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質問 # 49
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.
正解：D
質問 # 50
Which of the following consumes Page Units?
- A. Using ML Classifier on a 21-page document.
- B. Using Intelligent Form Extractor on a 5-page document with 0 successful extractions.
- C. Applying OCR on a 10-page document.
- D. Creation of a Document Validation Action in Action Center.
正解：C
解説：
According to the UiPath documentation, Page Units are the measure used to license Document Understanding products. Page Units are charged based on the number of pages processed by the Document Understanding models, such as extractors, OCR engines, and classifiers. Therefore, applying OCR on a 10-page document consumes Page Units, while the other options do not. The creation of a Document Validation Action in Action Center does not consume any Page Units, as it is a human-in-the-loop activity. Using ML Classifier on a 21- page document does not consume Page Units, as it is a free model. Using Intelligent Form Extractor on a 5- page document with 0 successful extractions does not consume Page Units, as the extractor only charges for successful extractions.
References:
* AI Center - AI Units
* Document Understanding - Metering & Charging Logic
質問 # 51
What is supervised learning?
- A. Supervised learning is a machine learning paradigm in which algorithms try to solve a problem only by trial and error and using a system of rewards and punishments.
There is no need for labeled input/output pairs to be presented. Instead, the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge). - B. Supervised learning is a machine learning paradigm in which algorithms try to solve a problem in an uncertain, potentially complex environment only by trial and error and using a system of rewards and punishments.
There are no correct answers, but feedback is given in the form of rewards and penalties. - C. Supervised learning is a machine learning paradigm with the goal of learning a function that maps input variables with output variables.
In every case there is a correct answer, so the aim is to train the model until it reaches an acceptable level of performance in predicting the outcome, at which point the learning stops. - D. Supervised learning is a machine learning paradigm that refers to algorithms that learn patterns from unlabeled data.There are only input variables, but no corresponding output variables. The goal of the algorithm is to model the underlying structure of the data, but there are no correct answers and no teachers.
正解：C
解説：
Supervised learning is one of the most popular and widely used machine learning approaches. It involves providing the algorithm with labeled input/output pairs, which serve as examples of the desired behavior or outcome. The algorithm then learns a function that can generalize from these examples and make predictions for new, unseen data. Supervised learning can be used for tasks such as classification, regression, and anomaly detection. Some common supervised learning algorithms are linear regression, logistic regression, decision trees, support vector machines, and neural networks.
References:
* UiPath AI Fabric - Machine Learning Concepts
* UiPath Document Understanding - Machine Learning Models
* UiPath Communications Mining - Overview
質問 # 52
Which of the following OCR (Optical Character Recognition) engines is not free of charge?
- A. OmniPaqe.
- B. Tesseract.
- C. Microsoft OCR.
- D. Microsoft Azure OCR.
正解：A
解説：
According to the UiPath documentation, OmniPaqe is a paid OCR engine that requires a license to use. It is one of the most accurate and reliable OCR engines available, and it supports over 200 languages. The other OCR engines listed are free of charge, but they may have different features, limitations, and performance levels. For example, Tesseract is an open-source OCR engine that supports over 100 languages, but it may not be as accurate as OmniPaqe. Microsoft Azure OCR and Microsoft OCR are both cloud-based OCR engines that use Microsoft's technology, but they have different capabilities and pricing models. Microsoft Azure OCR can process both printed and handwritten text, and it uses a pay-as-you-go model based on the number of transactions. Microsoft OCR can only process printed text, and it is included in the UiPath Studio license.
References:
* Document Understanding - OCR Engines
* Automation Pricing - Complete UiPath Enterprise Solution
質問 # 53
Can you use Queues in the Document Understanding Process?
- A. The Document Understanding Process can't use Queues because items waiting for Human Validation for more than 10 days will be marked as Abandoned.
- B. The Document Understanding Process can use Queues but the Auto Retry Functionality should be enabled.
- C. The Document Understanding Process can use Queues but the Auto Retry Functionality should be disabled.
- D. The Document Understanding Process can't use Queues because items waiting for Human Validation for more than 24h will be marked as Abandoned.
正解：C
解説：
The Document Understanding Process is a fully functional UiPath Studio project template based on a document processing flowchart. It supports both attended and unattended robots with human-in-the-loop validation via Action Center. The process uses queues to store and process the input files, one file per queue item. However, the Auto Retry Functionality should be disabled on queues, because it can interfere with the human validation step and cause errors or duplicates. The process handles the retry mechanisms internally, using the Try/Catch and Error management features.
References:
* Document Understanding Process: Studio Template
* Document Understanding Process - New Studio Template
質問 # 54
What do entity predictions refer to within UiPath Communications Mining?
