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UiPath UiPath-SAIAv1 Exam Syllabus Topics:

Topic
Details

Topic 1

Variables and Arguments: This section of the exam measures skills of automation analysts and covers the creation and management of variables and arguments. It introduces key data types and explains how to apply variables and arguments across workflows to pass, store, and manipulate data.

Topic 2

Working with Files and Folders: This section of the exam measures skills of automation analysts and covers creating and managing files and folders within local directories, including iteration and file manipulation using Studio activities.

Topic 3

UiPath Document Understanding: This section of the exam measures skills of RPA developers and covers the concepts and capabilities of UiPath Document Understanding, including processing various document types, understanding rule-based and ML-based extraction, and distinguishing DU from traditional OCR.

Topic 4

UiPath Document Understanding Framework: This section of the exam measures skills of automation analysts and covers how to apply the Document Understanding Framework, use templates, and develop proof-of-concept components. It focuses on building workflows for document processing.

Topic 5

Updates Introduced to 2023.10: This section of the exam measures skills of automation analysts and covers the most recent product updates in UiPath, including one-click classification and extraction, Generative AI features, and enhancements to validation, annotation, and workflow design.

Topic 6

UiPath Communications Mining - Taxonomy Design: This section of the exam measures skills of RPA developers and covers how to design a taxonomy for Communications Mining, enabling models to interpret and structure data effectively during classification and automation processes.

Topic 7

Business Knowledge: This section of the exam measures skills of automation analysts and covers the fundamental understanding of business process automation, its value in real-world operations, and essential concepts used to identify, map, and analyze business processes.

Topic 8

Control Flow: This section of the exam measures skills of RPA developers and covers debugging methods and logic handling in projects. It introduces the use of breakpoints, tracepoints, and debugging panels for managing and improving workflow execution.

Topic 9

Version Control Integration: This section of the exam measures skills of automation analysts and covers the use of Git integration in UiPath Studio for source control, including committing changes, cloning repositories, and pushing updates in collaborative environments.

Topic 10

UiPath Communications Mining - Model Training: This section of the exam measures skills of automation analysts and covers model training concepts in Communications Mining, explaining what defines a strong model and outlining the stages and components involved in developing one.

Topic 11

UiPath AI Center: This section of the exam measures skills of automation analysts and covers the basics of UiPath AI Center, its role in applying machine learning to automation, and the industries where AI models can be applied effectively.

Topic 12

Data Manipulation: This section of the exam measures skills of RPA developers and covers data handling with VB.Net string functions, RegEx patterns, arrays, lists, and dictionaries. It also covers DataTable operations such as building, filtering, and converting data for automation.

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UiPath Specialized AI Associate Exam (2023.10) Sample Questions (Q64-Q69):

NEW QUESTION # 64
What is the purpose of the "Explore" phase in UiPath Communications Mining?

A. To review the clusters of similar communications from a data set that unsupervised learning automatically found.
B. To use the bulk label functionality, a helpful tool to quickly train the model when searching for specific terms.
C. To provide each label/entity in a taxonomy with enough training examples so the model can make accurate predictions at scale.
D. To fully review and correctly tag the model version, regardless if it's "Live" or "Staging".

Answer: C

Explanation:
The Explore phase is the second phase of model training in UiPath Communications Mining, which is a solution that enables the analysis of large volumes of text-based communications using natural language processing and machine learning. The Explore phase builds on the foundations of the taxonomy that was created in the Discover phase by reviewing clusters and searching for different terms and phrases. The objective of the Explore phase is to provide each of the labels or entities thatare important for the use case with enough varied and consistent training examples, so that the platform has sufficient training data from which to make accurate predictions across the entire dataset. The Explore phase is the core phase of model training, and requires the most time and effort, but also leads to better model performance and accuracy1.
References: 1: Communications Mining - Explore

NEW QUESTION # 65
Which activity enables the identification of the document type by using any classifier?

A. Digitize Document activity.
B. Train Classifiers Scope activity.
C. Present Classification Station activity.
D. Classify Document Scope activity.

Answer: D

Explanation:
Reference: Classify Document Scope

NEW QUESTION # 66
Having the taxonomy in a file, shared and updated across multiple projects, what is the most convenient way to load it in a UiPath Studio project?

A. Using DocumentTaxonomy.Deserialize method inside an Assign activity.
B. Using Read Taxonomy activity.
C. Using DeserializeTaxonomy activity.
D. Using Load Taxonomy activity.

Answer: D

Explanation:
Reference: UiPath Load Taxonomy Activity

NEW QUESTION # 67
Which component from the image answers the question "Is the extracted information correct?"

A. Classify
B. Extract
C. Validate (Extract)
D. Train (Extract)

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
TheValidate (Extract)component is responsible for allowing a human toreview, correct, or approve the data extractedby the robot. It typically uses thePresent Validation Stationactivity in the workflow.
* This step ensures data quality before submission or processing.
* The extracted data is compared against what a human user confirms as accurate.
* UiPath Documentation Reference:Present Validation Station - UiPath Docs

NEW QUESTION # 68
Which of the following extractors can be used for Data Extraction Scope activity?

A. Intelligent Form Extractor, Machine Learning Extractor. Logic Extractor, and Regex Based Extractor.
B. Regex Based Extractor. Form Extractor. Intelligent Form Extractor, and Machine Learning Extractor.
C. Full Extractor. Machine Learning Extractor, Intelligent Form Extractor, and Regex Based Extractor.
D. Form Extractor Incremental Extractor Machine Learning Extractor and Intelligent Form Extractor

Answer: B

Explanation:
The Data Extraction Scope activity provides a scope for extractor activities, enabling you to configure them according to the document types defined in your taxonomy. The output of the activity is stored in an ExtractionResult variable, containing all automatically extracted data, and can be used as input for the Export Extraction Results activity. This activity also features a Configure Extractors wizard, which lets you specify exactly what fields from the document types defined in the taxonomy you want to extract1.
The extractors that can be used for Data Extraction Scope activity are:
Regex Based Extractor: This extractor enables you to use regular expressions to extract data from text documents. You can define your own expressions or use the predefined ones from the Regex Based Extractor Configuration wizard2.
Form Extractor: This extractor enables you to extract data from semi-structured documents, such as invoices, receipts, or purchase orders, based on the position and relative distance of the fields. You can define the templates for each document type using the Form Extractor Configuration wizard3.
Intelligent Form Extractor: This extractor enables you to extract data from semi-structured documents, such as invoices, receipts, or purchase orders, based on the labels and values of the fields. You can define the fields for each document type using the Intelligent Form Extractor Configuration wizard.
Machine Learning Extractor: This extractor enables you to extract data from any type of document, using a machine learning model that is trained on your data. You can use the predefined models from UiPath or your own custom models hosted on AI Center or other platforms. You can configure the fields and the model for each document type using the Machine Learning ExtractorConfiguration wizard.
References: 1: Data Extraction Scope 2: Regex Based Extractor 3: Form Extractor 4: Intelligent Form Extractor
5: Machine Learning Extractor

NEW QUESTION # 69
......

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