rows (>100k).
The field would benefit greatly from the Index in which three scenarios?
- A. The field contains a textual shot Business code.
- B. The Add contains Dig integers, above and below 0.
- C. The field contains a structured JSON.
- D. The field contains many datetimes, covering a large range
- E. The field contains long unstructured text such as a hash
Answer: A,B,D
Explanation:
Explanation
The field would benefit greatly from the index in the following scenarios:
* A. The field contains a textual short Business code. This is a scenario where an index can improve the performance of queries that search for exact matches or ranges of values in the field. A textual short Business code is likely to have high cardinality, meaning that it has many distinct values and low duplication. This makes the index more selective and efficient, as it can quickly narrow down the results based on the search criteria.
* C. The field contains many datetimes, covering a large range. This is a scenario where an index can improve the performance of queries that search for exact matches or ranges of values in the field. A datetime field is likely to have high cardinality, meaning that it has many distinct values and low duplication. This makes the index more selective and efficient, as it can quickly narrow down the results based on the search criteria.
* D. The field contains big integers, above and below 0. This is a scenario where an index can improve the performance of queries that search for exact matches or ranges of values in the field. A big integer field is likely to have high cardinality, meaning that it has many distinct values and low duplication. This makes the index more selective and efficient, as it can quickly narrow down the results based on the search criteria.
The other options are incorrect for the following reasons:
* B. The field contains long unstructured text such as a hash. This is a scenario where an index might not improve the performance of queries that search for exact matches or ranges of values in the field. A long unstructured text field is likely to have low cardinality, meaning that it has few distinct values and high duplication. This makes the index less selective and efficient, as it cannot quickly narrow down the results based on the search criteria. Moreover, indexing a long unstructured text field could increase thestorage space and maintenance cost for the database, which could affect the overall performance.
* E. The field contains a structured JSON. This is a scenario where an index might not improve the performance of queries that search for exact matches or ranges of values in the field. A structured JSON field is not a native data type in MySQL, and it requires special functions or operators to access or manipulate its elements. Indexing a structured JSON field could increase the complexity and overhead for the database, which could affect the overall performance. Verified References: Appian Documentation, section "Query Optimization".
NEW QUESTION # 18
You are taskedto build a large scale acquisition application for a prominent customer. The acquisition process tracks the time it takes is fulfill a purchase request with an award.
The customer has structured the contract so that there are multiple application dev teams.
How should you design for multiple processes and forms, while minimizing repeated code?
- A. Create duplicate processes and forms as needed
- B. Create a Scrum of Scrums sprint meeting for the team leads
- C. Create a Center of Excellence (CoE)
- D. Create a common objects application.
Answer: D
Explanation:
Explanation
To build a large scale acquisition application for a prominent customer, you should design for multiple processes and forms, while minimizing repeated code. One way to do this is to create a common objects application, which is a shared application that contains reusable components, such as rules, constants, interfaces, integrations, or data types, that can be used by multiple applications. This way, you can avoid duplication and inconsistency of code, and make it easier to maintain and update your applications. You can also use the common objects application to define common standards and best practices for your application development teams, such as naming conventions, coding styles, or documentation guidelines. Verified References: [Appian Best Practices], [Appian Design Guidance]
NEW QUESTION # 19
Your application contains a process model that Is scheduled to run daily at a certain time, which kicks off a user input task to a specified user on the 1ST time zone for morning data collection The time zone is set to the (default) pm!timezone.
In this situation, what does the pm!tinezone reflect?
- A. The default time zone for the environment as specified in the Administration Console
- B. The time zone of the server where Applan is intuited
- C. The time zone of the user who is completing the input task.
- D. The line zone of the user who most recently published the process model
Answer: A
Explanation:
Explanation
In this situation, pm!timezone reflects the default time zone for the environment as specified in the Administration Console. pm!timezone is a process variable that returns the time zone of the process. If the time zone is not explicitly set in the process model, then pm!timezone returns the default time zone for the environment, which can be configured in the Administration Console. In this case, the time zone is set to the (default) pm!timezone, which means that the process model does not have a specific time zone, and therefore uses the default time zone for the environment.
