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Exam UiPath UiPath-SAIAv1 Study Solutions & Passing UiPath-SAIAv1 Score Feedback

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UiPath UiPath-SAIAv1 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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 4

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 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

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 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

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 9

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 10

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 11

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 12

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 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

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 15

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 16

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.

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UiPath Specialized AI Associate Exam (2023.10) Sample Questions (Q66-Q71):

NEW QUESTION # 66
When creating a training dataset, what is the recommended number of samples for the Classification fields?

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

Answer: B

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 # 67
What is the recommended split of documents for training and evaluation, considering a total of 15 documents per vendor?

A. 10 documents for training the model, and 5 for evaluating the model.
B. 8 documents for training the model, and 7 for evaluating the model.
C. 12 documents for training the model, and 3 for evaluating the model.
D. 7 documents for training the model, and 8 for evaluating the model.

Answer: A

Explanation:
When you create a training dataset for document classification or data extraction, you need to split your documents into two subsets: one for training the model and one for evaluating the model. Thetraining subset is used to teach the model how to recognize the patterns and features of your document types and fields. The evaluation subset is used to measure the performance and accuracy of the model on unseen data. The evaluation subset should not be used for training, as this would bias the model and overfit it to the data1.
The recommended split of documents for training and evaluation depends on the size and diversity of your data. However, a general guideline is to use a 70/30 or 80/20 ratio, where 70% or 80% of the documents are used for training and 30% or 20% are used for evaluation. This ensures that the model has enough data to learn from and enough data to test on. For example, if you have 15 documents per vendor, you can use 10 documents for training and 5 documents for evaluation. This would give you a 67/33 split, which is close to the 70/30 ratio. You can also use the Data Manager tool to create and manage your training and evaluation datasets2.
References: 1: Document Understanding - Training High Performing Models 2: Data Manager - Creating a Dataset

NEW QUESTION # 68
Which activity is used to validate and correct automatic classification outputs?

A. Present Validation Station activity
B. Digitize Document activity
C. Present Classification Station activity
D. Classify Document activity

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
* ThePresent Classification Stationactivity is specifically used toreview and correct the classification resultsperformed by the "Classify Document" activity.
* It allows a human to validate which document type has been assigned before data extraction begins.
* UiPath Documentation Reference:Classification Station - Document Understanding

NEW QUESTION # 69
If you need to retrieve an item based on a corresponding identifier in UiPath, which collection type would you use?

A. ArrayOf<T> or System.DataType[] as it stores indexed values
B. System.Collections.Generic.List<T> as it stores ordered values
C. System.Collections.Generic.Dictionary<TKey, TValue> as it stores indexed values
D. System.Collections.Generic.Dictionary<TKey, TValue> as it stores key-value pairs

Answer: D

Explanation:
Reference: UiPath Dictionaries

NEW QUESTION # 70
Which of the following file types are supported for the DocumentPath property in the Classify Document Scope activity?

A. .bmp, .pdf, .jpe, .psd
B. .pdf, .jpeg, .raw, tif
C. .jpe, .eps, .jpg, .tiff
D. .png, .gif, .jpe, .tiff

Answer: D

Explanation:
According to the UiPath documentation portal1, the DocumentPath property in the Classify Document Scope activity accepts the path to the document you want to validate. This field supports only strings and String variables. The supported file types for this property field are .png, .gif, .jpe, .jpg, .jpeg, .tiff, .tif, .bmp, and .
pdf. Therefore, option B is the correct answer, as it contains four of the supported file types. Option A is incorrect, as .psd is not a supported file type. Option C is incorrect, as .raw is not a supported file type. Option D is incorrect, as .eps is not a supported file type.
References: 1 Activities - Classify Document Scope - UiPath Documentation Portal

NEW QUESTION # 71
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