UiPath-SAIv1 Questions Bank

When uploading an ML package in UiPath AI Center with the Enable Training option inactive, several validation checks are performed:

There must be a non-empty root folder.

A requirements.txt file must be present to define dependencies.

A main.py file should be in the root folder, which implements a class Main.

The class must implement the necessary methods, such as __init__ and predict.

For more details, refer to:

UiPath AI Center Documentation: ML Package Validation

 The Reports section of the dataset navigation bar in UiPath Communication Mining allows users to access detailed, quervable charts, statistics, and customizable dashboards that provide valuable insights and analysis on their communications data1. The Reports section has up to six tabs, depending on the data type, each designed to address different reporting needs2:

Dashboard: Users can create custom dashboard views using data from other tabs, such as label summary, trends, segments, threads, and comparison. Dashboards are specific to the dataset and can be edited, deleted, or renamed by users with the ‘Modify datasets’ permission3.

Label Summary: Users can view high-level summary statistics for labels, such as volume, precision, recall, and sentiment. Users can also filter by data type, source, date range, and label category.

Trends: Users can view charts for verbatim volume, label volume, and sentiment over a selected time period. Users can also filter by data type, source, date range, and label category.

Segments: Users can view charts comparing label volumes to verbatim metadata fields, such as sender domain, channel, or language. Users can also filter by data type, source, date range, and label category.

Threads: Users can view charts of thread volumes and label volumes within a thread, if the data is in thread form, such as call transcripts or email chains. Users can also filter by data type, source, date range, and label category.

Comparison: Users can compare different cohorts of data against each other, such as different sources, time periods, or label categories. Users can also filter by data type, source, date range, and label category.

References: 1: Communications Mining - Using Reports 2: Communications Mining - Reports 3: Communications Mining - Using Dashboards : [Communications Mining - Using Label Summary] : [Communications Mining - Using Trends] : [Communications Mining - Using Segments] : [Communications Mining - Using Threads] : [Communications Mining - Using Comparison]

 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