최신UiPath Certified Professional Specialized AI Professional v1.0 - UiPath-SAIv1무료샘플문제

A. __init_(self)

B. evaluate(self, evaluation_directory)

C. save(self)

D. train(setf. trainmg_directory}

A. RPA Developer.
D Process Controller

B. Data Scientist.

C. Administrator.

A. Tools for message analytics and monitoring.

B. Tools for evaluating label and entity performance.

C. Tools for evaluating model performance.

D. Tools for comparing different model versions.

A. Enhancing the logging capabilities to provide more detailed insights into each transaction.

B. Implementing a sophisticated automatic retry mechanism to handle failed transactions without manual intervention.

C. Recording and storing failed queue transactions for debugging purposes.

D. Introducing real-time error alerts to notify users immediately when a queue transaction fails.

A. By sending a POST request to yapi/framework/projectsy{projectld}/ctassifiers/generative_classifier
/classification with a body containing prompts that specify the 'Invoice' and 'Receipt' names and descriptions.

B. By sending a POST request to /api/framework/projects/{projectld}/classifiers/generative_classifier
/dassification with a body containing prompts that specify the 'Invoice' and 'Receipt' names and a description for their fields.

C. By sending a PUT request to /api/framework/proiects/{projectld}/classifiers/generative_classifier
/classification without any specific classification details in the body of the request.

D. By sending a GET request to /api/framework/projects/{projectld}/classifiers/generative_classifier
/classification with a body containing prompts that specify the 'Invoice' and 'Receipt' names and descriptions.

A. If it is not linked to any data labeling session.

B. There are no restrictions in editing a dataset.

C. If it is not being used in any active pipeline.

D. If it has not been modified within the last 24 hours.

A. Process / Request types.

B. Customer experiences.

C. Root causes / Exceptions.

D. Products / Systems.

A. Exact match and Naive string search.

B. Exact match and Levenshtein.

C. Exact match and Phonetic matching.

D. Exact match and Finite state automation-based search.

A. Only using pre-trained extraction models.

B. Manual classification of document types.

C. Generative capabilities for classifying various document types.

D. Real-time document translation.

A. Process unstructured communication data using NLP.

B. Design user interfaces.

C. Generate code for automation processes.

D. Analyze financial data.

A. Variables can only be used within the main workflow, while arguments can only be used within invoked workflows.

B. Variables are global and arguments are local.

C. Variables have a limited scope within a specific workflow or sequence, while arguments are used to pass data between workflows.

D. Variables are used for input data, while arguments are used for output data.

A. You have a large dataset that needs long-term processing.

B. You need real-time interaction and you are only processing documents with maximum five pages.

C. You need to process documents larger than five pages.

D. You need to handle multiple operations simultaneously, allowing for concurrent processing and avoiding idle time.

A. To organize knowledge by using a controlled vocabulary to make it easier to find related information.

B. To apply relationship schemas other than parent-child hierarchies, such as network structures on the processed data.

C. To apply rigor in the taxonomy of data, ensuring any newly discovered object fits into one and only one category or object.

D. To define the document types and the pieces of information targeted for data extraction (fields) for each document type.

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 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 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).