Practice Free UiPath-SAIv1 Exam Online Questions
Question #51
Which of the following is a best practice when choosing a UiPath ML (Machine Learning) Extractor?
- A . The popularity of the ML Extractor among other UiPath users should be the primary factor. Opt for the ML Extractor that has the highest number of downloads or positive reviews.
- B . Consider the document types, language, and data quality. 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.
- C . The size of the ML Extractor is the most important factor to consider. 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.
- D . The cost of the ML Extractor should be the main consideration. Select the ML Extractor that offers the lowest price, regardless of its performance or suitability for the specific document understanding
needs.
Correct Answer: B
B
Explanation:
The best practice is to select an ML Extractor based on document types, language, and data quality.
Choosing a model specifically optimized for the type of document being processed ensures higher accuracy and reliability. The quality and diversity of the training data used to develop the model play a significant role in its performance.
Reference: UiPath ML Extractors
B
Explanation:
The best practice is to select an ML Extractor based on document types, language, and data quality.
Choosing a model specifically optimized for the type of document being processed ensures higher accuracy and reliability. The quality and diversity of the training data used to develop the model play a significant role in its performance.
Reference: UiPath ML Extractors
Question #52
Which features in Generative Annotation are automatically enabled on datasets in Communication Mining technology?
- A . Taxonomy Uploading
- B . Assisted Labelling
- C . Preview Mode
- D . Sentiment Analysis
Correct Answer: B
B
Explanation:
In UiPath Communication Mining, the Generative Annotation feature automatically enables Assisted Labelling on datasets. Assisted Labelling helps to accelerate the labeling process by automatically suggesting relevant labels based on the content of the communications. This feature significantly
improves the efficiency of the model training process by reducing the manual effort required to label large datasets.
For more details, refer to:
UiPath Communication Mining Documentation: Generative Annotation and Assisted Labelling
Labeling and Annotation in UiPath Communications Mining: UiPath AI Center Documentation
B
Explanation:
In UiPath Communication Mining, the Generative Annotation feature automatically enables Assisted Labelling on datasets. Assisted Labelling helps to accelerate the labeling process by automatically suggesting relevant labels based on the content of the communications. This feature significantly
improves the efficiency of the model training process by reducing the manual effort required to label large datasets.
For more details, refer to:
UiPath Communication Mining Documentation: Generative Annotation and Assisted Labelling
Labeling and Annotation in UiPath Communications Mining: UiPath AI Center Documentation
Question #53
What information should be filled in when adding an entity label for the OOB (Out Of the Box) labeling template?
- A . Name. Data Type. Attribute name, and Color.
- B . Name, Data Type. Attribute name. Shortcut, and Color.
- C . Name, Shortcut, and Color.
- D . Name. Input to be labeled. Attribute name. Shortcut, and Color.
Correct Answer: D
D
Explanation:
The OOB labeling template is a predefined template that you can use to label your text data for entity recognition models. The template comes with some preset labels and text components, but you can also add your own labels using the General UI or the Advanced Editor. When you add an entity label, you need to fill in the following information:
Name: the name of the new label. This is how the label will appear in the labeling tool and in the exported data.
Input to be labeled: the text component that you want to label. You can choose from the existing text components in the template, such as Date, From, To, CC, and Text, or you can add your own text components using the Advanced Editor. The text component determines the scope of the text that can be labeled with the entity label.
Attribute name: the name of the attribute that you want to extract from the text. You can use this to create attributes such as customer name, city name, telephone number, and so on. You can add more than one attribute for the same label by clicking on + Add new.
Shortcut: the hotkey that you want to assign to the label. You can use this to label the text faster by using the keyboard. Only single letters or digits are supported.
Color: the color that you want to assign to the label. You can use this to distinguish the label from the others visually.
References: AI Center – Managing Data Labels, Data Labeling for Text – Public Preview
D
Explanation:
The OOB labeling template is a predefined template that you can use to label your text data for entity recognition models. The template comes with some preset labels and text components, but you can also add your own labels using the General UI or the Advanced Editor. When you add an entity label, you need to fill in the following information:
Name: the name of the new label. This is how the label will appear in the labeling tool and in the exported data.
