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UiPath-SAIv1 Exam Dumps : UiPath Certified Professional Specialized AI Professional v1.0

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UiPath Certified Professional Specialized AI Professional v1.0 Questions and Answers

Question 1

Which role is responsible for building and uploading ML (Machine Learning) models lo Al Center?

Options:

A.

RPA Developer.

B.

Process Controller.

C.

Administrator.

D.

Data Scientist.

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Question 2

What is supervised learning?

Options:

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.

Question 3

What is the purpose of the "Explore" phase in UiPath Communications Mining?

Options:

A.

To fully review and correctly tag the model version, regardless if it's "Live" or "Staging".

B.

To use the bulk label functionality, a helpful tool to quickly train the model when searching for specific terms.

C.

To provide each label/entity in a taxonomy with enough training examples so the model can make accurate predictions at scale.

D.

To review the clusters of similar communications from a data set that unsupervised learning automatically found.