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質問 # 108
How does UiPath Document Understanding handle structured documents with fixed formats?
- A. It uses optical character recognition (OCR) to extract text from the documents.
- B. It requires manual data entry, as structured documents cannot be processed automatically.
- C. It relies on AI models for natural language processing.
- D. It leverages predefined templates for accurate data extraction.
正解：D
質問 # 109
What does Data Extraction do?
- A. Applies rules for validating that the information extracted from a document is correct.
- B. Identifies and extracts specific information that should be processed.
- C. Digitizes the document that should be processed.
- D. Identifies words and their coordinates from images and PDFs.
正解：B
解説：
Data Extraction identifies and extracts specific information from documents to be processed by automations.
This is a key part of UiPath's Document Understanding Framework, enabling bots to process structured and unstructured documents effectively.
質問 # 110
What does the Label Trends table in UiPath Communications Mining show?
- A. How the top 10 labels for a given time period perform compared to the previous period and their change in rank.
- B. How the top 10 entities for a given time period perform compared to the previous period and their change in rank.
- C. How the top 10 senders for a given time period perform compared to the previous period and their change in rank.
- D. How the top 10 labels and entities for a given time period perform compared to the previous period and their change in rank.
正解：A
解説：
The Label Trends table in UiPath Communications Mining shows the trend of the top 10 highest volume labels over the selected time period, as well as their percentage change and rank change compared to the previous period1. The table allows users to quickly identify which labels are increasing or decreasing in volume, and by how much, over time. The table also shows the net sentiment score for each label, which is calculated as the difference between the positive and negative sentiment probabilities for each verbatim2. The table can be filtered by data type, source, date range, and label category. Users can also sort the table by label name, volume, percentage change, rank change, or net sentiment1.
References: 1: Trends 2: Sentiment Analysis
質問 # 111
What are the out-of-the-box model types available in AI Center?
- A. Pre-trained, fine-tunable, and reviewed.
- B. Custom training, fine-tunable, and reviewed.
- C. Pre-trained, custom training, and fine-tunable.
- D. Pre-trained, custom training, and reviewed.
正解：C
質問 # 112
Exhibit:
When a developer is examining a suspended state upon reaching a breakpoint, which activity will the Executor be directed to if Step Out is selected from the Debug section in UiPath Studio's ribbon interface?
- A. S4
- B. S2
- C. S3
- D. A1
正解：C
質問 # 113
What is supervised learning?
- A. 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. - 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 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.
- D. 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).
正解：A
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 # 114
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.
正解：A
解説：
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. The training 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
質問 # 115
When creating a training dataset, what is the recommended number of samples for the Classification fields?
- A. 50-200 document samples from each class.
- B. 5-10 document samples from each class.
- C. 20-50 document samples from each class.
- D. 10-20 document samples from each class.
正解：C
質問 # 116
What should a UiPath Communications Mining taxonomy contain when it is being imported?
- A. Entity descriptions.
- B. Label descriptions.
- C. Label predictions.
- D. Entity predictions.
正解：B
質問 # 117
What function in the train.py file is responsible for persisting the trained model?
- A. __init_(self)
- B. train(setf. trainmg_directory}
- C. evaluate(self, evaluation_directory)
- D. save(self)
正解：D
解説：
In the context of a machine learning pipeline, specifically within the train.py file in UiPath AI models, the function responsible for persisting the trained model is typically named save(self). This function is invoked after the model has been trained, and it ensures that the model's state, weights, and configurations are saved to disk or another persistent storage medium. The saved model can then be reused for inference or further fine- tuning in future executions.
For more details, refer to:
* UiPath AI Center Documentation: Training Models
* Model Persistence: Saving Machine Learning Models in AI Center
質問 # 118
What is the benefit of making an ML Skill public?
- A. It provides additional security measures for the ML Skill.
- B. It allows access from outside of the UiPath environment.
- C. It enables automatic updates and enhancements to the ML Skill without user intervention.
- D. It allows access from inside of the UiPath environment.
正解：B
解説：
Making an ML Skill public in UiPath enables it to be accessed externally from the UiPath ecosystem. This can be beneficial if the ML Skill needs to be utilized in external applications, systems, or services beyond UiPath's automation environment. Public access expands the usability of the skill, allowing integration with other systems while maintaining security through managed endpoints.
質問 # 119
What components are part of the Document Understanding Process template?
- A. Load Taxonomy, Digitization. Classification, Data Extraction, and Data Validation Export.
- B. Load Taxonomy, Digitization. Categorization. Data Validation, and Export.
- C. Load Document. Categorization. Data Extraction, and Validation.
- D. Import. Classification. Text Extractor, and Data Validation.
正解：A
質問 # 120
What rule should be used in Taxonomy Manager for a text field that can have one of multiple known values?
- A. Starts with
- B. Ends with
- C. Possible values
- D. Contains
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 # 121
What is the primary function of the Wait for Classification Validation Task and Resume activity In UiPath's Document Understanding Framework?
- A. It prioritizes actions in Action Center based on document classification results, optimizing task management and allocation according to the importance of document classifications.
- B. It automatically validates classified data without human intervention, expediting document processing by removing the need for manual review and correction.
- C. It suspends the workflow until a specified document validation action is completed, ensuring human review and correction.
- D. It initiates the classification process for documents across different platforms, ensuring consistent and accurate document organization and categorization.
正解：C
解説：
The "Wait for Classification Validation Task and Resume" activity in UiPath's Document Understanding Framework is primarily used to halt or suspend the workflow until a specified document classification validation task is completed by a human. This activity is part of the broader workflow to ensure that when automatic classification of documents cannot be confidently achieved, a human-in-the-loop (HITL) approach is followed to validate or correct classifications. Once the validation is performed in UiPath's Action Center by a human, the workflow is resumed, ensuring the proper handling of documents that require review and correction.
This is aligned with the design of the Action Center, which is integrated into UiPath's Document Understanding Framework. When dealing with document classification or extraction confidence issues, manual human validation tasks are often required, which is what this activity manages. It facilitates human oversight, preventing the automation from proceeding with potentially incorrect classifications.
Reference from UiPath documentation:
* UiPath Action Center explains how humans are involved in validation tasks to handle cases where classification or extraction needs manual review.
* Wait for Task and Resume Activity in UiPath Documentation explains how it waits for a task (such as document validation) to be completed in the Action Center before resuming the workflow.
For more details, you can consult the official UiPath documents:
* UiPath Document Understanding Framework
* Wait for Classification Validation Task and Resume
This functionality ensures that incorrect data processing due to automation can be caught and rectified by a human, improving accuracy in document handling workflows.
質問 # 122
Which of the following is an indicator that sufficient training has been completed for a model in UiPath Communications Mining?
- A. A model rating of 70-90 or better.
- B. A model rating of 50-60.
- C. A model rating of 30-40.
- D. A model rating of 40-50.
正解：A
解説：
The model rating is a proprietary score that assesses the overall health and performance of a model in UiPath Communications Mining. It considers four main factors: balance, underperforming labels, coverage, and all labels. The model rating is a score from 0 to 100, which equates to a rating of 'Poor' (0-49), 'Average' (50-
69), 'Good' (70-89) or 'Excellent' (90-100). A model rating of 70-90 or better indicates that the model has sufficient training and performs well in all of the most important areas. A model rating of 70-90 or better also means that the model has a balanced and representative training data, a low number of labels with performance issues or warnings, a high coverage of the dataset by informative labels, and a high average precision of all labels.
References: Communications Mining - Model Rating, Communications Mining - Understanding and improving model performance
質問 # 123
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