Input to be labeled: the text component that you want to label. You can choose from the existing text components in the template, such as Date, From, To, CC, and Text, or you can add your own text components using the Advanced Editor. The text component determines the scope of the text that can be labeled with the entity label.
Attribute name: the name of the attribute that you want to extract from the text. You can use this to create attributes such as customer name, city name, telephone number, and so on. You can add more than one attribute for the same label by clicking on + Add new.
Shortcut: the hotkey that you want to assign to the label. You can use this to label the text faster by using the keyboard. Only single letters or digits are supported.
Color: the color that you want to assign to the label. You can use this to distinguish the label from the others visually.
References: AI Center – Managing Data Labels, Data Labeling for Text – Public Preview
Question #54
What are the two main data extraction methodologies used in document understanding processes?
- A . Hybrid and manual data extraction.
- B . Rule-based and model-based data extraction.
- C . Rule-based and hybrid data extraction.
- D . Manual and model-based data extraction.
Correct Answer: B
B
Explanation:
According to the UiPath documentation, there are two common types of data extraction methodologies used in document understanding processes: rule-based data extraction and model-based data extraction12. Rule-based data extraction targets structured documents, such as forms, invoices, or receipts, that have a fixed layout and a predefined set of fields. Rule-based data extraction uses predefined rules, such as regular expressions, keywords, or coordinates, to locate and extract the relevant data from the documents1. Model-based data extraction is used to process semi-structured and unstructured documents, such as contracts, emails, or reports, that have a variable layout and a diverse set of fields. Model-based data extraction uses machine learning models, such as neural networks, to learn from examples and extract the relevant data from the documents1. Both methodologies have their advantages and limitations, and depending on the use case, they can be used separately or in combination, in a hybrid approach2.
References: 1: Data Extraction Overview 2: Document Processing with Improved Data Extraction
B
Explanation:
According to the UiPath documentation, there are two common types of data extraction methodologies used in document understanding processes: rule-based data extraction and model-based data extraction12. Rule-based data extraction targets structured documents, such as forms, invoices, or receipts, that have a fixed layout and a predefined set of fields. Rule-based data extraction uses predefined rules, such as regular expressions, keywords, or coordinates, to locate and extract the relevant data from the documents1. Model-based data extraction is used to process semi-structured and unstructured documents, such as contracts, emails, or reports, that have a variable layout and a diverse set of fields. Model-based data extraction uses machine learning models, such as neural networks, to learn from examples and extract the relevant data from the documents1. Both methodologies have their advantages and limitations, and depending on the use case, they can be used separately or in combination, in a hybrid approach2.
References: 1: Data Extraction Overview 2: Document Processing with Improved Data Extraction
Question #55
Which of the following extractors can be used for Data Extraction Scope activity?
- A . Intelligent Form Extractor, Machine Learning Extractor. Logic Extractor, and Regex Based Extractor.
- B . Full Extractor. Machine Learning Extractor, Intelligent Form Extractor, and Regex Based Extractor.
- C . Form Extractor Incremental Extractor Machine Learning Extractor and Intelligent Form Extractor
- D . Regex Based Extractor. Form Extractor. Intelligent Form Extractor, and Machine Learning Extractor.
Correct Answer: D
D
Explanation:
The Data Extraction Scope activity provides a scope for extractor activities, enabling you to configure them according to the document types defined in your taxonomy. The output of the activity is stored in an ExtractionResult variable, containing all automatically extracted data, and can be used as input for the Export Extraction Results activity. This activity also features a Configure Extractors wizard, which lets you specify exactly what fields from the document types defined in the taxonomy you want to extract1.
The extractors that can be used for Data Extraction Scope activity are:
Regex Based Extractor: This extractor enables you to use regular expressions to extract data from text documents. You can define your own expressions or use the predefined ones from the Regex Based Extractor Configuration wizard2.
