UiPath-ASAPv1 Exam Dumps - UiPath Certified Professional - Developer Track Questions and Answers

 Personal Identifiable Information (PII) is any data that potentially identifies a specific individual, allows for distinctions to be made between individuals, or could be used to de-anonymize anonymous data1. PII should be handled with care and protected from unauthorized access or disclosure. UiPath Orchestrator Queues are a way to store and process data items for automation projects. Queues have two types of data: Reference and Specific Content. Reference is a unique identifier for the data item, while Specific Content is a collection of key-value pairs that store the actual data2. To store PII in UiPath Orchestrator Queues, the best option is to use Specific Content and enable the “Store in encrypted format” option. This option encrypts the data using AES-256 encryption and prevents anyone, including database administrators, from viewing the data in plain text. Only the robots that have access to the queue can decrypt and process the data3. This ensures the security and privacy of the PII data.

References: About Queues and Transactions, Managing Queues in Studio, UiPath for Compliance, a GDPR Automation Solution

 According to the image, ‘E3L’ is a component that connects the Storage Bucket and the SME Validation Action. This implies that ‘E3L’ is responsible for retrieving the documents from the storage and sending them to the SMEs for validation. This is an example of a Human in the Loop (HITL) scenario, where human intervention is required to complete the automation process. Therefore, ‘E3L’ can be considered as a Human in the Loop Performer, which is a role that enables human interaction with the automation solution1.

References: Human in the Loop

Named user accounts are used to identify a person who can access UiPath products and services, and can be assigned roles, licenses, and groups. User Robot accounts are used to run unattended processes that do not require human interaction, and can also be assigned permissions and groups1.

References: About accounts and groups

 In a long-running context, a job can be suspended when it is waiting for human validation or intervention. This means that the job is paused until a human user performs an action, such as approving or rejecting a document, providing some input, or resolving an exception. A suspended job can be resumed by the user or by the orchestrator, depending on the configuration of the process. A suspended job is different from a stopping or stopped job, which means that the job is being terminated or has been terminated by the user or by the orchestrator. A suspended job is also different from a running job, which means that the job is executing normally without any interruption or delay. References: Long-Running Workflows - UiPath Documentation Portal, Managing Jobs - UiPath Documentation Portal, Long Running Workflow - UiPath Activities

The development time for process development depends on various factors, such as the scope, requirements, design, testing, deployment, and maintenance of the automation solution. However, two of the most important factors are the number of actions to be automated and the complexity of the architecture. The number of actions to be automated refers to the number of steps, activities, or tasks that the automation solution has to perform to achieve the desired outcome. The complexity of the architecture refers to the level of difficulty, integration, and customization involved in designing and implementing the automation solution. Both of these factors affect the amount of effort, resources, and skills required for the process development12.

References: Roles of a Solution Architect in Automation, RPA Setting, Become an Automation Solution Architect

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. References:

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

According to the UiPath documentation, making an ML Skill public allows it to be accessible via an endpoint from outside of UiPath environment. This means that you can call it without the need to go through a robot connected to the specific tenant1. To do that, you need to select Modify current deployment in the ML Skill Details page > Update skill window, and enable the Public skill option. This will generate an API Key and URL that you can use to connect to the ML Skill from a different tenant1.

References:

• AI Center - Managing ML Skills
• What are the steps in moving a ML Skill to a new Tenant?
• AI Center ML Skill export question
• UiPath tenant move for AI Center

UiPath Automation Cloud is a cloud-based enterprise automation solution that lets you start and scale your automation from the cloud with SaaS robots and self-hosted robots. It offers security, compliance, governance, and insights for your RPA initiatives. It does not require manual installation and maintenance, as it is delivered as a service. It does not have single or multi-node deployment options, as it is hosted on UiPath’s infrastructure. It does not provide major updates twice yearly, as it is updated continuously with new features and improvements.

References:

• Complete cloud-based enterprise automation solution | UiPath
• Automation Cloud - UiPath Documentation Portal
• Automation Cloud - About Automation Cloud - UiPath Documentation Portal

An Orchestration Process is a type of long-running workflow that supports service orchestration, human intervention, and long-running transactions in unattended environments1. An Orchestration Process can use persistence points, such as Wait for Queue Item and Resume or Wait for Job and Resume, to suspend and resume the execution based on certain conditions1. There is no limit on the number of jobs or other triggers that a job can wait for at a time, as long as there are enough runtimes available on the host machine2. However, if the number of pending jobs exceeds 10,000, a warning message is displayed in Orchestrator3.

References: 1: Studio - Orchestration Process 2: Orchestrator - About Jobs 3: Maximum number of pending jobs - Orchestrator

Storage buckets provide a per-folder storage solution for RPA developers to leverage in creating automation projects1. In the diagram, the Storage Bucket is used for updating information and uploading/moving unprocessed files. This is related to the classification data that is created and validated in the Action Center by the SME (Subject Matter Expert). The classification data is used to train the machine learning model that extracts data from the documents2.

References:

• About Storage Buckets
• UiPath Apps (Storage Bucket). Introduction