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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q184-Q189):

NEW QUESTION # 184
What is supervised learning?

A. 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.
B. 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.
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 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).

Answer: C

Explanation:
Supervised learning is one of the most popular and widely used machine learning approaches. It involves providing the algorithm with labeled input/output pairs, which serve as examples of the desired behavior or outcome. The algorithm then learns a function that can generalize from these examples and make predictions for new, unseen data. Supervised learning can be used for tasks such as classification, regression, and anomaly detection. Some common supervised learning algorithms are linear regression, logistic regression, decision trees, support vector machines, and neural networks.
References:
* UiPath AI Fabric - Machine Learning Concepts
* UiPath Document Understanding - Machine Learning Models
* UiPath Communications Mining - Overview

NEW QUESTION # 185
Which is a high-level view of the tabs within an AI Center project?

A. Datasets. Data Labeling. ML Packages. Pipelines, and ML Skills.
B. Dashboard. Datasets, Data Labeling. ML Packages. Pipelines, ML Skills, and ML Logs.
C. Datasets, Data Labeling. ML Packages, ML Training, ML Evaluation, ML Skills, and ML Logs.
D. Dashboard. Datasets. ML Packages. ML Training. ML Evaluation, and ML Logs.

Answer: B

Explanation:
A high-level view of the tabs within an AI Center project is as follows:
* Dashboard: This tab provides an overview of the project's status, such as the number of datasets, pipelines, packages, skills, and logs, as well as the AI Units consumption and quota.
* Datasets: This tab enables you to upload, view, and manage the datasets that are used for training and evaluating the ML models within the project. A dataset is a folder of storage containing arbitrary files and sub-folders1.
* Data Labeling: This tab enables you to upload raw data, annotate text data in the labeling tool (for classification or entity recognition), and use the labeled data to train ML models. It is also used by the human reviewer to re-label incorrect predictions as part of the feedback process2.
* ML Packages: This tab enables you to upload, view, and manage the ML packages and package versions within the project. An ML package is a group of package versions of the same package type, and a package version is a trained model that can be deployed to a skill3.
* Pipelines: This tab enables you to create, view, and manage the pipelines and pipeline runs within the project. A pipeline is a description of an ML workflow, including the functions and their order of execution, and a pipeline run is an execution of a pipeline based on code provided by the user4.
* ML Skills: This tab enables you to deploy, view, and manage the ML skills within the project. An ML skill is a live deployment of a package version, which can be consumed by an RPA workflow using an ML skill activity in UiPath Studio5.
* ML Logs: This tab enables you to view and filter the logs related to the project, such as the events, messages, and errors that occurred during the pipeline runs, skill deployments, and skill executions6.
References:
1: About Datasets 2: About Data Labeling 3: About ML Packages 4: About Pipelines 5: About ML Skills 6: About ML Logs

NEW QUESTION # 186
Which of the following is an indicator that sufficient training has been completed for a model in UiPath Communications Mining?

A. A model rating of 50-60.
B. A model rating of 40-50.
C. A model rating of 30-40.
D. A model rating of 70-90 or better.

Answer: D

NEW QUESTION # 187
Which OCR (Optical Character Recognition) option is recommended to be initially utilized for document processing in a project?

A. OmniPage.
B. Tesseract OCR.
C. UiPath Document OCR.
D. UiPath CJK (for Chinese. Japanese. Korean).

Answer: C

Explanation:
In UiPath's Document Understanding Framework, UiPath Document OCR is recommended as the initial OCR engine for processing documents. This OCR option is optimized for extracting text from a wide variety of document types and provides good accuracy with structured, semi-structured, and unstructured documents.
It is designed to work seamlessly within the UiPath Document Understanding process and is highly compatible with other UiPath activities and models, making it a preferred choice for starting document processing in most projects.
For more details, refer to:
* UiPath Document Understanding Framework: UiPath OCR Engines
* UiPath Document OCR: UiPath OCR Guide

NEW QUESTION # 188
What differentiates UiPath Communications Mining general fields trained from scratch from general fields that are pre-trained?

A. The trained-from-scratch general fields have better accuracy, while the pre-trained general fields have a longer training time.
B. The trained-from-scratch general fields are entirely user-trained, while the ones that are pre-trained are typically based on a set of standard or custom-defined rule
C. The trained-from-scratch general fields depend on the model version, while the ones that are pre-trained do not.
D. The trained-from-scratch general fields need to be normalized to be used in downstream automation, while the ones that are pre-trained can only be used in dashboards within the platform.

Answer: B

Explanation:
In UiPath Communications Mining, general fields that are trained from scratch require user-defined inputs and training data, making them highly customizable but dependent on the specific data provided by the user.
In contrast, pre-trained general fields are based on predefined rules and training models developed by UiPath.
These pre-trained fields offer out-of-the-box functionality and are optimized for common use cases, whereas user-trained fields offer more flexibility to meet specific business requirements.
(Source: UiPath Communications Mining documentation)

NEW QUESTION # 189
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