QUESTION: 29

What is the difference between 'Add Transaction Item' activity and "Add Queue Item' activity?

The status of the queue item added will 'Add Transaction Item' is "New" The status of the queue item added with 'Add Queue Item' is "inProgress"
'Add Transact-on item" activity stores tie torn locally not in Orchestrate "Add Queue Item' activity adds the queue Mem to me Orchestrator Queue
The status of the queue item added with 'Add Transaction Item' is "InProgress" The status of the queue item added with 'Add Queue Item' is "New'
There a no difference between the two activities

Answer(s): C

Explanation:

According to the UiPath documentation1, the Add Transaction Item activity adds a new item in the queue and starts a transaction. The status of the item is set to InProgress. This means that the item is locked for processing by the current robot and cannot be retrieved by other robots until the transaction is completed or abandoned. The Add Transaction Item activity also returns the item as a QueueItem variable, which can be used to access its properties and data. The Add Queue Item activity adds a new item in an Orchestrator queue. The status of the item will be New. This means that the item is available for processing by any robot that uses the Get Transaction Item activity. The Add Queue Item activity does not return the item as a variable, but it allows setting its priority, reference, and deadline. Therefore, the correct answer is C. The status of the queue item added with `Add Transaction Item' is "InProgress" The status of the queue item added with `Add Queue Item' is

"New'.

Reference:

1: Queues and Transactions - UiPath Documentation Portal

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QUESTION: 30

How can a user effectively store and query data using Entity Records in UiPath Data Service tor RPA projects?

Store data in fixed Entity Records and leverage cloud storage services to manage queries and data manipulation.
Utilize pre-defined entities and fields to store new data, while relying on queries for data retrieval
Create new Entity Records to store data while using suitable queries to retrieve and manipulate existing records as needed.
Employ a smote query tor all operators including storage and retrieval to avoid complexity n handing Entity Records

Answer(s): C

Explanation:

UiPath Data Service is a cloud-based data platform that enables users to store and manage structured and relational data for their RPA projects. Entity Records are the basic units of data in Data Service, which consist of fields and values that represent a specific object or concept. Users can create custom Entity Records to store data that is relevant to their automation scenarios, such as customer information, order details, invoice data, etc. Users can also use suitable queries to retrieve and manipulate existing Entity Records as needed, such as filtering, sorting, aggregating, updating, or deleting data. Users can also leverage the relationships between Entity Records to access related data across different entities, such as joining, expanding, or embedding data.

Reference:

Data Service
- Entities - UiPath, Data Service - Queries - UiPath, Data Service - Relationships - UiPath

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QUESTION: 31

What are the differences between rule based and model based extractions?

The rub-based extraction uses methods like regex extractor and form extractor on semi-structured documents while the model based extraction uses the forma Al and machine learning on documents with fixed formal
The model-based extraction is used for documents with a fixed format, relies on regular expressions and templates and ensures high accuracy for already known documents The rule-based extraction is used for semi-structured documents and relies on pre-trained models as we" as on custom models
The rule-based extraction is used for documents with a fixed format relies on rules (like regular expressions) and templates and ensures high accuracy for already known documents The model- based extraction is used tor semi-structured documents and relies on pre-trained models (like invoices receipts purchase orders etc) as well as on custom models
The rule-based extraction uses methods like regex extractor and forms Al. on documents with a fared format, while the model-based extraction uses the machine learning extractor on semi structured documents

Answer(s): C

Explanation:

The rule-based extraction and the model-based extraction are two different methods of data extraction that target different types of documents. The rule-based extraction is suitable for structured documents that have a fixed format and layout, such as forms, tax returns, or certificates. This method relies on rules (such as regular expressions) and templates (such as position or occurrence patterns) to identify and extract the data of interest from the document. The rule-based extraction ensures high accuracy and speed for already known documents, but it requires manual configuration and maintenance of the rules and templates, and it cannot handle variations or changes in the document format. The model-based extraction is suitable for semi-structured documents that have varying formats and layouts, but contain similar types of information, such as invoices, receipts, or purchase orders. This method relies on pre-trained models (such as machine learning or artificial intelligence models) or custom models (such as user-defined models) to analyze and extract the data of interest from the document. The model-based extraction can handle variations and changes in the document format, and it can learn from feedback and improve over time, but it requires training data and validation, and it may not achieve the same level of accuracy and speed as the rule-based extraction for some documents.

Reference:

Data Extraction Overview - UiPath Document Understanding Document Processing with Improved Data Extraction | UiPath Document Understanding - Machine Learning Extractor - UiPath

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QUESTION: 32

Which of the following phases are part of the UiPath Automation Hub lifecycle?

Idea Qualification Assessment
Analysis Solution Design Tasks Documentation
Qualification, Development Process Map
Assessment Data Gathering Testing

Answer(s): A

Explanation:

UiPath Automation Hub is a cloud-based platform that helps organizations manage their automation pipeline, from ideation to deployment and maintenance. Automation Hub enables users to submit, evaluate, prioritize, and track automation ideas, as well as collaborate with other stakeholders and developers. The UiPath Automation Hub lifecycle consists of the following phases:
Idea: This is the initial phase where users can submit their automation ideas, either by filling out a form or by using the Task Capture tool to record their manual tasks. Users can also browse and vote for existing ideas, or provide feedback and comments.
Qualification: This is the phase where the automation ideas are assessed and validated by the automation experts, such as business analysts, solution architects, or automation sponsors. The qualification criteria include the feasibility, complexity, impact, and alignment of the automation idea with the business goals and strategy. The qualified ideas are then approved and moved to the next phase.
Assessment: This is the phase where the automation experts perform a detailed analysis of the automation idea, such as defining the scope, requirements, inputs, outputs, exceptions, risks, and dependencies. The assessment also involves estimating the effort, cost, and benefits of the automation, as well as creating a high-level solution design and a process map. The assessed ideas are then prioritized and assigned to the development team. Development: This is the phase where the developers use UiPath Studio and other tools to build, test, and debug the automation solution, following the best practices and standards. The development also involves creating the documentation, such as the technical specification document, the test cases, and the user guide. The developed automation is then deployed to the testing environment and moved to the next phase.
Testing: This is the phase where the automation solution is tested and validated by the quality assurance team, the business users, and the automation experts, using UiPath Test Suite and other tools. The testing involves verifying the functionality, performance, security, and compliance of the automation, as well as identifying and resolving any defects or issues. The tested automation is then deployed to the production environment and moved to the next phase. Maintenance: This is the final phase where the automation solution is monitored and maintained by the operations team, using UiPath Orchestrator and other tools. The maintenance involves ensuring the availability, reliability, and scalability of the automation, as well as performing any updates, enhancements, or fixes as needed. The maintenance also involves measuring and reporting the outcomes and benefits of the automation, as well as collecting feedback and suggestions for improvement.

Reference:

Automation Hub - Automation Pipeline Management | UiPath, Overview - Product Lifecycle - UiPath, Studio - Automation Lifecycle - UiPath.

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Free UiPath-ASAPv1 Exam Braindumps (page: 9)

QUESTION: 29

Explanation:

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QUESTION: 30

Explanation:

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QUESTION: 31

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QUESTION: 32

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