The other options are not correct. Option A, the time zone of the server where Appian is installed, is not what pm!timezone reflects, as the server time zone may not be the same as the default time zone for the environment. Option B, the time zone of the user who most recently published the process model, is not what pm!timezone reflects, as the user's time zone may not be the same as the default time zone for the environment. Option D, the time zone of the user who is completing the input task, is not what pm!timezone reflects, as the user's time zone may not be the same as the default time zone for the environment.
NEW QUESTION # 20
You are taking your package from the source environment and importing it into the target environment.
Review the errors encountered during inspection:
Whatis the first action you should take to Investigate the issue?
- A. Check whether the object(UUD ending in 7t00000i4e7a)is included in this package
- B. Check whether the object (UUID ending in 18028931) is included in this package
- C. Check whether the object (UUID ending in 25606) is included in this package
- D. Check whether the object(UUIDending in 18028821) is included in this package
Answer: A
Explanation:
Explanation
The error message indicates that the object with UUID ending in 18028821 has a dependency on another object with UUID ending in 7t00000i4e7a, which is missing from the target environment. Therefore, the first action to investigate the issue is to check whether the object with UUID ending in 7t00000i4e7a is included in this package or not. If not, then it should be added to the package or imported separately before importing the current package. Verified References: Appian Certified Lead Developer study guide, page 17, section
"Importing and Exporting Applications".
NEW QUESTION # 21
You are just starting with a new team that has been working together on an application for months. They ask you toreview some of their views thathave been degrading inperformance. The viewsare highly complex with hundreds of lines of SOL What is the first step in troubleshooting the degradation?
- A. Run an explain statement on the views, identify criticalareas of improvement that can be remediated and without business knowledge
- B. Browse through the tables, note any tables that contain a large volume of null values, and work with your team to plan for table restructure.
- C. Go through all of the tables one by one to identify which of the grouped by. ordered by. or joined keys are currently indexed
- D. Go through the entire database structure to obtain on overview, ensure you understand the business needs, andthen normalize the tables to optimizeperformance.
Answer: A
Explanation:
Explanation
The first step in troubleshooting the degradation of the views is to run an explain statement on the views, identify critical areas of improvement that can be remediated without business knowledge. An explain statement is a tool that shows how a database executes a query or a view, and provides information about the cost, plan, and steps involved in the execution. By running an explain statement on the views, you can identify any inefficiencies or bottlenecks that are causing the degradation, such as missing indices, full table scans, nested loops, or hash joins. You can then apply some basic optimization techniques that do not require business knowledge, such as creating indices, limiting the number of columns or rows returned, using joins instead of subqueries, or using materialized views. Verified References: Appian Documentation, section
"Query Optimization".
NEW QUESTION # 22
You need to export data using an out-of-the-box Appian smart service. Which two formats are available (or data generation?
- A. XML
- B. Excel
- C. CSV
- D. JSDN
Answer: B,C
Explanation:
Explanation
The two formats that are available for data generation using an out-of-the-box Appian smart service are:
* A. CSV. This is a comma-separated values format that can be used to export data in a tabular form, such as records, reports, or grids. CSV files can be easily opened and manipulated by spreadsheet applications such as Excel or Google Sheets.
* C. Excel. This is a format that can be used to export data in a spreadsheet form, with multiple worksheets, formatting, formulas, charts, and other features. Excel files can be opened by Excel or other compatible applications.
The other options are incorrect for the following reasons:
* B. XML. This is a format that can be used to export data in a hierarchical form, using tags and attributes to define the structure and content of the data. XML files can be opened by text editors or XML parsers, but they are not supported by the out-of-the-box Appian smart service for data generation.
