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便利なUiPath UiPath-SAIAv1認定デベロッパー &合格スムーズUiPath-SAIAv1資格問題対応 |信頼的なUiPath-SAIAv1試験対策

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UiPath UiPath-SAIAv1 認定試験の出題範囲：

トピック
出題範囲

トピック 1

• 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.

トピック 2

• 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.

トピック 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.

トピック 4

• 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.

トピック 5

• 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.

トピック 6

• 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.

トピック 7

• 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.

トピック 8

• 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.

トピック 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.

トピック 10

• 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.

トピック 11

• 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.

トピック 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).

トピック 13

• 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.

トピック 14

• 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.

トピック 15

• 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.

トピック 16

• 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.

トピック 17

• 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.

トピック 18

• 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.

トピック 19

• 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.

トピック 20

• 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.

トピック 21

• 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.

 

>> UiPath-SAIAv1認定デベロッパー <<

UiPath UiPath-SAIAv1認定デベロッパー & CertJuken - 資格試験のリーダー

UiPathのUiPath-SAIAv1試験に参加するつもりの多くの受験生は就職しました。ほかのたくさんの受験生は生活の中でのことに挑戦しています。だから、我々は受験生の皆さんに一番効果的なUiPathのUiPath-SAIAv1復習方法を提供します。あなたは安心で我々の商品を購入できるために、我々は各バーションのUiPathのUiPath-SAIAv1復習資料のサンプルを提供してあなたに試させます。我々のUiPathのUiPath-SAIAv1復習資料を通して、いろいろな受験生はもうUiPathのUiPath-SAIAv1試験に合格しました。あなたは我々のソフトのメリットを感じられると希望します。

UiPath Specialized AI Associate Exam (2023.10) 認定 UiPath-SAIAv1 試験問題 (Q73-Q78):

質問 # 73
While creating a process automation pipeline, what process attribute should be avoided to ensure there are minimal or no automation maintenance requirements?

• A. High process run time
• B. Frequent business logic change
• C. The process requires exception handling
• D. The process is prone to human error

正解：B

解説：
Comprehensive and Detailed Explanation From Exact Extract:
Processes that undergofrequent business logic changesrequire frequent updates to automation scripts, resulting inhigh maintenance effortand potential breakdowns in unattended execution.
* Stable, rules-based processes are preferred for long-term, low-maintenance automation.
* UiPath Academy Reference:RPA Developer Foundation - "Process Assessment and Selection" Module

 

質問 # 74
What is the purpose of the generative classifier?

• A. To classify documents using generative OCR.
• B. To classify documents using a combination of generative and specialized models.
• C. To classify documents using AI Center out-of-the-box generative models.
• D. To classify documents using generative models.

正解：D

解説：
Reference: UiPath Document Understanding Framework

 

質問 # 75
If you need to retrieve an item based on a corresponding identifier in UiPath, which collection type would you use?

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

正解：A

解説：
Reference: UiPath Dictionaries

 

質問 # 76
What is the Machine Learning Extractor?

• A. A tool using machine learning models to identify and report on data targeted for data extraction.
• B. A specialized model that can recognize multiple languages in the same document using API calls to a Hugging Face model with over 250 languages.
• C. An extraction model that can be enabled and trained in Al Center. For better accuracy. 25 documents per model are recommended to train the model.
• D. A tool that helps extract data from different document structures, and is particularly useful when the same document has multiple formats.

正解：D

解説：
The Machine Learning Extractor utilizes machine learning models to effectively extract data from documents, especially when dealing with varying structures or formats within the same document type. This capability is crucial in scenarios where documents do not follow a strict template and have variations in their layout or content organization. The extractor can be trained to understand these variations and accurately extract the needed information.
The Machine Learning Extractor is a data extraction tool that uses machine learning models to extract data from various types of documents, such as invoices, receipts, or forms. It is especially useful when the same document type has multiple layouts or formats, as it can learn and infer the values for the targeted fields, even from documents and layouts it has never seen before1.
The Machine Learning Extractor can be used with one of UiPath's public Document Understanding endpoints, which provide generic models for certain document types, or with custom trained machine learning models hosted in AI Center, which can be tailored to specific use cases. The Machine Learning Extractor can be configured and trained using the Data Extraction Scope activity in UiPath Studio2.
References:
Document Understanding - Machine Learning Extractor
UiPath Activities - Data Extraction Scope

 

質問 # 77
Which of the following is true when creating an ML Package in UiPath Al Center?

• A. The package name cannot contain any spaces or hyphens.
• B. The package name cannot use any Python reserved keywords.
• C. The package name cannot include any special characters such as "@", "S", or "#".
• D. The package name cannot exceed 10 characters in length.

正解：B

解説：
When creating an ML Package in UiPath AI Center, the package name must adhere to Python naming conventions, meaning it cannot include reserved keywords such as class, break, finally, etc. This ensures that the package can be successfully deployed without conflicts in the Python environment.

 

質問 # 78
......

この情報の時代の中に、たくさんのIT機構はUiPathのUiPath-SAIAv1認定試験に関する教育資料がありますけれども、受験生がこれらのサイトを通じて詳細な資料を調べられなくて、対応性がなくて受験生の注意 に惹かれなりません。

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