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UiPath UiPath-SAIAv1 Exam Syllabus Topics:

Topic
Details

Topic 1

Studio Interface: This section of the exam measures skills of RPA developers and covers essential navigation and setup within UiPath Studio. It includes installing Studio, connecting to Orchestrator, navigating the interface, managing packages, configuring activity settings, and publishing processes to Orchestrator.

Topic 2

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 3

Implementation Methodology: This section of the exam measures skills of automation analysts and covers project lifecycle knowledge, understanding key stages of implementation, and interpreting Process Design Documents (PDDs) and Solution Design Documents (SDDs).

Topic 4

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 5

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 6

Platform Knowledge: This section of the exam measures skills of RPA developers and covers the high-level purpose and use of UiPath platform components, including Studio, Robots, Orchestrator, and Integration Service. It also explains the difference between attended and unattended processes, providing foundational knowledge of process deployment environments.

Topic 7

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 8

Orchestrator: This section of the exam measures skills of RPA developers and covers Orchestrator's structure and functionality, including entities at the tenant and folder level. It includes using assets, queues, storage buckets, and provisioning robots along with setting up roles and logging.

Topic 9

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 10

Debugging: This section of the exam measures skills of automation analysts and covers debugging within Document Understanding workflows. It explores the template’s architecture, exception handling, validation steps, and post-processing techniques that ensure accuracy and fault tolerance.

Topic 11

Logging: This section of the exam measures skills of automation analysts and covers interpretation of robot execution logs and the application of logging best practices to support auditability, diagnostics, and monitoring.

Topic 12

Environments, Applications, and
or Tools: This section of the exam measures skills of RPA developers and covers the candidate’s comfort level with common development tools, platforms, and environments such as Excel, Outlook, browsers, version control, Studio, Document Understanding Template, AI Center, and Communication Mining.

Topic 13

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 14

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.

Topic 15

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.

UiPath Specialized AI Associate Exam (2023.10) Sample Questions (Q57-Q62):

NEW QUESTION # 57
What are all the ways to deploy Al Center?

A. In cloud availability, on-premises air-gapped, on-premises online, hybrid mode (Al Center on-premise
+ Orchestrator on-premise). and Automation Suite.
B. In cloud availability, on-premises air-gapped, on-premises online, and hybrid mode {cloud Al Center + Orchestrator on-premise).
C. In cloud availability, on-premises, hybrid mode (Al Center on-premise + cloud Orchestrator), and Automation Suite.
D. In cloud availability, on-premises air-gapped, on-premises online, hybrid mode (cloud Al Center + Orchestrator on-premise). and Automation Suite.

Answer: A

Explanation:
UiPath AI Center can be deployed in multiple ways to meet different organizational needs and infrastructures.
The available deployment options include:
Cloud availability: Using UiPath's cloud services.
On-premises air-gapped: A fully isolated, offline environment for organizations with strict security requirements.
On-premises online: Deployed on-premise but with internet connectivity.
Hybrid mode: Combining on-premises AI Center with on-premises Orchestrator for flexibility.
Automation Suite: A comprehensive deployment of UiPath tools, including AI Center.
For more details, refer to:
UiPath AI Center Deployment Models: Deployment Options

NEW QUESTION # 58
Which validation checks are performed for ML packages uploaded with the Enable Training option inactive?

A. 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.
B. Existence of a requirements.txt file, and main.py file which implements a class Main. The class is further validated lo implement an__init__and a predict function.
C. Existence of a non-empty root folder, main.py file in the root folder which implements a dass Main.
The class is further validated to implement an__init__and a predict function.
D. Existence of a non-empty root folder, requlrements.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.

Answer: D

Explanation:
When uploading an ML package in UiPath AI Center with the Enable Training option inactive, several validation checks are performed:
There must be a non-empty root folder.
A requirements.txt file must be present to define dependencies.
A main.py file should be in the root folder, which implements a class Main.
The class must implement the necessary methods, such as __init__ and predict.
For more details, refer to:
UiPath AI Center Documentation: ML Package Validation

NEW QUESTION # 59
Which of the following extractors can be used for Data Extraction Scope activity?

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

Answer: D

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 # 60
What is the primary advantage of the One Click Extraction feature in UiPath's Document Understanding interface?

A. It automates the creation of document extractors by automatically creating Datasets, Pipelines, and ML Skills but requires manual creation of Pipelines.
B. It provides a simplified way to manually create Datasets, Pipelines, and Datasets for document extraction.
C. It allows training document extractors without manually creating Datasets and Pipelines, but you need to manually deploy the ML Skills in AI Center.
D. It allows training document extractors without manually creating Datasets, Pipelines, and ML Skills in AI Center.

Answer: D

Explanation:
Reference: UiPath One Click Extraction

NEW QUESTION # 61
On at least how many different pages should a regular field be labeled in Data Manager before Exporting the labeled documents to Al Center?

A. 0
B. 1
C. 2
D. 3

Answer: D

Explanation:
To ensure the model is trained effectively, UiPath recommends that regular fields be labeled on at least 10 different pages in Data Manager before exporting the labeled documents to AI Center. This helps in providing enough variation and examples for the model to learn from and generalize effectively.

NEW QUESTION # 62
......

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Ron Reed Ron Reed

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