UiPath-SAIv1練習問題集で検証済みで更新された213問題あります [Q79-Q94]

UiPath-SAIv1練習問題集で検証済みで更新された213問題あります

更新されたUiPath-SAIv1試験問題集でPDF問題とテストエンジン

質問 # 79
What does the Train stage of the Document Understanding Framework do?

A. Improves the extractor accuracy by learning from the classification result.
B. Allows the extractor to improve its prediction over time by using better OCR (Optical Character Recognition) engines.
C. Allows the model to learn from human-validated data.
D. Allows a human to validate and correct the extracted data.

正解：C

解説：
In the UiPath Document Understanding Framework, the Train stage enables models to learn from human- validated data. This process involves feeding the corrections made by humans during the validation phase back into the model, allowing it to refine its predictions and improve accuracy over time.
UiPath Documentation
The training component is crucial for classifiers and extractors capable of learning from human feedback. By incorporating validated data, these components can adjust their algorithms to better handle similar documents in the future, enhancing the overall efficiency and effectiveness of the automation process.
Other options are incorrect because:
* B. Allows the extractor to improve its prediction over time by using better OCR engines: While better OCR engines can enhance data extraction, this is not the function of the Train stage.
* C. Allows a human to validate and correct the extracted data: This describes the Validation stage, not the Train stage.
* D. Improves the extractor accuracy by learning from the classification result: Training focuses on learning from human-validated extraction results, not just classification outcomes.
Therefore, the primary purpose of the Train stage is to allow the model to learn from human-validated data, thereby improving its future performance.

質問 # 80
What is the definition of a UiPath Communications Mining data source?

A. A user-permissioned project containing a taxonomy with labels and entities.
B. A collection of raw unlabeled communications data of a similar type, that can be associated with up to
10 datasets.
C. A permissioned storage area within the platform which contains communications and labels.
D. The model that we create when training the platform to understand the data in those sources.

正解：B

解説：
According to the UiPath documentation, a data source is a raw collection of verbatims, which are text-based communications such as survey responses, emails, transcripts, or calls1. A data source can be of a similar type and share a similar intended purpose, such as capturing customer feedback or servicing requests2. A data source can be added to up to 10 different datasets, which are collections of sources and labels that are used to train and evaluate ML models3. Therefore, the correct definition of a UiPath Communications Mining data source is A.
References:
1: Communications Mining - Sources 2: Communications Mining - Managing Sources and Datasets 3: Communications Mining - Understanding the data structure and permissions

質問 # 81
Which of the following is a best practice when choosing a UiPath ML (Machine Learning) Extractor?

A. The size of the ML Extractor is the most important factor to consider when choosing an ML Extractor.Bigger models always perform better and provide more accurate extraction results because the development team invested time and effort into creating the algorithm, which in turn will result in better performance for the trained model.
B. The cost of the ML Extractor should be the main consideration when choosing an ML Extractor.
Select the ML Extractor that offers the lowest price, regardless of its performance or suitability for the specific document understanding needs.
C. The popularity of the ML Extractor among other UiPath users should be the primary factor when choosing a UiPath ML Extractor.
Opt for the ML Extractor that has the highest number of downloads or positive reviews.
D. Consider the document types, language, and data quality when choosing an ML Extractor.
It is important to select one that is specifically trained or optimized for the document types being processed.
It is also important to take into account the quality and diversity of the training data used to train the ML Extractor to ensure accurate and reliable extraction results.

正解：D

解説：
The ML Extractor is a data extraction tool that uses machine learning models provided by UiPath to identify and extract data from documents. The ML Extractor can work with predefined document types, such as invoices, receipts, purchase orders, and utility bills, or with custom document types that are trained using the Data Manager and the Machine Learning Classifier Trainer12.
According to the best practice, the ML Extractor should be chosen based on the document types, language, and data quality of the documents being processed. It is important to select an ML Extractor that is specifically trained or optimized for the document types that are relevant for the use case, as different document types may have different layouts, fields, and formats. It is also important to take into account the language of the documents, as some ML Extractors may support only certain languages or require specific language settings. Moreover, it is important to consider the quality and diversity of the training data used to train the ML Extractor, as this may affect the accuracy and reliability of the extraction results. The training data should be representative of the real-world data, and should cover various scenarios, variations, and exceptions3.
References: 1: Machine Learning Extractor - UiPath Activities 2: Machine Learning Classifier Trainer - UiPath Document Understanding 3: Data Extraction - UiPath Document Understanding

質問 # 82
What is the role of the Taxonomy Manager?