Form Extractor: This extractor enables you to extract data from semi-structured documents, such as invoices, receipts, or purchase orders, based on the position and relative distance of the fields. You can define the templates for each document type using the Form Extractor Configuration wizard3.
Intelligent Form Extractor: This extractor enables you to extract data from semi-structured documents, such as invoices, receipts, or purchase orders, based on the labels and values of the fields. You can define the fields for each document type using the Intelligent Form Extractor Configuration wizard.
Machine Learning Extractor: This extractor enables you to extract data from any type of document, using a machine learning model that is trained on your data. You can use the predefined models from UiPath or your own custom models hosted on AI Center or other platforms. You can configure the fields and the model for each document type using the Machine Learning Extractor Configuration wizard.
References: 1: Data Extraction Scope 2: Regex Based Extractor 3: Form Extractor: Intelligent Form Extractor: Machine Learning Extractor
D
Explanation:
The Data Extraction Scope activity provides a scope for extractor activities, enabling you to configure them according to the document types defined in your taxonomy. The output of the activity is stored in an ExtractionResult variable, containing all automatically extracted data, and can be used as input for the Export Extraction Results activity. This activity also features a Configure Extractors wizard, which lets you specify exactly what fields from the document types defined in the taxonomy you want to extract1.
The extractors that can be used for Data Extraction Scope activity are:
Regex Based Extractor: This extractor enables you to use regular expressions to extract data from text documents. You can define your own expressions or use the predefined ones from the Regex Based Extractor Configuration wizard2.
Form Extractor: This extractor enables you to extract data from semi-structured documents, such as invoices, receipts, or purchase orders, based on the position and relative distance of the fields. You can define the templates for each document type using the Form Extractor Configuration wizard3.
Intelligent Form Extractor: This extractor enables you to extract data from semi-structured documents, such as invoices, receipts, or purchase orders, based on the labels and values of the fields. You can define the fields for each document type using the Intelligent Form Extractor Configuration wizard.
Machine Learning Extractor: This extractor enables you to extract data from any type of document, using a machine learning model that is trained on your data. You can use the predefined models from UiPath or your own custom models hosted on AI Center or other platforms. You can configure the fields and the model for each document type using the Machine Learning Extractor Configuration wizard.
References: 1: Data Extraction Scope 2: Regex Based Extractor 3: Form Extractor: Intelligent Form Extractor: Machine Learning Extractor
Question #56
What are the available options for Scoring in Document Manager, that apply only to string content type?
- A . Exact match and Naive string search.
- B . Exact match and Phonetic matching.
- C . Exact match and Levenshtein.
- D . Exact match and Finite state automation-based search.
Correct Answer: C
C
Explanation:
According to the UiPath documentation, the available options for Scoring in Document Manager, that apply only to string content type, are exact match and Levenshtein. Exact match is a scoring strategy that considers a prediction to be correct only if it exactly matches the true value. Levenshtein is a scoring strategy that measures the similarity between two strings by counting the minimum number of edits (insertions, deletions, or substitutions) required to transform one string into another. The lower the Levenshtein distance, the higher the score. These options can be configured in the Advanced tab of the Edit Field window for string fields.
References:
Document Understanding – Create and Configure Fields Document Understanding – Training High Performing Models
C
Explanation:
According to the UiPath documentation, the available options for Scoring in Document Manager, that apply only to string content type, are exact match and Levenshtein. Exact match is a scoring strategy that considers a prediction to be correct only if it exactly matches the true value. Levenshtein is a scoring strategy that measures the similarity between two strings by counting the minimum number of edits (insertions, deletions, or substitutions) required to transform one string into another. The lower the Levenshtein distance, the higher the score. These options can be configured in the Advanced tab of the Edit Field window for string fields.
References:
Document Understanding – Create and Configure Fields Document Understanding – Training High Performing Models
Question #57
Which of the following statements is correct in the context of migrating a schema from Document Manager to a Modern Project?
