Practice Free UiPath-SAIv1 Exam Online Questions
Question #21
What is one of the purposes of the Config file in the UiPath Document Understanding Template?
- A . It contains the configuration settings for the UiPath Robot and Orchestrator integration.
- B . It stores the API keys and authentication credentials for accessing external services.
- C . It specifies the output file path and format for the processed documents.
- D . It defines the input document types and formats supported by the template.
Correct Answer: B
B
Explanation:
The Config file in the UiPath Document Understanding Template is a JSON file that contains various parameters and values that control the behavior and functionality of the template. One of the purposes of the Config file is to store the API keys and authentication credentials for accessing external services, such as the Document Understanding API, the Computer Vision API, the Form Recognizer API, and the Text Analysis API. These services are used by the template to perform document classification, data extraction, and data validation tasks. The Config file also allows the user to customize the template according to their needs, such as enabling or disabling human-in-the-loop validation, setting the retry mechanism, defining the custom success logic, and specifying the taxonomy of document types.
References: Document Understanding Process: Studio Template, Automation Suite – Document Understanding configuration file
B
Explanation:
The Config file in the UiPath Document Understanding Template is a JSON file that contains various parameters and values that control the behavior and functionality of the template. One of the purposes of the Config file is to store the API keys and authentication credentials for accessing external services, such as the Document Understanding API, the Computer Vision API, the Form Recognizer API, and the Text Analysis API. These services are used by the template to perform document classification, data extraction, and data validation tasks. The Config file also allows the user to customize the template according to their needs, such as enabling or disabling human-in-the-loop validation, setting the retry mechanism, defining the custom success logic, and specifying the taxonomy of document types.
References: Document Understanding Process: Studio Template, Automation Suite – Document Understanding configuration file
Question #22
What is one best practice when designing a UiPath Communications Mining label taxonomy?
- A . Each label should be identifiable from the text of the individual verbatim (not thread) to which it will be applied.
- B . Each label should include customer experience/sentiment analysis in its coverage.
- C . Each parent label should have at least 3 children labels to ensure specificity.
- D . Each label should overlap sliqhtlv with a few distinct others so we ensure 100% coveraqe.
Correct Answer: A
A
Explanation:
A label taxonomy is a hierarchical structure of concepts that you want to capture from your communications data, such as emails, chats, or calls. Each label represents a specific concept that serves a business purpose and is aligned to your objectives. A label taxonomy can have multiple levels of hierarchy, where each child label is a subset of its parent label. For example, a parent label could be “Product Feedback” and a child label could be “Product Feature Request” or “Product Bug Report”. A label taxonomy is used to train a machine learning model that can automatically classify your communications data according to the labels you defined1.
One of the best practices for designing a label taxonomy is to ensure that each label is clearly identifiable from the text of the individual verbatim (not thread) to which it will be applied. A verbatim is a single unit of communication, such as an email message, a chat message, or a call transcript segment. A thread is a collection of related verbatims, such as an email conversation, a chat session, or a call recording. When you train your model, you will apply labels to verbatims, not threads, so it is important that each label can be recognized from the verbatim text alone, without relying on the context of the thread. This will help the model to learn the patterns and features of each label and to generalize to new data. It will also help you to maintain consistency and accuracy when labelling your data2.
References: 1: Communications Mining – Taxonomies 2: Communications Mining – Label hierarchy and best practice
A
Explanation:
A label taxonomy is a hierarchical structure of concepts that you want to capture from your communications data, such as emails, chats, or calls. Each label represents a specific concept that serves a business purpose and is aligned to your objectives. A label taxonomy can have multiple levels of hierarchy, where each child label is a subset of its parent label. For example, a parent label could be “Product Feedback” and a child label could be “Product Feature Request” or “Product Bug Report”. A label taxonomy is used to train a machine learning model that can automatically classify your communications data according to the labels you defined1.
One of the best practices for designing a label taxonomy is to ensure that each label is clearly identifiable from the text of the individual verbatim (not thread) to which it will be applied. A verbatim is a single unit of communication, such as an email message, a chat message, or a call transcript segment. A thread is a collection of related verbatims, such as an email conversation, a chat session, or a call recording. When you train your model, you will apply labels to verbatims, not threads, so it is important that each label can be recognized from the verbatim text alone, without relying on the context of the thread. This will help the model to learn the patterns and features of each label and to generalize to new data. It will also help you to maintain consistency and accuracy when labelling your data2.
References: 1: Communications Mining – Taxonomies 2: Communications Mining – Label hierarchy and best practice
Question #23
What are the options available in the Export Now tab of the Export Files dialog box in Document Manager?
- A . Download to Excel. Download, and Export to Al Center.
- B . Download to Excel. Export to Al Center, and Export All.
- C . Download to Excel and Export All.
- D . Export to Al Center, Export All. and Download.
Correct Answer: B
B
Explanation:
Document Understanding documentation, when using the "Export Now" tab in the Export Files dialog box within the Document Manager, the available options include: Download to Excel C This allows downloading the dataset locally in an Excel format.
Export to AI Center C This exports the data directly to the AI Center for use in model training or evaluation.
Export All C This option exports all documents regardless of labeling or filtering status.
These features are designed to facilitate exporting labeled data for further processing in AI Center, or locally for offline analysis
B
Explanation:
Document Understanding documentation, when using the "Export Now" tab in the Export Files dialog box within the Document Manager, the available options include: Download to Excel C This allows downloading the dataset locally in an Excel format.
Export to AI Center C This exports the data directly to the AI Center for use in model training or evaluation.
Export All C This option exports all documents regardless of labeling or filtering status.
These features are designed to facilitate exporting labeled data for further processing in AI Center, or locally for offline analysis
Question #24
How do the prediction mechanisms for labels and general fields differ in the UiPath Communications Mining platform?
- A . Label predictions are made based on the text of the message as well as some metadata properties, while general fields are learnt from the assigned span of text and the context of the text surrounding that span.
- B . Label predictions rely solely on metadata properties, general fields are learnt from the presence of certain key phrases in the text.
- C . Labels predictions are based on assigned span of text and entities are predicted solely from metadata properties.
- D . Labels and general fields are both predicted based on the text of the message, with no consideration given to metadata properties or contextual surrounding text.
Correct Answer: A
A
Explanation:
In the UiPath Communications Mining platform, label predictions utilize the text of the message and additional metadata properties for classification. General fields, on the other hand, are predicted based on the span of text assigned during training and the context surrounding that text. This distinction ensures accurate predictions for different data types.
Reference: UiPath Communications Mining
A
Explanation:
In the UiPath Communications Mining platform, label predictions utilize the text of the message and additional metadata properties for classification. General fields, on the other hand, are predicted based on the span of text assigned during training and the context surrounding that text. This distinction ensures accurate predictions for different data types.
Reference: UiPath Communications Mining
Question #25
What is the definition of Deep Learning?
- A . A sub-field of artificial intelligence that enables systems to learn from data.
Systems learn from previous experience and information to deduce and predict future information. To do this they use algorithms that learn to perform a specific task without being explicitly programmed. - B . The theory and development of computer systems that are able to perform tasks that normally require human intelligence and decision making.
- C . A field of artificial intelligence that enables computers to gain high-level understanding from digital images or videos. If AI is the brain, then this is the eye that enables the computer to observe and understand. It works the same as the human eye.
- D . An area of machine learning concerned with artificial neural networks.
These are a series of algorithms that aim to recognize relationships in a set of data through a process that mimics biological neural networks.
Correct Answer: D
D
Explanation:
Deep learning is a subset of machine learning that uses multiple layers of artificial neural networks to learn from data and perform complex tasks. The term “deep” refers to the number of layers in the network, which can range from a few to hundreds or even thousands. Each layer consists of a set of nodes that perform mathematical operations on the input data and pass the output to the next layer. The network learns by adjusting the weights of the connections between the nodes based on the feedback from the desired output. Deep learning can handle various types of data, such as images, text, speech, or video, and can automatically extract features and patterns from them without human intervention. Deep learning is behind many applications of artificial intelligence, such as computer vision, natural language processing, speech recognition, and generative models123.
References: 1: What is Deep Learning? | IBM 2: What Is Deep Learning? Definition, Examples, and Careers | Coursera 3: Deep learning – Wikipedia
D
Explanation:
Deep learning is a subset of machine learning that uses multiple layers of artificial neural networks to learn from data and perform complex tasks. The term “deep” refers to the number of layers in the network, which can range from a few to hundreds or even thousands. Each layer consists of a set of nodes that perform mathematical operations on the input data and pass the output to the next layer. The network learns by adjusting the weights of the connections between the nodes based on the feedback from the desired output. Deep learning can handle various types of data, such as images, text, speech, or video, and can automatically extract features and patterns from them without human intervention. Deep learning is behind many applications of artificial intelligence, such as computer vision, natural language processing, speech recognition, and generative models123.
References: 1: What is Deep Learning? | IBM 2: What Is Deep Learning? Definition, Examples, and Careers | Coursera 3: Deep learning – Wikipedia
Question #26
Which of the following file types are supported for the DocumentPath property in the Classify Document Scope activity?
- A . .bmp, .pdf, .jpe, .psd
- B . .png, .gif, .jpe, .tiff
- C . .pdf, .jpeg, .raw, tif
- D . .jpe, .eps, .jpg, .tiff
Correct Answer: B
B
Explanation:
According to the UiPath documentation portal1, the DocumentPath property in the Classify Document Scope activity accepts the path to the document you want to validate. This field supports only strings and String variables. The supported file types for this property field are .png, .gif, .jpe, .jpg, .jpeg, .tiff, .tif, .bmp, and .pdf. Therefore, option B is the correct answer, as it contains four of the supported file types. Option A is incorrect, as .psd is not a supported file type. Option C is incorrect, as .raw is not a supported file type. Option D is incorrect, as .eps is not a supported file type. References: 1 Activities – Classify Document Scope – UiPath Documentation Portal
B
Explanation:
According to the UiPath documentation portal1, the DocumentPath property in the Classify Document Scope activity accepts the path to the document you want to validate. This field supports only strings and String variables. The supported file types for this property field are .png, .gif, .jpe, .jpg, .jpeg, .tiff, .tif, .bmp, and .pdf. Therefore, option B is the correct answer, as it contains four of the supported file types. Option A is incorrect, as .psd is not a supported file type. Option C is incorrect, as .raw is not a supported file type. Option D is incorrect, as .eps is not a supported file type. References: 1 Activities – Classify Document Scope – UiPath Documentation Portal
Question #27
Which of the following is true when creating an ML Package in UiPath Al Center?
- A . The package name cannot use any Python reserved keywords.
- B . The package name cannot contain any spaces or hyphens.
- C . The package name cannot exceed 10 characters in length.
- D . The package name cannot include any special characters such as "@", "S", or "#".
Correct Answer: A
A
Explanation:
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.
A
Explanation:
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.
Question #28
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 force the Ul to show predictions from that model version in explore
- C . To allow rollback of annotations to that model version.
- D . To delete all other model versions.
Correct Answer: B
B
Explanation:
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
B
Explanation:
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
Question #29
What activity from the Microsoft 365 package should be used to share a SharePoint file URL with specific permissions to a specific user?
- A . Upload File
- B . Share URL Link
- C . Get File-Folder
- D . Share File/Folder
Correct Answer: D
D
Explanation:
The Share File/Folder activity in the Microsoft 365 package is used to share a SharePoint file or folder with specific permissions to a user. This allows efficient collaboration by granting or restricting access as needed.
Reference: UiPath Microsoft 365 Activities
D
Explanation:
The Share File/Folder activity in the Microsoft 365 package is used to share a SharePoint file or folder with specific permissions to a user. This allows efficient collaboration by granting or restricting access as needed.
Reference: UiPath Microsoft 365 Activities
Question #30
For what type of documents is it recommended to use the RegEx Based Extractor?
- A . For structured or semi-structured documents in which layouts of different document providers have little to no variation.
- B . For structured or semi-structured documents in which layouts of different document providers vary greatly.
- C . For structured or semi-structured documents where the extraction method is based on a set of
FlexiCapture definition files. - D . For structured or semi-structured documents where for certain fields, data is always found in a strict, predictable format and context.
Correct Answer: D
D
Explanation:
The RegEx Based Extractor is most effective for documents where data fields follow a strict and predictable format, such as dates, invoice numbers, or specific patterns like email addresses. This method is well-suited for extracting information from structured or semi-structured documents with consistent formats across different documents, making it highly reliable for use cases where patterns are easily identifiable and do not vary significantly. (Source: UiPath Document Understanding documentation)
D
Explanation:
The RegEx Based Extractor is most effective for documents where data fields follow a strict and predictable format, such as dates, invoice numbers, or specific patterns like email addresses. This method is well-suited for extracting information from structured or semi-structured documents with consistent formats across different documents, making it highly reliable for use cases where patterns are easily identifiable and do not vary significantly. (Source: UiPath Document Understanding documentation)