A. To present a document processing specific user interface for validating and correcting automatic classification outputs.
B. To select the type of ML that can be used in the project.
C. To create and edit a Taxonomy file specific to the current automation project.
D. To select which extractors are trained for each document type and field.

正解：C

解説：
The Taxonomy Manager is a tool that enables you to create and edit a Taxonomy file, which is an XML file that defines the document types and fields that are relevant for your automation project1. The Taxonomy file is used by the Classify Document Scope and Data Extraction Scope activities to perform document classification and data extraction, respectively2. The Taxonomy Manager allows you to add, remove, rename, or reorder document types and fields, as well as specify the data type, format, and validation rules for each field3. The Taxonomy Manager also provides a preview of the Taxonomy file and a validation feature to check for errors or inconsistencies.
References:
1: About Taxonomy Manager 2: About Document Understanding Framework 3: Using the Taxonomy Manager : Taxonomy Manager User Interface Description

質問 # 83
Which of the following best describes UiPath Document Understanding?

A. A suite of tools for automating document processing tasks.
B. A solution for managing cloud infrastructure.
C. A platform for managing robotic process automation (RPA) workflows.
D. A software for creating machine learning models.

正解：A

質問 # 84
What are all the ways to deploy Al Center?

A. In cloud availability, on-premises air-gapped, on-premises online, hybrid mode (cloud Al Center + Orchestrator on-premise). and Automation Suite.
B. In cloud availability, on-premises air-gapped, on-premises online, and hybrid mode {cloud Al Center + Orchestrator on-premise).
C. In cloud availability, on-premises, hybrid mode (Al Center on-premise + cloud Orchestrator), and Automation Suite.
D. In cloud availability, on-premises air-gapped, on-premises online, hybrid mode (Al Center on-premise
+ Orchestrator on-premise). and Automation Suite.

正解：D

解説：
UiPath AI Center can be deployed in multiple ways to meet different organizational needs and infrastructures. The available deployment options include:
* Cloud availability: Using UiPath's cloud services.
* On-premises air-gapped: A fully isolated, offline environment for organizations with strict security requirements.
* On-premises online: Deployed on-premise but with internet connectivity.
* Hybrid mode: Combining on-premises AI Center with on-premises Orchestrator for flexibility.
* Automation Suite: A comprehensive deployment of UiPath tools, including AI Center.
For more details, refer to:
* UiPath AI Center Deployment Models: Deployment Options

質問 # 85
What is the relationship between AI Center and UiPath Document Understanding?

A. Document Understanding is the infrastructure on which AI Center machine learning models run.
B. Document Understanding is the infrastructure on which AI Center digitization runs.
C. AI Center is the infrastructure on top of which UiPath Document Understanding digitization runs.
D. AI Center is the infrastructure on top of which UiPath Document Understanding machine learning models run.

正解：D

質問 # 86
Which of the following options is accepted as a Column field name in Document Manager?

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

正解：B

質問 # 87
What happens during the Classify stage of the Document Understanding Framework?

A. The target fields are extracted from the document and sent to Action Center for human validation.
B. The documents are included in one of the taxonomy document types or skipped.
C. The extracted data is exported as a dataset.
D. The OCR engine is used to extract text from the image document.