- A . If you import a schema into a document type that already contains a schema, the import will be successful and the schema will be updated with the missing field.
- B . If you import a schema into a document type that already contains a schema, the import will fail only if documents were already uploaded.
- C . If you import a schema into a document type that already contains a schema, the import will fail.
- D . If you import a schema into a document type that already contains a schema, the import will be successful and the schema will be overridden.
Correct Answer: D
D
Explanation:
When migrating a schema from Document Manager to a Modern Project, UiPath will override any existing schema associated with the document type if you attempt to import a new schema. This behavior is confirmed in UiPath’s handling of document types and schema imports, where the new schema will replace the old one, ensuring that the latest version is applied to the project. This helps maintain consistency in schema configurations during migrations, especially when schemas are updated or need to be standardized
D
Explanation:
When migrating a schema from Document Manager to a Modern Project, UiPath will override any existing schema associated with the document type if you attempt to import a new schema. This behavior is confirmed in UiPath’s handling of document types and schema imports, where the new schema will replace the old one, ensuring that the latest version is applied to the project. This helps maintain consistency in schema configurations during migrations, especially when schemas are updated or need to be standardized
Question #58
How should the data be managed after extraction in the UiPath Document Understanding process?
- A . Directly pass the extracted data to other processes via Arguments.
- B . Serialize the data and store it in a storage bucket or similar location.
- C . Store the data temporarily in local folders before processing.
- D . Keep the data only in the queue items without serialization.
Correct Answer: B
B
Explanation:
After the data is extracted using the UiPath Document Understanding framework, it is crucial to handle the data securely and efficiently for further use in other processes or storage. Serialization is often used to convert the extracted data into a format suitable for storage or transmission. By serializing the data, it can be stored in storage buckets (such as cloud storage or a database) and accessed later as needed for subsequent processing. This approach ensures that data is preserved, secure, and available for other workflows or automations. (Source: UiPath Document Understanding Documentation).
B
Explanation:
After the data is extracted using the UiPath Document Understanding framework, it is crucial to handle the data securely and efficiently for further use in other processes or storage. Serialization is often used to convert the extracted data into a format suitable for storage or transmission. By serializing the data, it can be stored in storage buckets (such as cloud storage or a database) and accessed later as needed for subsequent processing. This approach ensures that data is preserved, secure, and available for other workflows or automations. (Source: UiPath Document Understanding Documentation).
Question #59
What information is required when creating a data labeling session?
- A . Data labeling session name and dataset.
- B . Data labeling name, dataset. and Al Center project.
- C . Dataset and Al Center project.
- D . Data labeling name, language, and number of documents.
Correct Answer: A
A
Explanation:
When creating a data labeling session in UiPath AI Center, the key pieces of information required are:
The data labeling session name: A unique identifier for the session.
The dataset: The data that will be used in the labeling session.
For more details, refer to:
UiPath AI Center Documentation: Data Labeling Sessions
A
Explanation:
When creating a data labeling session in UiPath AI Center, the key pieces of information required are:
The data labeling session name: A unique identifier for the session.
The dataset: The data that will be used in the labeling session.
For more details, refer to:
UiPath AI Center Documentation: Data Labeling Sessions
Question #60
As a best practice, who should perform the data labeling?
- A . Data Scientists.
- B . Automation RPA Developers.
- C . Business Analysts.
- D . Subject Matter Experts.
Correct Answer: D
D
Explanation:
As a best practice, Subject Matter Experts (SMEs) should perform the data labeling in UiPath Communications Mining or Document Understanding projects. SMEs have the in-depth knowledge of the specific content and context, which ensures that the data is labeled correctly and meaningfully for training machine learning models. Their expertise is essential for accurate taxonomy and data preparation
D
Explanation:
As a best practice, Subject Matter Experts (SMEs) should perform the data labeling in UiPath Communications Mining or Document Understanding projects. SMEs have the in-depth knowledge of the specific content and context, which ensures that the data is labeled correctly and meaningfully for training machine learning models. Their expertise is essential for accurate taxonomy and data preparation