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UiPath UiPath-SAIAv1 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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 4

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 5

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 6

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 7

Exception Handling: This section of the exam measures skills of RPA developers and covers structured error handling using Try Catch, Throw, Rethrow, and Retry Scope. It prepares the candidate to handle and resolve automation errors gracefully.

Topic 8

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 9

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 10

Email Automation: This section of the exam measures skills of RPA developers and covers automating email processes using Microsoft 365 and Gmail integrations. It focuses on sending, receiving, and managing emails as part of workflow automation.

Topic 11

UiPath Studio - Document Understanding Activities: This section of the exam measures skills of RPA developers and covers configuring document classification and extraction workflows using Studio activities, taxonomy management, digitization, and validation tools. It also includes the use of trained ML models and prebuilt extractors.

Topic 12

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 13

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.

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UiPath Specialized AI Associate Exam (2023.10) Sample Questions (Q192-Q197):

NEW QUESTION # 192
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't be made public.
C. An ML skill is by default private and can be made public.
D. An ML skill is by default public and can't be made private.

Answer: C

Explanation:
An ML skill is a consumer-ready, live deployment of an ML or OS package that can be used in RPA workflows. By default, an ML skill is private, which means it can only be accessed by the users who have the permission to view and manage the project that contains the skill. However, an ML skill can be made public by enabling the Public Skill option in the ML Skill Details page. This will generate a public URL and an API key for the skill, which can be used to access the skill from any external system or application12.
References: 1: AI Center - About ML Skills 2: Make ML Skills and Datasets public via URL + API Key - Preview

NEW QUESTION # 193
What is Document Understanding?

A. A solution for combining different approaches to extract entities from Word documents such as contracts and agreements.
B. A solution that offers the ability to digitize, extract, validate, and train data from documents.
C. A solution for the processing of Excel files for extracting data tables.
D. A solution for combining different approaches to extract information from workflows.

Answer: B

Explanation:
Document Understanding is a tool that helps you create and manage documents for your automation scenarios in the UiPath ecosystem. It allows you to process and extract data from multiple document types in an open, extensible, and versatile environment. The framework consists of components such as Taxonomy, Digitization, Data Extraction, Data Extraction Validation, Data Extraction Training, and Data Consumption, and enables you to customize and train your own algorithms12.
References: Intelligent Document Processing - Document Understanding | UiPath, Document Understanding - Introduction - UiPath Documentation Portal

NEW QUESTION # 194
What is the name of the web application that allows users to prepare, review, and make corrections to datasets required for Machine Learning models?

A. Document Manager.
B. Digitization.
C. Data Manager.
D. ML Extractor.

Answer: C

Explanation:
Data Manager is a web application that allows users to prepare, review, and make corrections to datasets required for Machine Learning models. Data Manager enables users to create and manage datasets, label data, validate and export data, and monitor data quality and progress. Data Manager supports various types of data, such as documents, images, text, and tables. Data Manager is integrated with AI Center, where users can train and deploy Machine Learning models using the datasets created or modified in Data Manager12.
References: 1: Data Manager Overview 2: AI Center - About Datasets

NEW QUESTION # 195
What are the options available in the Export Now tab of the Export Files dialog box in Document Manager?

A. Download to Excel. Export to Al Center, and Export All.
B. Download to Excel and Export All.
C. Download to Excel. Download, and Export to Al Center.
D. Export to Al Center, Export All. and Download.

Answer: A

Explanation:
Document Understanding documentation, when using the "Export Now" tab in the Export Files dialog box within the Document Manager, the available options include:
Download to Excel - This allows downloading the dataset locally in an Excel format.
Export to AI Center - This exports the data directly to the AI Center for use in model training or evaluation.
Export All - This option exports all documents regardless of labeling or filtering status.
These features are designed to facilitate exporting labeled data for further processing in AI Center, or locally for offline analysis

NEW QUESTION # 196
What does adding missed labels help improve in UiPath Communications Mining?

A. Increases data security.
B. Label precision and recall.
C. Increases the taxonomy coverage.
D. Label bias warnings.

Answer: B

Explanation:
Adding missed labels helps improve the label precision and recall in UiPath Communications Mining.
Precision is the percentage of correctly labeled verbatims out of all the verbatims that have the label applied, while recall is the percentage of correctly labeled verbatims out of all the verbatims that should have the label applied. By adding missed labels, you are increasing the recall of the label, as you are reducing the number of false negatives (verbatims that should have the label but do not). This also improves the precision of the label, as you are reducing the noise in the data and making the label more informative and consistent. Adding missed labels is one of the recommended actions that the platform suggests to improve the model rating and performance of the labels.
References: Communications Mining - Training using 'Check label' and 'Missed label', Communications Mining - Model Rating

NEW QUESTION # 197
......

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