正解：B

解説：
According to the UiPath documentation, the Classify stage of the Document Understanding Framework is used to automatically determine what document types are found within a digitized file. The document types are defined in the project taxonomy, which is a collection of all the labels and fields applied to the documents in a dataset. The Classify stage uses one or more classifiers, which are algorithms that assign document types to files based on their content and structure. The classifiers can be configured and executed using the Classify Document Scope activity, which also allows for document type filtering, taxonomy mapping, and minimum confidence threshold settings. The Classify stage outputs the classification information in a unified manner, irrespective of the source of classification. The documents that are classified are then sent to the next stage of the framework, which is Data Extraction. The documents that are not classified or skipped are either excluded from further processing or sent to Action Center for human validation and correction.
References:
* Document Understanding - Document Classification Overview
* Document Understanding - Introduction
* Generative Extraction & Classification using Document Understanding in Cross-Platform Projects (Public Preview)

質問 # 88
What is the default visibility of an ML skill?

A. An ML skill is by default private and can't be made public.
B. An ML skill is by default private and can be made public.
C. An ML skill is by default public and can be made private.
D. An ML skill is by default public and can't be made private.

正解：B

質問 # 89
What is the minimum number of pinned examples users should provide per label in UiPath Communications Mining?

A. 0
B. 1
C. 2
D. 3

正解：D

解説：
Mining, it is recommended that users provide a minimum of 25 pinned examples per label to ensure proper training and accurate predictions by the machine learning models. This number allows the platform to have a sufficient variety of examples to generalize and make reliable predictions for each label in real-world scenarios.
The minimum number of pinned examples per label is crucial because it enhances both precision and recall, helping the model effectively differentiate between labels and improving overall model performance. If fewer examples are provided, the model may struggle with generalization and might not perform well in distinguishing between similar or overlapping categories.
This standard of 25 pinned examples is outlined in several UiPath documentation sections and best practices for training models in Communications Mining UiPath Documentation UiPath Documentation UiPath Community Forum For further details, refer to UiPath's official Communications Mining User Guide on their documentation portal.

質問 # 90
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.

正解：B

質問 # 91
What is a reason for pinning a UiPath Communications Mining Model?

A. To allow AB comparing of the statistics of that model version with another one.
B. To allow rollback of annotations to that model version.
C. To delete all other model versions.
D. To force the Ul to show predictions from that model version in explore

正解：D

解説：
In UiPath Communications Mining, pinning a model ensures that the predictions shown in the Explore tab are generated from that specific model version. This feature allows users to control which version of the model is actively making predictions, particularly during evaluation or comparison stages. By pinning a model, the user ensures that the UI reflects the predictions from the selected version, helping maintain consistency when analyzing results or making changes.
For more details, refer to:
* UiPath Communications Mining: Model Management and Pinning
* UiPath AI Center Documentation: Managing Model Versions

質問 # 92
What is the purpose of the End Process in the Document Understanding Process?

A. The purpose of the End Process in the Document Understanding Process is to generate a summary report of the processing statistics and performance metrics.
B. End Process in the Document Understanding Process silently shuts down the Virtual Machine so that another robot can use it.
C. End Process is a feature in the Document Understanding Process that exports the extracted data into a readable document format.
D. End Process sets the queue transaction status as Successful in case of no exception, and as Failed in case of an exception with their corresponding Business or System Exception, and the post processing
/cleaning if required.

正解：D

解説：
The End Process is the final stage of the Document Understanding Process, which is a fully functional UiPath Studio project template based on a document processing flowchart. The End Process is responsible for setting the queue transaction status, logging the results, and performing any post processing or cleaning actions if needed. The End Process sets the queue transaction status as Successful if the document was processed without any exception, and as Failed if an exception occurred, either a Business Exception (such as invalid data) or a System Exception (such as network failure). The End Process also adds the extracted data and the validation status as output arguments to the queue transaction. The End Process also logs the processing statistics, such as the number of documents processed, the number of exceptions, the average processing time, and the accuracy rate. The End Process also performs any post processing or cleaning actions, such as deleting temporary files, closing applications, or sending notifications1.
References: 1: Document Understanding Process: Studio Template

質問 # 93
What information does the comparison between two cohorts display on the Comparison page in UiPath Communications Mining?