UIPATH-SAIV1 LATEST EXAM PAPERS - EXAM UIPATH-SAIV1 FEES

UiPath-SAIv1 Latest Exam Papers - Exam UiPath-SAIv1 Fees

UiPath-SAIv1 Latest Exam Papers - Exam UiPath-SAIv1 Fees

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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q72-Q77):

NEW QUESTION # 72
In which of the following scenarios, the ML Classifier is the only recommended classifier to be used, according to best practice?

  • A. When the custom document types are very similar and file splitting is not necessary.
  • B. When the custom document types are not similar and file splitting is not necessary.
  • C. When the custom document types are very similar and file splitting is necessary.
  • D. When the custom document types are not similar and file splitting is necessary.

Answer: A

Explanation:
The ML Classifier is a document classifier that uses a machine learning model deployed as an ML Skill in AI Center to perform document classification tasks. The ML Classifier can work by default with Invoices, Purchase Orders, Receipts, 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 Classifier is the only recommended classifier to be used when the custom document types are very similar and file splitting is not necessary. This is because the ML Classifier can handle complex and ambiguous cases where the document types are hard to distinguish by rules or keywords, and can also learn from feedback and improve over time. File splitting is not necessary when the documents are single-page or have a consistent number of pages per document type3.
The other options are not correct because they are scenarios where other classifiers, such as the Keyword Based Classifier or the Intelligent Keyword Classifier, can be used in combination with the ML Classifier or instead of it. These classifiers are based on rules or keywords that can identify the document types based on their content or metadata, and can also perform file splitting if the documents are multi-page or have a variable number of pages per document type3.
References: 1: Machine Learning Classifier - UiPath Activities 2: Machine Learning Classifier Trainer - UiPath Document Understanding 3: Document Classification - UiPath Document Understanding


NEW QUESTION # 73
Which of the following extractors can be used for Data Extraction Scope activity?

  • A. Regex Based Extractor. Form Extractor. Intelligent Form Extractor, and Machine Learning Extractor.
  • B. Full Extractor. Machine Learning Extractor, Intelligent Form Extractor, and Regex Based Extractor.
  • C. Intelligent Form Extractor, Machine Learning Extractor. Logic Extractor, and Regex Based Extractor.
  • D. Form Extractor Incremental Extractor Machine Learning Extractor and Intelligent Form Extractor

Answer: A

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


NEW QUESTION # 74
Which of the following best describes the primary purpose of the Quality of Service (QoS) functionality in UiPath Communications Mining?

  • A. To measure the quality of the output of the model.
  • B. To measure and monitor service quality within a communications channel.
  • C. To track the number of responses that employees get to their outbound communications.
  • D. To track the time spent by employees working productively.

Answer: B

Explanation:
The Quality of Service (QoS) feature in UiPath Communications Mining is designed to monitor and measure the quality of service within a communications channel. It helps businesses understand how well their communications are being managed by evaluating different performance metrics such as response times and the quality of interactions. QoS ensures that communications are aligned with service level agreements and business expectations.
(Source: UiPath Communications Mining documentation)


NEW QUESTION # 75
What are the mandatory activities to be included in an automation workflow to allow a remote knowledge worker to pick up an action that validates the extracted data in the form of a Document Validation Action?

  • A. Document Understanding Process Activities.
  • B. Orchestration Process Activities.
  • C. Present Validation Station, Wait for Document Validation Action and Resume.
  • D. Create Document Validation Action, Wait for Document Validation Action and Resume.

Answer: D

Explanation:
To enable a remote knowledge worker to validate the extracted data from documents in Action Center, the automation workflow needs to include the following activities12:
* Create Document Validation Action: This activity creates an action of type Document Validation in Orchestrator Action Center, and returns an action object as output. The action object contains the information needed to resume the workflow after the human validation is completed. The input properties of this activity include the action details, such as title, priority, catalog, and folder, and the document validation data, such as the document object model, the document text, the taxonomy, and the automatic extraction results.
* Wait for Document Validation Action and Resume: This activity suspends the execution of the workflow until the human validation is done in Action Center, and then resumes it with the updated extraction results. The input property of this activity is the action object obtained from the Create Document Validation Action activity. The output property is the validated extraction results, which can be used for further processing or exporting.
References: 1: Create Document Validation Action 2: Wait for Document Validation Action and Resume


NEW QUESTION # 76
Which UiPath Communications Mining model performance factor relates to the proportion of messages in the dataset that have informative label predictions?

  • A. Coverage.
  • B. Balance.
  • C. Average label performance.
  • D. Underperforming labels.

Answer: A

Explanation:
In UiPath Communications Mining, the term Coverage refers to the proportion of messages in the dataset that have informative label predictions. This is an important metric that indicates how much of the dataset the model is able to classify with meaningful and relevant labels. High coverage means that the model is effectively assigning labels to a large portion of the data, which is crucial for ensuring the model's usefulness in automating communication mining tasks.
For more details, refer to:
* UiPath Communications Mining Performance Factors: Model Coverage and Accuracy
* Communications Mining: Coverage and Other Metrics


NEW QUESTION # 77
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