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UiPath UiPath-SAIv1 Exam Syllabus Topics:

UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions:

1. Given the following scenario:
* You have a trained version of the Document Understanding Model with 1000 pages called v22.10.0.1.
* You have an evaluation dataset of 100 pages that gave a score of 0.72 for v22.10.0.1.
* The business team labeled 800 pages and they ask for an increment of the Model that would contain all
1000+800 pages.
What is the first recommended pipeline run configuration to create the new version?

A) Pipeline type: Evaluate
Package Major Version: 23.10.4.0 Package Minor Version: 0 Input Dataset: 1000+800 pages Evaluation Dataset: 100 pages
B) Pipeline type: Full Package Major Version: 23.10.4.0 Package Minor Version: 0 Input Dataset:
1000+800 pages Evaluation Dataset: 100 pages
C) Pipeline type: Training Package Major Version: 23.10.4.0 Package Minor Version: 1 Input Dataset:
1000+800 Evaluation Dataset: N/A
D) Pipeline type: Full
Package Major Version: 23.10.4.0 Package Minor Version: 1 Input Dataset: 800 pages Evaluation Dataset: 100 pages

2. What is supervised learning?

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 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.
C) 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.
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.

3. What are all the ways to deploy Al Center?

A) In cloud availability, on-premises, hybrid mode (Al Center on-premise + cloud Orchestrator), and Automation Suite.
B) In cloud availability, on-premises air-gapped, on-premises online, and hybrid mode {cloud Al Center + Orchestrator on-premise).
C) In cloud availability, on-premises air-gapped, on-premises online, hybrid mode (Al Center on-premise
+ Orchestrator on-premise). and Automation Suite.
D) In cloud availability, on-premises air-gapped, on-premises online, hybrid mode (cloud Al Center + Orchestrator on-premise). and Automation Suite.

4. Having the following Rules defined in the Taxonomy Manager for Billing Address field.

At the data extraction step. 42 W 80th St. West New York, NJ 1234, USA have been extracted (or the Billing Address field. When processing a Invoice using the DU process what will happen in (he Validation Station after data extraction step?

A) There will be an error for Billing Address field regarding the ContalnsNY rule, but the validation can be performed.
B) There will be a warning message for Billing Address field regarding the ContalnsNY rule, and the validation step will throw the user specified exception.
C) There will be an error for Billing Address field regarding the ContainsNY rule, and the validation step will throw the user specified exception.
D) There will be a warning message for Billing Address field regarding the ContalnsNY rule, but the validation can be performed.

5. In the general fields training, what actions does the Communications Mining Train feature guide you through?

A) It guides you through the completion of model training before starling entity training.
B) It guides you to start entity training without a dataset.
C) It guides you right from the moment you create a dataset with the next best action to take to advance your general fields training.
D) It guides you to focus solely on model refinement.

Solutions: