UiPath Certified Professional Specialized AI Professional v1.0 Practice Tests 2025 | Pass UiPath-SAIv1 with confidence!
Practice UiPath Certified Professional UiPath-SAIv1 exam. Online Exam Practice Tests with detailed explanations!
NEW QUESTION # 97
Which feature should be used to inspect the available versions for activities employed within a workflow?
- A. Variables Panel
- B. Activities Panel
- C. Manage Packages
- D. Outline Panel
Answer: C
NEW QUESTION # 98
What is the name of the web application that allows users to prepare, review, and make corrections to datasets required for Machine Learning models?
- A. Data Manager.
- B. Digitization.
- C. ML Extractor.
- D. Document Manager.
Answer: A
Explanation:
Data Manager is a web application that allows users to prepare, review, and make corrections to datasets required for Machine Learning models. Data Manager enables users to create and manage datasets, label data, validate and export data, and monitor data quality and progress. Data Manager supports various types of data, such as documents, images, text, and tables. Data Manager is integrated with AI Center, where users can train and deploy Machine Learning models using the datasets created or modified in Data Manager12.
References: 1: Data Manager Overview 2: AI Center - About Datasets
NEW QUESTION # 99
While training a UiPath Communications Mining model, the Search feature was used to pin a certain label on a few communications. After retraining, the new model version starts to predict the tagged label but infrequently and with low confidence.
According to best practices, what would be the correct next step to improve the model's predictions for the label, in the "Explore" phase of training?
- A. Use the "Search" feature to pin the label to more communications.
- B. Use the "Rebalance" training mode to pin the label to more communications.
- C. Use the 'Teach" training mode to pin the label to more communications.
- D. Use the "Low confidence" training mode to pin the label to more communications.
Answer: C
NEW QUESTION # 100
What actions can be performed on a pipeline which is in the running state?
- A. Remove and kill.
- B. Remove and restart.
- C. Kill.
- D. Kill, remove, and restart.
Answer: C
Explanation:
When a pipeline is in the running state in UiPath AI Center, the only action that can be performed is to Kill it.
This terminates the process immediately, stopping any further processing. Actions like removing or restarting the pipeline can only be performed after the pipeline has stopped.(Source: UiPath Pipeline documentation
NEW QUESTION # 101
What additional information can be included in the exported data, apart from the extraction results?
- A. The page number from which the field was extracted and the exact position on the page.
- B. The extraction confidence and the digitization confidence.
- C. The number of occurrences and the extraction confidence.
- D. The position on the page.
Answer: A
Explanation:
The exported data from the UiPath Document Understanding Template contains the extraction results in a JSON format, along with some additional information that can be useful for debugging or analysis purposes.
One of the additional information that can be included is the page number from which the field was extracted and the exact position on the page, represented by the coordinates of the bounding box. This information can help to locate the field on the original document image and to verify the accuracy of the extraction. The additional information can be enabled or disabled by setting the IncludeMetadata parameter to true or false in the Config file of the template.
References: Document Understanding Process: Studio Template, Export Results
NEW QUESTION # 102
Which of the following is true when creating an ML Package in UiPath Al Center?
- A. The package name cannot contain any spaces or hyphens.
- B. The package name cannot include any special characters such as "@", "S", or "#".
- C. The package name cannot exceed 10 characters in length.
- D. The package name cannot use any Python reserved keywords.
Answer: D
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.
NEW QUESTION # 103
What is the Machine Learning Extractor?
- A. A tool using machine learning models to identify and report on data targeted for data extraction.
- B. A tool that helps extract data from different document structures, and is particularly useful when the same document has multiple formats.
- C. A specialized model that can recognize multiple languages in the same document using API calls to a Hugging Face model with over 250 languages.
- D. An extraction model that can be enabled and trained in Al Center. For better accuracy. 25 documents per model are recommended to train the model.
Answer: B
NEW QUESTION # 104
What is the purpose of the generative classifier?
- A. To classify documents using generative models.
- B. To classify documents using a combination of generative and specialized models.
- C. To classify documents using AI Center out-of-the-box generative models.
- D. To classify documents using generative OCR.
Answer: A
NEW QUESTION # 105
How are UiPath RPA and AI Center used for process improvement?
- A. UiPath RPA and AI Center have overlapping functionalities, so they are not designed to work together in process improvement. Each tool serves different purposes and is used independently to achieve automation and AI capabilities, respectively.
- B. UiPath RPA and AI Center work together in process improvement by leveraging RPA capabilities to automate repetitive tasks and integrating AI Center to enhance decision-making and cognitive abilities.RPA bots can collect data, perform actions, and feed information to ML models in AI Center, which in turn can analyze the data and make predictions.
- C. UiPath RPA and AI Center are independent tools that do not work together in process improvement.
RPA focuses on task automation, while AI Center is primarily used for training and deploying machine learning models. - D. UiPath RPA and AI Center work together in process improvement by utilizing RPA bots to execute models deployed in Action Center that can be monitored in Insights. The ML models process data and generate insights, which are then used by the RPA bots to make informed decisions and carry out automated actions.
Answer: B
NEW QUESTION # 106
What additional information can be included in the exported data, apart from the extraction results?
- A. The page number from which the field was extracted and the exact position on the page.
- B. The extraction confidence and the digitization confidence.
- C. The number of occurrences and the extraction confidence.
- D. The position on the page.
Answer: A
Explanation:
When exporting data in UiPath Document Understanding workflows, additional information such as the page number and the exact position on the page can be included. This metadata is particularly useful for auditing or validating the extracted data.
NEW QUESTION # 107
Which role consumes ML Skills within customized workflows in Studio using the ML Skill activity from the UiPath.MLServices.Activities package?
