最新的UiPath Certified Professional Specialized AI Professional v1.0 - UiPath-SAIv1免費考試真題

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

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

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

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).

A. GIT, TFS

B. GIT, SVN, TFS

C. GIT, TFS, Azure DevOps

D. You cannot connect to multiple plugins at the same time.

A. Contains

B. Possible values

C. Starts with

D. Ends with

A. If it has never been used in a pipeline.

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

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

D. If it is not associated with any ML skills.

A. Sums up all the line amounts and converts the fields to CDec.

B. Sums up the subtotal to the total variable by converting it to CDec

C. Sums up subtotal fields from the config file converted to CDec with the subtotal.

D. Sums up all the line amounts converted to CDec and the subtotal

A. Dashboard. Datasets. ML Packages. ML Training. ML Evaluation, and ML Logs.

B. Datasets. Data Labeling. ML Packages. Pipelines, and ML Skills.

C. Dashboard. Datasets, Data Labeling. ML Packages. Pipelines, ML Skills, and ML Logs.

D. Datasets, Data Labeling. ML Packages, ML Training, ML Evaluation, ML Skills, and ML Logs.

A. Pre-trained, fine-tunable, and reviewed.

B. Pre-trained, custom training, and fine-tunable.

C. Custom training, fine-tunable, and reviewed.

D. Pre-trained, custom training, and reviewed.

A. It stores the API keys and authentication credentials for accessing external services.

B. It contains the configuration settings for the UiPath Robot and Orchestrator integration.

C. It defines the input document types and formats supported by the template.

D. It specifies the output file path and format for the processed documents.

A. Using Load Taxonomy activity.

B. Using DeserializeTaxonomy activity.

C. Using Read Taxonomy activity.

D. Using DocumentTaxonomy.Deserialize method inside an Assign activity.