Free UiPath Certified Professional Specialized AI Professional v1.0 Practice Questions

A.  

Supervised learning is a machine learning paradigm in which algorithms try to solve a problem only by trial and error and using a system of rewards and punishments.

There is no need for labeled input/output pairs to be presented. Instead, the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).

B.  

Supervised learning is a machine learning paradigm in which algorithms try to solve a problem in an uncertain, potentially complex environment only by trial and error and using a system of

rewards and punishments.

There are no correct answers, but feedback is given in the form of rewards and penalties.

C.  

Supervised learning is a machine learning paradigm with the goal of learning a function that maps input variables with output variables.

In every case there is a correct answer, so the aim is to train the model until it reaches an acceptable level of performance in predicting the outcome, at which point the learning stops.

D.  

Supervised learning is a machine learning paradigm that refers to algorithms that learn patterns from unlabeled data.

There are only input variables, but no corresponding output variables. The goal of the algorithm is to model the underlying structure of the data, but there are no correct answers and no

teachers.

A.  

Kill.

B.  

Remove and restart.

C.  

Kill, remove, and restart.

D.  

Remove and kill.

A.  

The assign's set value syntax should be PinMapping["John Doe"].

B.  

The "John Doe" key was not present in the dictionary.

C.  

The Dictionary was not initialized.

D.  

The assign's set value syntax should be PinMapping<"John Doe">.

A.  

It requires manual data entry, as structured documents cannot be processed automatically.

B.  

It relies on AI models for natural language processing.

C.  

It uses optical character recognition (OCR) to extract text from the documents.

D.  

It leverages predefined templates for accurate data extraction.

A.  

Name. Data Type. Attribute name, and Color.

B.  

Name, Data Type. Attribute name. Shortcut, and Color.

C.  

Name, Shortcut, and Color.

D.  

Name. Input to be labeled. Attribute name. Shortcut, and Color.

A.  

Project Manager

B.  

Solution Architect

C.  

Automation Developer

D.  

Business Analyst

A.  

ApplyOcrOnPdf -> Auto

B.  

ApplyOcrOnPdf -> Auto DegreeOfParallelism -> -1 DetectCheckboxes -> True

C.  

ApplyOcrOnPdf -> No DegreeOfParallelism -> True DetectCheckboxes -> True

D.  

ApplyOcrOnPdf -> Auto DegreeOfParallelism -> Max DetectCheckboxes -> True

E.  

ApplyOcrOnPdf -> No DegreeOfParallelism -> -1 DetectCheckboxes -> False

A.  

Use them as a template to create other tests.

B.  

Simply run the existing tests

C.  

Based on the use case developed, create test data to test existing and new tests.

D.  

Based on the use case developed, create test data to test existing tests.

A.  

Total verbatim count and proportion for each label.

B.  

Entity count for each metadata.

C.  

Verbatim content for each label.

D.  

Differences in verbatim length between Group A and Group

B.  

A.  

The understanding of the parent-label relationship when assigning label predictions.

B.  

The difference between label suggestions and label predictions in a training process.

C.  

The identification of a specific span of text as a value for a particular entity type.

D.  

The model's confidence that a specific concept exists within a communication.