- A. The identification of a specific span of text as a value for a particular entity type.
- B. The understanding of the parent-label relationship when assigning label predictions.
- C. The model's confidence that a specific concept exists within a communication.
- D. The difference between label suggestions and label predictions in a training process.
正解：A
解説：
Entity predictions refer to the process of identifying and highlighting a specific span of text within a communication that represents a value for a predefined entity type. For example, an entity type could be
"Organization" and an entity value could be "UiPath". Entity predictions are made by the platform based on the training data and the rules defined for each entity type. Users can review, accept, reject, or modify the entity predictions using the Classification Station interface12.
References: Communications Mining - Reviewing and applying entities, Communications Mining - Predictions - UiPath Documentation Portal.
質問 # 55
Which of the following statements is true regarding reviewing and applying entities in UiPath Communications Mining?
- A. All of the entities within a paragraph should be reviewed.
- B. All of the entities in a communication must be reviewed.
- C. If the entity value is correctly predicted, but the entity type is wrong, it cannot be changed.
- D. A single entity value can be split across multiple paragraphs.
正解：A
質問 # 56
What can be done in the Reports section of the dataset navigation bar in UiPath Communication Mining?
- A. Access detailed, quervable charts, statistics, and customizable dashboards.
- B. View, save, and modify dataset model versions.
- C. Train models using unsupervised learning.
- D. Monitor model performance and receive recommendations.
正解：A
質問 # 57
A developer has created a string array variable as shown below:
UserNames = {"Jane", "Jack", "Jill", "John"}
Which expression should the developer use in a Log Message activity to print the elements of the array separated by the string ","?
- A. String.Concat(",", UserNames)
- B. String.Join(",", UserNames)
- C. String.Join(UserNames,",")
- D. String.Concat(UserNames,",")
正解：B
質問 # 58
What is the role of the Taxonomy Manager?
- A. To select which extractors are trained for each document type and field.
- B. To present a document processing specific user interface for validating and correcting automatic classification outputs.
- C. To select the type of ML that can be used in the project.
- D. To create and edit a Taxonomy file specific to the current automation project.
正解：D
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 # 59
Having the following Rules defined in the Taxonomy Manager for Billing Address field.
At the data extraction step. 42 W 80th St. West New York, NJ 1234, USA have been extracted (or the Billing Address field. When processing a Invoice using the DU process what will happen in (he Validation Station after data extraction step?
- A. There will be an error for Billing Address field regarding the ContainsNY rule, and the validation step will throw the user specified exception.
- B. There will be a warning message for Billing Address field regarding the ContalnsNY rule, but the validation can be performed.
- C. There will be an error for Billing Address field regarding the ContalnsNY rule, but the validation can be performed.
- D. There will be a warning message for Billing Address field regarding the ContalnsNY rule, and the validation step will throw the user specified exception.
正解：B
解説：
In this scenario, the rules defined in the Taxonomy Manager are as follows:
* IsNotEmpty: Ensures that the field is not empty.
* EndsWithUSA: Checks if the extracted address ends with "USA".
* ContainsNY: Ensures that the extracted address contains "NY".
The address extracted in this case is: "42 W 80th St. West New York, NJ 1234, USA". While the address ends with "USA" (passing the EndsWithUSA rule), it includes "West New York, NJ", which satisfies the ContainsNY rule even though "NY" is part of "New York". However, the exact behavior of the Contains rule can generate a warning message because "NY" is part of a larger string ("New York"). Since this does not constitute an error but simply a rule conflict, the validation can proceed.
Therefore, the most accurate outcome would be a warning, but validation can still be performed
質問 # 60
Why might labels have bias warnings in UiPath Communications Mining, even with 100% precision?
- A. They have low recall.
- B. They were trained using the "Shuffle" option extensively.
- C. They lack training examples.
- D. They were trained using the "Search" option extensively.
正解：C
解説：
Labels in UiPath Communications Mining are user-defined categories that can be applied to communications data, such as emails, chats, and calls, to identify the topics, intents, and sentiments within them1. Labels are trained using supervised learning, which means that users need to provide examples of data that belong to each label, and the system will learn from these examples to make predictions for new data2. However, not all labels are equally easy to train, and some may require more examples than others to achieve good performance. Labels that have bias warnings are those that have relatively low average precision, not enough training examples, or were labelled in a biased manner3. Precision is a measure of how accurate the predictions are for a given label, and it is calculated as the ratio of true positives (correct predictions) to the total number of predictions made for that label. A label with 100% precision means that all the predictions made for that label are correct, but it does not necessarily mean that the label is well-trained. It could be that the label has very few predictions, or that the predictions are only made on a subset of data that is similar to the training examples. This could lead to overfitting, which means that the label is too specific to the training data and does not generalize well to new or different data. Therefore, labels with 100% precision may still have bias warnings if they lack training examples, because this indicates that the label is not representative of the underlying data distribution, and may miss important variations or nuances that could affect the predictions. To improve the performance and reduce the bias of these labels, users need to provide more and diverse examples that cover the range of possible scenarios and expressions that the label should capture.
References: 1: Communications Mining Overview 2: [Creating and Training Labels] 3: Understanding and Improving Model Performance : [Precision and Recall] : [Overfitting and Underfitting] : Fixing Labelling Bias With Communications Mining
質問 # 61
What is the role of the Taxonomy Manager?
- A. To select which extractors are trained for each document type and field.
- B. To present a document processing specific user interface for validating and correcting automatic classification outputs.
- C. To select the type of ML that can be used in the project.
- D. To create and edit a Taxonomy file specific to the current automation project.
正解：D
質問 # 62
What can the Custom Named Entity Recognition out-of-the-box model be used for?
- A. Understand sentiment in product reviews, customer surveys, social media posts, and emails.
- B. Relate customer questions to FAQ documents and automatically pull responses from these documents.
- C. Classify text in resumes, emails, web pages, and other formats.
- D. Extract and classify text in emails, letters, web pages, research papers, and call transcripts.
正解：D
質問 # 63
What rule should be used in Taxonomy Manager for a text field that can have one of multiple known values?
- A. Starts with
- B. Contains
- C. Possible values
- D. Ends with
正解：C
解説：
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)
質問 # 64
Which statement accurately describes out-of-the-box models in UiPath?
- A. Out-of-the-box models are only available for specific languages and cannot be adapted for multilingual documents.
- B. Out-of-the-box models require extensive training and customization before they can be used effectively.
- C. Out-of-the-box models are pre-trained models that cover a wide range of document types.
- D. Out-of-the-box models are custom-built models created specifically for each project.
正解：C
解説：
UiPath provides out-of-the-box pre-trained models that are ready for use and cover a wide variety of document types. These models can be used as-is for common use cases or fine-tuned based on specific requirements, making them versatile for different projects
質問 # 65
What is the purpose of the "Explore" phase in UiPath Communications Mining?
- A. To provide each label/entity in a taxonomy with enough training examples so the model can make accurate predictions at scale.
- B. To use the bulk label functionality, a helpful tool to quickly train the model when searching for specific terms.
- C. To review the clusters of similar communications from a data set that unsupervised learning automatically found.
- D. To fully review and correctly tag the model version, regardless if it's "Live" or "Staging".
正解：A
解説：
The Explore phase is the second phase of model training in UiPath Communications Mining, which is a solution that enables the analysis of large volumes of text-based communications using natural language processing and machine learning. The Explore phase builds on the foundations of the taxonomy that was created in the Discover phase by reviewing clusters and searching for different terms and phrases. The objective of the Explore phase is to provide each of the labels or entities that are important for the use case with enough varied and consistent training examples, so that the platform has sufficient training data from which to make accurate predictions across the entire dataset. The Explore phase is the core phase of model training, and requires the most time and effort, but also leads to better model performance and accuracy1.
References: 1: Communications Mining - Explore
質問 # 66
What is a reason for pinning a UiPath Communications Mining Model?
- A. To delete all other model versions.
- B. To allow rollback of annotations to that model version.
- C. To force the Ul to show predictions from that model version in explore
- D. To allow AB comparing of the statistics of that model version with another one.
正解：C
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 # 67
Having the following list of documents:
Invoice1.pdf, Invoice2.raw, Invoice3.gif, Invoice4.jpg, Invoice5.docx
Please choose all the files that can be used in the DocumentPath property of the Classify Document Scope activity.
- A. Invoice1.pdf, Invoice3.gif, Invoice5.docx
- B. Invoice1.pdf, Invoice3.gif, Invoice4.jpg
- C. Invoice1.pdf, Invoice4.jpg, Invoice5.docx
- D. Invoice1.pdf, Invoice2.raw, Invoice3.gif, Invoice4.jpg
正解：B
解説：
The Classify Document Scope activity in UiPath is used to classify documents supported by the Document Understanding framework. It primarily works with file formats like PDF, JPG, PNG, and other image-based formats but does not process raw or non-standard file types like .raw.
質問 # 68
What information does the comparison between two cohorts display on the Comparison page in UiPath Communications Mining?
- A. Entity count for each metadata.
- B. Differences in verbatim length between Group A and Group B.
- C. Verbatim content for each label.
- D. Total verbatim count and proportion for each label.
正解：D
解説：
According to the UiPath documentation, UiPath Communications Mining is a tool that enables you to analyze text-based communications data, such as customer feedback, support tickets, or chat transcripts, using natural language processing (NLP) and machine learning (ML) techniques1. One of the features of UiPath Communications Mining is the Comparison page, which allows you to compare two cohorts of verbatims based on different criteria, such as date range, source, metadata, or label2. The Comparison page displays the following information for each cohort3:
* Total verbatim count: The number of verbatims in the cohort.
* Proportion for each label: The percentage of verbatims in the cohort that are assigned to each label. A label is a category or a topic that is relevant for the analysis, such as sentiment, intent, or issue type.
Labels can be predefined or custom-defined by the user.
* Statistical significance: The p-value that indicates whether the difference in proportions between the two cohorts is statistically significant or not. A p-value less than 0.05 means that the difference is unlikely to be due to chance.
The Comparison page also provides a visual representation of the proportions for each label using a bar chart, and allows the user to drill down into the verbatim content for each label by clicking on the bars3. Therefore, the correct answer is A.
References:
1: About Communications Mining 2: Communications Mining - Comparing Cohorts 3: Communications Mining - Comparison Page
質問 # 69
What is the correct order of migrating a dataset from Document Manager to a Modern Project? Instructions:
Drag the Description found on the left and drop on the correct Step found on the right.
正解：
解説：
Explanation:
To organize the steps in the correct order for migrating a dataset from the Document Manager to a Modern Project, I'll analyze the instructions and then match the steps accordingly. Here's the logical order based on the descriptions:
* Step 1: Navigate to and open the project into which you want to import data. Select an already existing custom document type or create a new one.
* Step 2: Select "Upload" and choose the ZIP file exported from the classic project. Wait for the upload to finish.
* Step 3: Go to the document type you want to export and select "Open document type."
* Step 4: From the "Filter documents" drop-down list, select "Training and validation set." Select
"Export."
* Step 5: Leave "Current search results" selected and fill in a name for your export job. Select
"Download."
This should reflect the correct sequential process of migrating a dataset from Document Manager to a Modern Project.
質問 # 70
What are the UiPath Action Center action statuses?
- A. Unassigned, Assigned, Completed
- B. Unassigned, Pending, Completed
- C. Unassigned, Assigned, Modified, Completed
- D. Unassigned, Pending, Edited, Completed
正解：B
解説：
The valid Action Center statuses are:
* Unassigned: The action is not assigned to any user.
* Pending: The action is assigned and awaiting user response.
質問 # 71
What can be done in the Reports section of the dataset navigation bar in UiPath Communication Mining?
- A. Access detailed, quervable charts, statistics, and customizable dashboards.
- B. View, save, and modify dataset model versions.
- C. Train models using unsupervised learning.
- D. Monitor model performance and receive recommendations.
正解：A
解説：
The Reports section of the dataset navigation bar in UiPath Communication Mining allows users to access detailed, quervable charts, statistics, and customizable dashboards that provide valuable insights and analysis on their communications data1. The Reports section has up to six tabs, depending on the data type, each designed to address different reporting needs2:
* Dashboard: Users can create custom dashboard views using data from other tabs, such as label summary, trends, segments, threads, and comparison. Dashboards are specific to the dataset and can be edited, deleted, or renamed by users with the 'Modify datasets' permission3.
* Label Summary: Users can view high-level summary statistics for labels, such as volume, precision, recall, and sentiment. Users can also filter by data type, source, date range, and label category.
* Trends: Users can view charts for verbatim volume, label volume, and sentiment over a selected time period. Users can also filter by data type, source, date range, and label category.
* Segments: Users can view charts comparing label volumes to verbatim metadata fields, such as sender domain, channel, or language. Users can also filter by data type, source, date range, and label category.
* Threads: Users can view charts of thread volumes and label volumes within a thread, if the data is in thread form, such as call transcripts or email chains. Users can also filter by data type, source, date range, and label category.
* Comparison: Users can compare different cohorts of data against each other, such as different sources, time periods, or label categories. Users can also filter by data type, source, date range, and label category.
References: 1: Communications Mining - Using Reports 2: Communications Mining - Reports 3: Communications Mining - Using Dashboards : [Communications Mining - Using Label Summary]
2: [Communications Mining - Using Trends] : [Communications Mining - Using Segments]
3: [Communications Mining - Using Threads] : [Communications Mining - Using Comparison]
質問 # 72
Who is responsible for devising a strategy to prioritize processes during the Business Case and Technical Validation phase?
- A. Business Analyst
- B. Solution Architect
- C. Automation Developer
- D. Project Manager
正解：B
解説：
The Solution Architect is responsible for devising a strategy to prioritize processes during the Business Case and Technical Validation phase. Their role involves assessing technical feasibility, scalability, and business value to determine process prioritization.
質問 # 73
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