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UiPath UiPath-SAIAv1 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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 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 4	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 5	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 6	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 7	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 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	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 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	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 12	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 13	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 14	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 15	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 16	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 17	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 18	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.

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UiPath Specialized AI Associate Exam (2023.10) Sample Questions (Q247-Q252):

NEW QUESTION # 247
Why should using the Search feature be limited when training in UiPath Communications Mining?

A. It could increase model bias.
B. It could increase model coverage.
C. It could decrease model bias.
D. It could decrease model coverage.

Answer: A

Explanation:
In UiPath Communications Mining, over-reliance on the Search feature during the training process can lead to an increase in model bias. This happens because using search-based filtering to identify and label examples might not represent the full diversity of the data. The model could be trained on a skewed subset of the data, causing it to favor certain patterns or keywords, and thus biasing the model towards specific types of data rather than learning to generalize effectively across all data.
Limiting the use of search ensures that the training process considers a broader and more representative sample of the data, which reduces the risk of introducing bias into the model and helps it generalize better to new, unseen communications.
For more details, refer to:
UiPath Communications Mining Documentation: Model Training and Avoiding Bias

NEW QUESTION # 248
What is the definition of Deep Learning?

A. An area of machine learning concerned with artificial neural networks.These are a series of algorithms that aim to recognize relationships in a set of data through a process that mimicsbiological neural networks.
B. A sub-field of artificial intelligence that enables systems to learn from data.Systems learn from previous experience and information to deduce and predict future information. To do this they use algorithms that learn to perform a specific task without being explicitly programmed.
C. The theory and development of computer systems that are able to perform tasks that normally require human intelligence and decision making.
D. A field of artificial intelligence that enables computers to gain high-level understanding from digital images or videos. If AI is the brain, then this is the eye that enables the computer to observe and understand. It works the same as the human eye.

Answer: A

Explanation:
Deep learning is a subset of machine learning that uses multiple layers of artificial neural networks to learn from data and perform complex tasks. The term "deep" refers to the number of layers in the network, which can range from a few to hundreds or even thousands. Each layer consists of a set of nodes that perform mathematical operations on the input data and pass the output to the next layer. The network learns by adjusting the weights of the connections between the nodes based on the feedback from the desired output.
Deep learning can handle various types of data, such as images, text, speech, or video, and can automatically extract features and patterns from them without human intervention. Deep learning is behind many applications of artificial intelligence, such as computer vision, natural language processing, speech recognition, and generative models123.
References: 1: What is Deep Learning? | IBM 2: What Is Deep Learning? Definition, Examples, and Careers | Coursera 3: Deep learning - Wikipedia

NEW QUESTION # 249
Which of the following options is accepted as a Column field name in Document Manager?

A. First_name123
B. f1rst-name
C. first_n@me
D. first name

Answer: A

Explanation:
According to the UiPath documentation, the field name for a column field in Document Manager does not accept uppercase letters. It can only contain lowercase letters, numbers, underscore _ and dash -12. Therefore, the only option that meets these criteria is D. First_name123. The other options are invalid because they either contain uppercase letters, spaces, or @ symbols, which are not allowed.
References: 1: Document Understanding - Create and Configure Fields 2: Document Understanding - Create
& Configure Fields

NEW QUESTION # 250
When creating a training dataset, what is the recommended number of samples for the Classification fields?

A. 10-20 document samples from each class.
B. 50-200 document samples from each class.
C. 5-10 document samples from each class.
D. 20-50 document samples from each class.

Answer: D

Explanation:
According to the UiPath documentation, the recommended number of samples for the classification fields depends on the number of document types and layouts that you want to classify. The more document types and layouts you have, the more samples you need to cover the diversity of your data. However, a general guideline is to have at least 20-50 document samples from each class, as this would provide enough data for the classifiers to learn from12. A large number of samples per layout is not mandatory, as the classifiers can generalize from other layouts as well3.
References: 1: Document Classification Training Overview 2: Document Classification Training Related Activities 3: Training High Performing Models

NEW QUESTION # 251
When processing a document type that comes in a high variety of layouts, what is the recommended data extraction methodology?

A. Model-based data extraction.
B. Hybrid data extraction.
C. Manual data extraction.
D. Rule-based data extraction.

Answer: B

Explanation:
Based on the classification of documents, there are two common types of data extraction methodologies: rule- based data extraction and model-based data extraction1. Rule-based data extraction targets structured documents, while model-based data extraction is used to process semi-structured and unstructured documents1. However, neither of these methods alone can handle the high variety of layouts that some document types may have. Therefore, a hybrid data extraction approach is recommended, which combines the strengths of both methods and allows for more flexibility and accuracy23. A hybrid data extraction approach can use one or more extractors, such as RegEx Based Extractor, Form Extractor, Intelligent Form Extractor, Machine Learning Extractor, or FlexiCapture Extractor, depending on the document type and the fields of interest3. The Data Extraction Scope activity in UiPath enables the configuration and execution of a hybrid data extraction methodology, by allowing the user to customize which fields are requested from each extractor, what is the minimum confidence threshold for a given data point extracted by each extractor, what is the taxonomy mapping, at field level, between the project taxonomy and the extractor's internal taxonomy (if any), and how to implement "fall-back" rules for data extraction2.
References: 2: Data Extraction Overview 3: Data Extraction 1: Document Processing with Improved Data Extraction

NEW QUESTION # 252
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100% Pass Quiz 2025 UiPath UiPath-SAIAv1 Newest Valid Test Objectives

UiPath UiPath-SAIAv1 Exam Syllabus Topics:

100% Pass Quiz 2025 UiPath UiPath-SAIAv1 – Reliable Valid Test Objectives

UiPath Specialized AI Associate Exam (2023.10) Sample Questions (Q247-Q252):