UiPath-SAIv1 無料問題集「UiPath Certified Professional Specialized AI Professional v1.0」

（A）To formulate a comprehensive training set that closely reflects the entirety of the dataset.

（B）To provide the platform with a collection of keywords associated with each label for predicting other instances.

（C）To allocate 20 training examples for each label.

（D）To apply labels to a maximum amount of data to enhance model performance.

（A）Robotic Process Automation

（B）Natural Language Processing (NLP)

（C）Cloud Computing.

（D）Virtual Reality.

（A）Automatically updating all packages in a project to their latest versions.

（B）Managing package dependencies exclusively for attended automation projects.

（C）Disabling previously installed packages without uninstalling them.

（D）Installing, uninstalling, and updating activity packages in a project.

（A）Entity descriptions.

（B）Label predictions.

（C）Label descriptions.

（D）Entity predictions.

（A）eval.use_cuda

（B）eval.enable_ocr

（C）eval.enable_qpu

（D）eval.redo_ocr

（A）The package name cannot include any special characters such as "@", "S", or "#".

（B）The package name cannot exceed 10 characters in length.

（C）The package name cannot use any Python reserved keywords.

（D）The package name cannot contain any spaces or hyphens.

（A）Enables the deployment, management, and improvement of ML models locally, and requires manual download of all the resources and then loading them into the node.

（B）Enables the deployment of the full UiPath Automation Platform in the environment of choice and contains everything in one package that can be deployed in multi-node mode with automatic scaling and built-in HA. monitor, configure, and upgrade.

（C）Enables the deployment, management, and improvement of ML models on UiPath Automation Cloud, and requires no infrastructure and no maintenance.

（D）Enables the deployment, management, and improvement of ML models locally with easy installation due to the automatic retrieval of the installer and associated artifacts from the internet.

（A）Classification.

（B）Digitization.

（C）Validation.

（D）Extraction.

（A）Name, description, and permissions.

（B）Name, description, and labels.

（C）Name and labels.

（D）Name and description.

（A）For structured or semi-structured documents where for certain fields, data is always found in a strict, predictable format and context.

（B）For structured or semi-structured documents in which layouts of different document providers vary greatly.

（C）For structured or semi-structured documents where the extraction method is based on a set of FlexiCapture definition files.

（D）For use cases in which non-variable format documents need to be processed.

（A）Create External Task activity

（B）Create Document Labeling Action activity

（C）Create Labeling Action activity

（D）Create Labeling Task activity

（A）Structured data points.

（B）Concepts, themes, and intents.

（C）Thread properties.

（D）Metadata properties.

（A）Data labeling name, language, and number of documents.

（B）Data labeling session name and dataset.

（C）Data labeling name, dataset. and Al Center project.

（D）Dataset and Al Center project.

（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 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.

（C）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

（D）The trained-from-scratch general fields depend on the model version, while the ones that are pre-trained do not.

（A）List

（B）Array

（C）Tuple

（D）Integer

（A）Improves the extractor accuracy by learning from the classification result.

（B）Allows the model to learn from human-validated data.

（C）Allows the extractor to improve its prediction over time by using better OCR (Optical Character Recognition) engines.

（D）Allows a human to validate and correct the extracted data.