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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q191-Q196):

NEW QUESTION # 191
What is supervised learning?

A. 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.
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 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).
D. 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.

Answer: A

NEW QUESTION # 192
What is the order of steps for automatically retraining and deploying a Document Understanding ML Model in Al Center with data from Document Validation Action?
Instructions: Drag the steps found on the "Left" and drop them on the "Right" in the correct order.

Answer:

Explanation:

To automatically retrain and deploy a Document Understanding Machine Learning (ML) Model in AI Center with data from the Document Validation Action, the steps should be followed in this order:
* Send Human in the Loop data to the used Dataset.
* This step involves sending the data that has been validated and corrected by human reviewers to the dataset. This data will be used for training the ML model.
* Use the Export feature from Document Manager using Scheduled Export.
* After the data is reviewed and validated, it needs to be exported from the Document Manager.
Scheduled Export automates this process, ensuring the dataset in AI Center is regularly updated with new data.
* Run Training/Full Pipeline in AI Center using Scheduled Pipeline with Auto-Retraining enabled.
* With the updated data in the dataset, the next step is to run the training or the full pipeline. The use of Scheduled Pipeline with Auto-Retraining ensures that the ML model is automatically retrained with the latest data.
* Enable the Auto Update feature in the ML Skill.
* Finally, enabling the Auto Update feature in the ML Skill ensures that the newly trained model is automatically deployed, making the improved model available for document understanding tasks.
Following these steps in the specified order allows for a streamlined process of continuously improving the ML model based on human-validated data, ensuring better accuracy and efficiency in document understanding tasks over time.

NEW QUESTION # 193
Who is responsible for devising a strategy to prioritize processes during the Business Case and Technical Validation phase?

A. Business Analyst
B. Solution Architect
C. Automation Developer
D. Project Manager

Answer: B

Explanation:
The Solution Architect is responsible for devising a strategy to prioritize processes during the Business Case and Technical Validation phase. Their role involves assessing technical feasibility, scalability, and business value to determine process prioritization.

NEW QUESTION # 194
What is the correct order of recommended steps when introducing new labels into a mature taxonomy?
Instructions: Drag the steps found on the "Left" and drop them on the "Right" in the correct order.

Answer:

Explanation:

* Create the new label by assigning it at least once. This is the initial step to introduce a new category or classification within your data taxonomy.
* Search for instances in the reviewed data where the new label should have been assigned, and apply the label accordingly. This step is crucial for maintaining consistency across your data set.
* Use 'Missed Label' to find all additional missing examples in the reviewed data. This action helps in identifying and rectifying any instances that may have been overlooked during the initial review.
* Check validation to ensure the label is performing as expected, and follow recommended actions if further training is required. Validation is key to assess the accuracy and performance of the new label within the system.

NEW QUESTION # 195
When processing a document type that comes in a high variety of layouts, what is the recommended data extraction methodology?

A. Rule-based data extraction.
B. Manual data extraction.
C. Model-based data extraction.
D. Hybrid data extraction.

Answer: D

Explanation:
Based on the classification of documents, there are two common types of data extraction methodologies: rule- based data extraction and model-based data extraction1. Rule-based data extraction targets structured documents, while model-based data extraction is used to process semi-structured and unstructured documents1. However, neither of these methods alone can handle the high variety of layouts that some document types may have. Therefore, a hybrid data extraction approach is recommended, which combines the strengths of both methods and allows for more flexibility and accuracy23. A hybrid data extraction approach can use one or more extractors, such as RegEx Based Extractor, Form Extractor, Intelligent Form Extractor, Machine Learning Extractor, or FlexiCapture Extractor, depending on the document type and the fields of interest3. The Data Extraction Scope activity in UiPath enables the configuration and execution of a hybrid data extraction methodology, by allowing the user to customize which fields are requested from each extractor, what is the minimum confidence threshold for a given data point extracted by each extractor, what is the taxonomy mapping, at field level, between the project taxonomy and the extractor's internal taxonomy (if any), and how to implement "fall-back" rules for data extraction2.
References: 2: Data Extraction Overview 3: Data Extraction 1: Document Processing with Improved Data Extraction

NEW QUESTION # 196
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