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UiPath-SAIAv1 Practice Exam Online | Latest UiPath-SAIAv1 Version

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UiPath UiPath-SAIAv1 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

Workflow Analyzer: This section of the exam measures skills of RPA developers and covers using Workflow Analyzer and validation tools to identify errors, maintain project compliance, and ensure workflow efficiency during development.

Topic 4

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 5

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 6

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 7

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 8

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 9

Integration Service: This section of the exam measures skills of automation analysts and covers the use of UiPath Integration Service, its connectors, and triggers, showing how these elements enable smooth interaction between UiPath and third-party systems.

Topic 10

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 11

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 12

UiPath Communications Mining: This section of the exam measures skills of RPA developers and covers the application of Communications Mining in automation and analytics. It distinguishes this capability from Task Mining and Process Mining, explains the interface, and describes use cases.

Topic 13

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 14

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 15

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 16

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 17

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 18

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 19

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 20

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 21

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.

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UiPath Specialized AI Associate Exam (2023.10) Sample Questions (Q91-Q96):

NEW QUESTION # 91
How can you build custom models supported by AI Center?

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

Answer: A

Explanation:
To build custom models supported by AI Center, you can use a Python IDE or an AutoML platform of your choice. A Python IDE is a software application that provides tools and features for writing, editing, debugging, and running Python code. An AutoML platform is a service that automates the process of building and deploying machine learning models, such as data preprocessing, feature engineering, model selection, hyperparameter tuning, and model evaluation. Some examples of Python IDEs are PyCharm, Visual Studio Code, and Jupyter Notebook. Some examples of AutoML platforms are Google Cloud AutoML, Microsoft Azure Machine Learning, and DataRobot.
To use a Python IDE, you need to install the required Python packages and dependencies, write the code for your model, and test it locally. Then, you need to package your model as a zip file that follows the AI Center ML Package structure and requirements. You can then upload the zip file to AI Center and create an ML Skill to deploy and consume your model.
To use an AutoML platform, you need to sign up for the service, upload your data, configure your model settings, and train your model. Then, you need to export your model as a zip file that follows the AI Center ML Package structure and requirements. You can then upload the zip file to AI Center and create an ML Skill to deploy and consume your model.
References: AI Center - Building ML Packages, AI Center - ML Package Structure, AI Center - Creating ML Skills

NEW QUESTION # 92
What rule should be used in Taxonomy Manager for a text field that can have one of multiple known values?

A. Contains
B. Possible values
C. Ends with
D. Starts with

Answer: B

Explanation:
In UiPath's Taxonomy Manager, the "Possible values" rule should be used when a text field can have one of several predefined values. This ensures that the extracted data is validated against a list of acceptable values, helping to maintain consistency and accuracy during the extraction process. It is particularly useful for fields such as status indicators or categorical fields where only a limited set of options is valid.
(Source: UiPath Taxonomy Manager documentation)

NEW QUESTION # 93
What happens to your document and the process of pre-labeling when you choose the "Predict" option from the "Predict" dropdown in Document Manager?

A. It predicts all fields using the Generative Predict capability only, ignoring any pre-labeling endpoint that may be configured. If Generative Predict is not available, it will not predict any fields.
B. It predicts fields using only the prelabeling endpoint model configured in the Prelabeling settings, and it does not use Generative Predict.
C. It predicts fields using the Generative Prelabeling for OOTB document types and the pre-labeling endpoint for custom document types.
D. It merges the results of the Generative Predict functionality and the results of the prelabeling endpoint (if configured). If the latter is not configured, it uses solely Generative Predict for all fields.

Answer: D

Explanation:
Reference: UiPath Document Manager - Predict Feature

NEW QUESTION # 94
Which validation checks are performed for ML packages uploaded with the Enable Training option inactive?

A. 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.
B. 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.
C. 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.
D. 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.

Answer: C

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 # 95
Which features in Generative Annotation are automatically enabled on datasets in Communication Mining technology?

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

Answer: A

Explanation:
In UiPath Communication Mining, the Generative Annotation feature automatically enables Assisted Labelling on datasets. Assisted Labelling helps to accelerate the labeling process by automatically suggesting relevant labels based on the content of the communications. This feature significantly improves the efficiency of the model training process by reducing the manual effort required to label large datasets.
For more details, refer to:
UiPath Communication Mining Documentation: Generative Annotation and Assisted Labelling Labeling and Annotation in UiPath Communications Mining: UiPath AI Center Documentation

NEW QUESTION # 96
......

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