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UiPath Automation Solution Architect Professional v1.0 Sample Questions:

1. What is UiPath Apps?

A) A cloud platform for creating and managing RPA solutions.
B) An IDE to develop custom activities for UiPath Studio.
C) A system for managing digital files and content storage.
D) A low-node platform for building custom applications for automation.

2. What is the UiPath Document Understanding (DU) Process template purpose role?

A) To allow deploying a new Document Understanding process as an Unattended implementation already tailored to your business use case Further development is optional and only required when the business presents additional requirements. The testing debugging and scaling are easy and the template is bum with the community's best practices in mmd.
B) To allow a seamless start for any new Document Understanding process that is suitable for all use cases, production-ready has a common architecture tor both Attended and Unattended implementations makes development lasting deployment debugging, and scaling easy and follows the best practices pertaining to RPA Document Understanding Orchestration Processes and Long-running workflows
C) To allow one generic implementation to address all business needs without further development testing and debugging The DU template can be deployed immediately used as is without any performance concerns and following the best practices pertaining to RPA Document Understanding Orchestration Processes and Long-running workflows.
D) To allow deploying a new Document Understanding process as an Attended implementation already tailored to your business use case making the lasting debugging and scaling easy and following the community best practices II also makes development optional the template being ready for production deployment out of the box (needed only when the business comes with extraordinary requirements)

3. What is the difference between rule-based and model-based data extraction methodologies?

A) Rule-based extraction requires manual data labeling while model-based extraction does not need any label or tracing data
B) Model-based extraction is only effective for structured data sources whereas rule-based extraction can handle both structured and unstructured data sources seamlessly
C) Rule-based extraction is more computationally intensive whereas model based systems are simpler and require fewer computational resources
D) Rule-based extraction relies on predefined rules and patterns, while model-based extraction utilizes machine learning algorithms to automatically identify and extract data

4. Considering the following scenario:
We have a customer who has a on-premises installation of the Orchestration and wants to start a new project with Documents Understanding and ML. the project must be deployed into production in 6 weeks, but AI Center and Document Understanding are not available (not installed).
Considering the following
- any cloud solution must undergo an approval process that takes roughly 4 weeks for any production usage and 2 weeks for development lasting
- getting the hardware resources for an on-premise installation will take roughly 5 weeks and the costs are barely fitting the customer budget.
- the Orchestrator installation must remain unchanged
Which deployment option minimizes the risks for the process to reach production in time?

A) On-premise Mode (Orchestrator on-premise Al Center/Document Understanding Automation Suite installation)
B) Hybrid Mode (Orchestrate on premise Al Center Document Understanding in cloud)
C) Cloud Mode (Orchestrator, Al Center/Document Understanding in cloud)
D) On-premise Mode (Orchestrator on-premise Al Center Document Understating stand-alone installation)

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

A) 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
B) 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
C) 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
D) 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

Solutions: