Free UiPath UiPath-SAIv1 Practice Exams

Question #1

What is the default visibility of an ML skill?

A . An ML skill is by default public and can be made private.
B . An ML skill is by default private and can be made public.
C . An ML skill is by default public and can’t be made private.
D . An ML skill is by default private and can’t be made public.

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Question #2

What is a mandatory requirement before using the One Click Classification functionality?

A . The Document Understanding project must be linked to Action Center.
B . The Document Understanding project must have at least five Document Types defined.
C . The Document Understanding project must be linked to AI Fabric.
D . The Document Understanding project must be linked to AI Center.

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Question #3

Which features in Generative Annotation are automatically enabled on datasets in Communication Mining technology?

A . Taxonomy Uploading
B . Assisted Labelling
C . Preview Mode
D . Sentiment Analysis

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Question #4

What components are part of the Document Understanding Process template?

A . Import. Classification. Text Extractor, and Data Validation.
B . Load Document. Categorization. Data Extraction, and Validation.
C . Load Taxonomy, Digitization. Classification, Data Extraction, and Data Validation Export.
D . Load Taxonomy, Digitization. Categorization. Data Validation, and Export.

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Correct Answer: C
C

Explanation:

The Document Understanding Process template is a fully functional UiPath Studio project template based on a document processing flowchart. It provides logging, exception handling, retry mechanisms, and all the methods that should be used in a Document Understanding workflow, out of the box. The template has an architecture decoupled from other connected automations and supports both attended and unattended processes with human-in-the-loop validation via Action Center. The template consists of the following components1:

Load Taxonomy: This component loads the taxonomy file that defines the document types and fields to be extracted. The taxonomy file can be created using the Taxonomy Manager in Studio or the Data Manager web application.

Digitization: This component converts the input document into a digital format that can be processed by the subsequent components. It uses the Digitize Document activity to perform OCR (optical character recognition) on the document and obtain a Document Object Model (DOM). Classification: This component determines the document type of the input document using the Classify Document Scope activity. It can use either a Keyword Based Classifier or a Machine Learning Classifier, depending on the configuration. The classification result is stored in a ClassificationResult variable.

Data Extraction: This component extracts the relevant data from the input document using the Data Extraction Scope activity. It can use different extractors for different document types, such as the Form Extractor, the Machine Learning Extractor, the Regex Based Extractor, or the Intelligent Form Extractor. The extraction result is stored in an ExtractionResult variable.

Data Validation: This component allows human validation and correction of the extracted data using the Present Validation Station activity. It opens the Validation Station window where the user can review and edit the extracted data, as well as provide feedback for retraining the classifiers and extractors. The validated data is stored in a DocumentValidationResult variable.

Export: This component exports the validated data to a desired output, such as an Excel file, a database, or a downstream process. It uses the Export Extraction Results activity to convert the DocumentValidationResult variable into a DataTable variable, which can then be manipulated or written using other activities.

References: Document Understanding Process: Studio Template, Document Understanding Process – New Studio Template, Document Understanding Process Template in UiPath Studio

Question #5

Which of the following is a best practice when choosing a UiPath ML (Machine Learning) Extractor?

A . The popularity of the ML Extractor among other UiPath users should be the primary factor when choosing a UiPath ML Extractor.
opt for the ML Extractor that has the highest number of downloads or positive reviews.
B . Consider the document types, language, and data quality when choosing an ML Extractor.
It is important to select one that is specifically trained or optimized for the document types being processed.
It is also important to take into account the quality and diversity of the training data used to train the ML Extractor to ensure accurate and reliable extraction results.
C . The cost of the ML Extractor should be the main consideration when choosing an ML Extractor. Select the ML Extractor that offers the lowest price, regardless of its performance or suitability for the specific document understanding needs.
D . The size of the ML Extractor is the most important factor to consider when choosing an ML Extractor.
Bigger models always perform better and provide more accurate extraction results because the development team invested time and effort into creating the algorithm, which in turn will result in better performance for the trained model.

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Question #6

What can the Custom Named Entity Recognition out-of-the-box model be used for?

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

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Question #7

In the UiPath Implementation Methodology, which phase involves building the SDD (Solution Design Document)?

A . Kick-off phase
B . Solution Design phase
C . Process Design phase
D . User Acceptance Testing phase

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Question #8

What is the recommended split of documents for training and evaluation, considering a total of 15 documents per vendor?

A . 7 documents for training the model, and 8 for evaluating the model.
B . 8 documents for training the model, and 7 for evaluating the model.
C . 10 documents for training the model, and 5 for evaluating the model.
D . 12 documents for training the model, and 3 for evaluating the model.

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Question #9

Which Reports tab do we use to get a high-level overview and statistics on all labels in UiPath Communications Mining?

A . Dashboards.
B . Label Summary.
C . Trends.
D . Segments.

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Question #10

What does the darker shading of a label prediction represent in Explore in UiPath Communications Mining?

A . An incorrect prediction.
B . A lower confidence score.
C . Multiple label predictions.
D . A higher confidence score.

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