UiPath-SAIv1 Practice Questions

A.  

Applying OCR on a 10-page document.

B.  

Creation of a Document Validation Action in Action Center.

C.  

Using ML Classifier on a 21-page document.

D.  

Using Intelligent Form Extractor on a 5-page document with 0 successful extractions.

A.  

To allocate 20 training examples for each label.

B.  

To apply labels to a maximum amount of data to enhance model performance.

C.  

To formulate a comprehensive training set that closely reflects the entirety of the dataset.

D.  

To provide the platform with a collection of keywords associated with each label for predicting other instances.

A.  

ML skills can be created, stopped, redeployed, updated to a new package version, rolled back to a previous package version, modified to use or not use GPU. modified to use or not use Al units, made public or private, or deleted.

B.  

ML skills can be created, stopped, redeployed, updated to a new package version, rolled back to a previous package version, made public or private, or deleted.

C.  

ML skills can be created, stopped, redeployed, updated to a new package version, rolled back to a previous package version, modified to use or not use GPU. made public or private, or deleted.

D.  

ML skills can be created, updated to a new package version, rolled back to a previous package version, modified to use or not use GPU. made public or private, or deleted.

A.  

Load Taxonomy, Digitization, Categorization, Data Validation, and Export

B.  

Load Document, Categorization, Data Extraction, and Validation

C.  

Load Taxonomy, Digitization, Classification, Data Extraction, and Data Validation Export

D.  

Import, Classification, Text Extractor, and Data Validation

A.  

7 documents for training the model, and 8 for evaluating the model.

B.  

8 documents for training the model, and 7 for evaluating the model.

C.  

10 documents for training the model, and 5 for evaluating the model.

D.  

12 documents for training the model, and 3 for evaluating the model.

A.  

Creating taxonomies that focus solely on scanned documents.

B.  

Designing taxonomies without considering the need for post-processing rules.

C.  

Focusing on creating separate taxonomies for each document type to avoid confusion.

D.  

Grouping as many document types under the same taxonomy as possible.

A.  

The Document Understanding Process can't use Queues because items waiting for Human Validation for more than 10 days will be marked as Abandoned.

B.  

The Document Understanding Process can use Queues but the Auto Retry Functionality should be disabled.

C.  

The Document Understanding Process can use Queues but the Auto Retry Functionality should be enabled.

D.  

The Document Understanding Process can't use Queues because items waiting for Human Validation for more than 24h will be marked as Abandoned.

A.  

A collection of raw unlabeled communications data of a similar type, that can be associated with up to 10 datasets.

B.  

The model that we create when training the platform to understand the data in those sources.

C.  

A permissioned storage area within the platform which contains communications and labels.

D.  

A user-permissioned project containing a taxonomy with labels and entities.

A.  

By sending a GET request to /api/framework/projects/{projectld}/classifiers/generative_classifier/classification with a body containing prompts that specify the 'Invoice' and 'Receipt' names and descriptions.

B.  

By sending a POST request to yapi/framework/projectsy{projectld}/ctassifiers/generative_classifier/classification with a body containing prompts that specify the 'Invoice' and 'Receipt' names and descriptions.

C.  

By sending a PUT request to /api/framework/proiects/{projectld}/classifiers/generative_classifier/classification without any specific classification details in the body of the request.

D.  

By sending a POST request to /api/framework/projects/{projectld}/classifiers/generative_classifier/dassification with a body containing prompts that specify the 'Invoice' and 'Receipt' names and a description for their fields.

A.  

To measure and monitor service quality within a communications channel.

B.  

To track the time spent by employees working productively.

C.  

To track the number of responses that employees get to their outbound communications.

D.  

To measure the quality of the output of the model.