Free UISAIV1 – Uipath UISAIV1 Practice Test Question

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• ○ The number of occurrences and the extraction confidence.
• ○ The page number from which the field was extracted and the exact position on the page.
• ○ The extraction confidence and the digitization confidence.
• ○ The position on the page.

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• ○ Tesseract.
• ○ Microsoft Azure OCR.
• ○ OmniPage.
• ○ Microsoft OCR.

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• ○ Enables the deployment, management, and improvement of ML models on UiPath Automation Cloud, and requires no infrastructure and no maintenance.
• ○ Enables the deployment, management, and improvement of ML models locally, and requires manual download of all the resources and then loading them into the node.
• ○ Enables the deployment of the full UiPath Automation Platform in the environment of choice and contains everything in one package that can be deployed in multi-node mode with automatic scaling and built-in HA, monitor, configure, and upgrade.
• ○ Enables the deployment, management, and improvement of ML models locally with easy installation due to the automatic retrieval of the installer and associated artifacts from the internet.

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• ○ For a given concept what % of cases will the model incorrectly predict.
• ○ For a given concept, what % of cases will the model not detect.
• ○ For a given concept what % of cases will the model correctly predict.
• ○ For a given concept, what % of cases will the model detect.

Answer is in the explanation below.

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• ○ Using the AI Center IDE (Integrated Development Environment).
• ○ Using the AI Center model builder.
• ○ Using a Python IDE (Integrated Development Environment) or an AutoML platform.
• ○ Using a C/C++ IDE (Integrated Development Environment), then upload the code to Al Center IDE.

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• ○ Minimum 10% per label.
• ○ Maximum 12 per label.
• ○ Minimum 12 per label.
• ○ Maximum 10% per label.

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• ○ Existence of a non-empty root folder, requirements.txt file, and main.py file in the root folder which implements a class Main. The class is further validated to implement an __init__ and a predict function.
• ○ Existence of a requirements.txt file, and main.py file which implements a class Main. The class is further validated to implement an __init__ and a predict function.
• ○ Existence of a non-empty root folder, main.py file in the root folder which implements a class Main. The class is further validated to implement an __init__ and a predict function.
• ○ Existence of a non-empty root folder, requirements.txt file, and train.py file in the root folder which implements a class Main. The class is further validated to implement an __init__ function.

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• ○ When comments are uploaded or updated on the platform.
• ○ When model training occurs.
• ○ When using the automation framework to consume predictions from a stream.
• ○ When pinning a new model version.

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• ○ Pipeline Events.
• ○ ML Package Validation Events.
• ○ ML Skill Deployment Events.
• ○ Dataset Events.

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• ○ Understand sentiment in product reviews, customer surveys, social media posts, and emails.
• ○ Relate customer questions to FAQ documents and automatically pull responses from these documents.
• ○ Classify text in resumes, emails, web pages, and other formats.
• ○ Extract and classify text in emails, letters, web pages, research papers, and call transcripts.

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• ○ To evaluate the ML model.
• ○ To retrain the ML model.
• ○ To keep track of corrections done on the extracted data.
• ○ To keep track of the number of documents that require human validation.

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• ○ By adding them when creating the dataset or later from the dataset settings page
• ○ By creating a new model or editing an existing model version
• ○ By creating a new data source or editing an existing data source
• ○ By creating a new project or editing an existing project

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• ○ Escalation
• ○ Status Update
• ○ Out of Office
• ○ Root Cause

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• ○ Data labeling session name and dataset.
• ○ Data labeling name, dataset, and AI Center project.
• ○ Dataset and AI Center project.
• ○ Data labeling name, language, and number of documents.

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• ○ Unassigned, Assigned, Completed
• ○ Unassigned, Pending, Edited, Completed
• ○ Unassigned, Assigned, Modified, Completed
• ○ Unassigned, Pending, Completed

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• ○ Re-executes the activity which threw an exception.
• ○ Executes activities in the current container and then pauses the execution.
• ○ Executes only one activity at a time and then pauses the execution.
• ○ Steps out and stops current execution.

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• ○ Immediate Panel and Watch Panel.
• ○ Locals Panel and Watch Panel.
• ○ Locals Panel and Immediate Panel.
• ○ Watch Panel and Breakpoint Panel.