- A. RPA Developer.
- B. Data Scientist.
- C. Administrator.
Answer: A
Explanation:
D Process Controller
NEW QUESTION # 108
Why is having high coverage important for an automation-focused use case in UiPath Communications Mining?
- A. High coverage on the model means that fewer communications will be sent for manual review and that fewer automatable processes are missed.
- B. High coverage ensures that the software consumes less computational resources, resulting in cost savings for the organization implementing the automation.
- C. The higher the coverage, the lower the model's recall, resulting in greater throughput of automatable processes.
- D. With high coverage, you can increase the amount of data provided downstream via Streams.
Answer: A
Explanation:
In UiPath Communications Mining, high coverage ensures that a larger proportion of communications is classified with meaningful labels, meaning fewer communications are sent for manual review and more processes are captured for automation. This leads to more effective automation, reducing the need for human intervention and ensuring that the automation use case is fully realized.
For more details, refer to:
* UiPath Communications Mining Performance Metrics: Coverage and Automation
NEW QUESTION # 109
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 parent label should have at least 3 children labels to ensure specificity.
- C. Each label should include customer experience/sentiment analysis in its coverage.
- D. Each label should overlap sliqhtlv with a few distinct others so we ensure 100% coveraqe.
Answer: A
NEW QUESTION # 110
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.
Answer: A
NEW QUESTION # 111
Which is the correct description of the Configure Extractors Wizard?
- A. A mandatory step in the extractor configuration that allows choosing which extractors are applied to each document type.
- B. A mandatory step in the extractor configuration that allows choosing which extractors are applied to each field.
- C. An optional step in the extractor configuration which allows choosing which extractors are applied to each document type.
- D. A mandatory step in the extractor configuration that allows choosing which extractors are applied to each document type and field.
Answer: D
NEW QUESTION # 112
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.
Answer: A
Explanation:
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
NEW QUESTION # 113
What components are part of the Document Understanding Process template?
- A. Load Taxonomy, Digitization. Categorization. Data Validation, and Export.
- B. Import. Classification. Text Extractor, and Data Validation.
- C. Load Document. Categorization. Data Extraction, and Validation.
- D. Load Taxonomy, Digitization. Classification, Data Extraction, and Data Validation Export.
Answer: D
Explanation:
The Document Understanding Process template is a fully functional UiPath Studio project template based on a document processing flowchart. It provides logging, exception handling, retry mechanisms, and all the methods that should be used in a Document Understanding workflow, out of the box. The template has an architecture decoupled from other connected automations and supports both attended and unattended processes with human-in-the-loop validation via Action Center. The template consists of the following components1:
* Load Taxonomy: This component loads the taxonomy file that defines the document types and fields to be extracted. The taxonomy file can be created using the Taxonomy Manager in Studio or the Data Manager web application.
* Digitization: This component converts the input document into a digital format that can be processed by the subsequent components. It uses the Digitize Document activity to perform OCR (optical character recognition) on the document and obtain a Document Object Model (DOM).
* Classification: This component determines the document type of the input document using the Classify Document Scope activity. It can use either a Keyword Based Classifier or a Machine Learning Classifier, depending on the configuration. The classification result is stored in a ClassificationResult variable.
* Data Extraction: This component extracts the relevant data from the input document using the Data Extraction Scope activity. It can use different extractors for different document types, such as the Form Extractor, the Machine Learning Extractor, the Regex Based Extractor, or the Intelligent Form Extractor. The extraction result is stored in an ExtractionResult variable.
* Data Validation: This component allows human validation and correction of the extracted data using the Present Validation Station activity. It opens the Validation Station window where the user can review and edit the extracted data, as well as provide feedback for retraining the classifiers and extractors. The validated data is stored in a DocumentValidationResult variable.
* Export: This component exports the validated data to a desired output, such as an Excel file, a database, or a downstream process. It uses the Export Extraction Results activity to convert the DocumentValidationResult variable into a DataTable variable, which can then be manipulated or written using other activities.
References: Document Understanding Process: Studio Template, Document Understanding Process - New Studio Template, Document Understanding Process Template in UiPath Studio
NEW QUESTION # 114
Which of the following extractors can be used for Data Extraction Scope activity?
- A. Full Extractor. Machine Learning Extractor, Intelligent Form Extractor, and Regex Based Extractor.
- B. Intelligent Form Extractor, Machine Learning Extractor. Logic 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.
Answer: 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 4: Intelligent Form Extractor
5: Machine Learning Extractor
NEW QUESTION # 115
Under what condition can a dataset be deleted in UiPath Al Center?
- A. If it has not been modified within the last 24 hours.
- B. If it is not being used in any active pipeline.
- C. If it is not associated with any ML skills.
- D. If it has never been used in a pipeline.
Answer: B
Explanation:
In UiPath AI Center, datasets can only be deleted if they are not being utilized in any active pipelines. This ensures that datasets tied to running or scheduled operations are not inadvertently deleted, which could disrupt ongoing processes. Once the dataset is confirmed to be unused in any active pipeline, it can safely be removed
NEW QUESTION # 116
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 fail only if documents were already uploaded.
- B. 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.
- 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 updated with the missing field.
Answer: B
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
NEW QUESTION # 117
What is the definition of a UiPath Communications Mining data source?
- A. A permissioned storage area within the platform which contains communications and labels.
- B. A collection of raw unlabeled communications data of a similar type, that can be associated with up to 10 datasets.
- C. The model that we create when training the platform to understand the data in those sources.
- D. A user-permissioned project containing a taxonomy with labels and entities.
Answer: B
NEW QUESTION # 118
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