* D. JSON. This is a format that can be used to export data in a structured form, using objects and arrays to represent the data. JSON files can be opened by text editors or JSON parsers, but they are not supported by the out-of-the-box Appian smart service for data generation. Verified References: Appian Documentation, section "Write to Data Store Entity" and "Write to Multiple Data Store Entities".
NEW QUESTION # 23
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.
Answer:
Explanation:
Explanation
Requirement: Archive processes 2 days after completion or cancellation. Correct match: A. Processes that need to be available for 2 days after completion or cancellation, after which are no longer required nor accessible Exact explanation of correct match taken from Appian Documentation: This strategy is called
"Archive after 2 days" and it is one of the options for process model data management in Appian. This strategy means that processes that complete or cancel will remain available for 2 days, after which they will be archived and no longer accessible. This strategy can help reduce the size of the process database and improve the performance of process reporting.
Requirement: Use system default (currently auto-archive processes 7 days after completion or cancellation).
Correct match: C. Processes that remain available for 7 days after completion or cancellation, after which are archived when accessed Exact explanation of correct match taken from Appian Documentation: This strategy is called "Use system default" and it is one of the options for process model data management in Appian. This strategy means that processes that complete or cancel will remain available for 7 days, after which they will be archived when accessed. This strategy can help balance the availability and performance of process data, as it allows processes to be archived on demand rather than on a fixed schedule.
Requirement: Delete processes 2 days after completion or cancellation. Correct match: B. Processes that need to be available for 2 days after completion or cancellation, after which remain accessible Exact explanation of correct match taken from Appian Documentation: This strategy is called "Delete after 2 days" and it is one of the options for process model data management in Appian. This strategy means that processes that complete or cancel will remain available for 2 days, after which they will be deleted and no longer accessible. This strategy can help reduce the size of the process database and improve the performance of process reporting, but it also means that process data will be permanently lost.
Requirement: Do not automatically clean-up processes. Correct match: D. Processes that need to remain available without the need to unarchive Exact explanation of correct match taken from Appian Documentation: This strategy is called "Do not automatically clean-up" and it is one of the options for process model data management in Appian. This strategy means that processes that complete or cancel will remain available indefinitely without being archived or deleted. This strategy can help ensure the availability and integrity of process data, but it also means that the process database will grow over time and affect the performance of process reporting.
NEW QUESTION # 24
You are designing a process that is anticipated to be executed multiple times a day. This process retrieves data from an external system and then calls various utility processes as needed. The mam process will not use the results of the utility processes, and there are no user forms anywhere.
Which design choice should be used to start the utility processes and minimize the load on the execution engines?
- A. Use Process Messaging lo star! the utility process.
- B. Start the utility processes via a subprocess synchronously.
- C. Use the Start Process Smart Service to start the utility processes.
- D. Start the utility processes via a subprocess asynchronously
Answer: A
Explanation:
Explanation
To design a process that is anticipated to be executed multiple times a day, that retrieves data from an external system and then calls various utility processes as needed, you should use Process Messaging to start the utility process and minimize the load on the execution engines. Process Messaging is a feature that allows you to send and receive messages between processes in Appian. By using Process Messaging, you can start the utility process asynchronously, which means that the main process does not have to wait for the utility process to finish before continuing. This way, you can improve the performance and scalability of your process design, and reduce the load on the execution engines.
The other options are not as effective. Option A, using the Start Process Smart Service to start the utility processes, would also start the utility process asynchronously, but it would require more configuration and maintenance than Process Messaging. Option B, starting the utility processes via a subprocess synchronously, would start the utility process as a part of the main process flow, which means that the main process would have to wait for the utility process to finish before continuing. This would reduce the performance and scalability of your process design, and increase the load on the execution engines. Option D, starting the utility processes via a subprocess asynchronously, would also start the utility process as a part of the main process flow,but it would not wait for the utility process to finish before continuing. However, this option would still create more overhead than Process Messaging, as it would create more instances of processes in Appian.
NEW QUESTION # 25
You are planning a strategy around data volume testing for an Appian application that queries and writes to 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?
- A. The amount of data that needs to be populated should be determined by the project sponsor and the stakeholders based on their estimation
- B. Data from previous tests needs to remain in the testing environment prior to loading prepopulated data
- C. large datasets must be loaded via Applan processes
- D. Data model changes must wait until towards the end of the protect.
- E. Testing with the correct amount of data should be in the definition of done as part of each sprint.
Answer: D,E
Explanation:
Explanation
When designing a data volume testing strategy for an Appian application that queries and writes to MySQL database, you should consider two key considerations:
* Testing with the correct amount of data should be in the definition of done as part of each sprint. Data volume testing is a type of testing that verifies how well an application performs when handling large amounts of data. Data volume testing is important to ensure that the application meets the performance and quality requirements of the users and stakeholders. By including data volume testing in the definition of done as part of each sprint, you can ensure that each feature or functionality of your application is tested with realistic data volumes before being delivered to production. This way, you can identify and resolve any potential issues or bottlenecks early in the development cycle, and avoid any surprises or delays later on.
* Data model changes must wait until towards the end of the project. Data model changes are changes that affect the structure or schema of your database, such as adding, modifying, or deleting tables, columns, indexes, or constraints. Data model changes are risky and costly to make, especially when dealing with large amounts of data. Data model changes can affect the performance, functionality, or integrity of your
* application and database. Therefore, data model changes must wait until towards the end of the project, when you have finalized your requirements and design decisions, and have minimized your data volume testing efforts. By waiting until towards the end of the project to make data model changes, you can reduce the impact and complexity of those changes, and avoid any unnecessary rework or regression.
The other options are not as effective. Option A, data from previous tests needs to remain in the testing environment prior to loading prepopulated data, is not a key consideration for designing a data volume testing strategy, but rather a best practice for preparing your testing environment. Option B, large datasets must be loaded via Appian processes, is not a key consideration for designing a data volume testing strategy, but rather a technical implementation detail that may or may not be suitable for your application. 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, is not a key consideration for designing a data volume testing strategy, but rather an input or assumption that you need to validate before conducting your data volume testing.
NEW QUESTION # 26
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. status
- C. modified_date
- D. case_id
- E. site_id
- F. priority
Answer: B,E,F
Explanation:
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
NEW QUESTION # 27
You are running an inspection as a part of the first deployment process from TEST to PROD. You receive a notice that one of your objects will not deploy because it is dependent on an object from an application owned by a separate team.
What should be your next step?
- A. Create your own object with the same code base, replace (he dependent object in the application. and deploy to PROO.
- B. Halt the production deployment and contact the other team tor guidance on promoting the object to PROD
- C. Check the dependencies of the necessary object Deploy w PROO if there are few dependencies and it is low risk
- D. Push a functionally viable package to PROD without the dependencies, and plan the rest o! the deployment accordingly with the other team's constraints
Answer: B
Explanation:
Explanation
Deploying an object that is dependent on another object from a different application can cause errors and inconsistencies in the production environment. The best practice is to halt the production deployment and contact the other team for guidance on how to promote the object to PROD. The other team may have a different deployment schedule, or they may have some dependencies or customizations that need to be considered. By communicating with the other team, you can ensure that the object is deployed in a safe and coordinated manner, and avoid any potential conflicts or issues. Verified References: [Appian Deployment Guide], [Appian Best Practices]
NEW QUESTION # 28
You are reviewing the Engine Performance Logs in Production for a single application thathas 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 notifications 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?
- A. Optimize slow-performing user interfaces.
- B. Add more application servers.
- C. Add execution and analytics shards
- D. Add more engine replicas.
Answer: D
Explanation:
Explanation
Adding more engine replicas will increase the number of threads available to execute unattended process activities, such as timer events and sending notification emails. This will reduce the Java Work Queue Size and improve the performance of the application. Verified References: Appian Engine Performance Logs, Appian Engine Configuration
NEW QUESTION # 29
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