100% PASS QUIZ UNPARALLELED UIPATH - FREE UIPATH-SAIV1 SAMPLE

100% Pass Quiz Unparalleled UiPath - Free UiPath-SAIv1 Sample

100% Pass Quiz Unparalleled UiPath - Free UiPath-SAIv1 Sample

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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q178-Q183):

NEW QUESTION # 178
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. ML Packages. ML Training. ML Evaluation, and ML Logs.
  • 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.

Answer: C

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 # 179
Which UiPath Communications Mining model performance factor relates to the proportion of messages in the dataset that have informative label predictions?

  • A. Underperforming labels.
  • B. Average label performance.
  • C. Balance.
  • D. Coverage.

Answer: D

Explanation:
In UiPath Communications Mining, the term Coverage refers to the proportion of messages in the dataset that have informative label predictions. This is an important metric that indicates how much of the dataset the model is able to classify with meaningful and relevant labels. High coverage means that the model is effectively assigning labels to a large portion of the data, which is crucial for ensuring the model's usefulness in automating communication mining tasks.
For more details, refer to:
* UiPath Communications Mining Performance Factors: Model Coverage and Accuracy
* Communications Mining: Coverage and Other Metrics


NEW QUESTION # 180
How is the Taxonomy component used in the Document Understanding Template?

  • 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 define the document types and the pieces of information targeted for data extraction (fields) for each document type.
  • C. To organize knowledge by using a controlled vocabulary to make it easier to find related information.
  • D. To apply relationship schemas other than parent-child hierarchies, such as network structures on the processed data.

Answer: B

Explanation:
According to the UiPath documentation, the Taxonomy component is used in the Document Understanding Template to define the document types and the fields that are targeted for data extraction for each document type. The Taxonomy component is the metadata that the Document Understanding framework considers in each of its steps, such as document classification and data extraction. The Taxonomy component allows you to create, edit, import, or export the taxonomy of your project, which is a collection of document types and fields that suit your specific objectives. The Taxonomy component also allows you to configure the field types, details, and validations, as well as the supported languages and categories for your documents.
References:
* Document Understanding - Taxonomy
* Document Understanding - Taxonomy Overview
* Document Understanding - Create and Configure Fields


NEW QUESTION # 181
Which environment variable is relevant for Evaluation pipelines?

  • A. eval.redo_ocr
  • B. eval.enable_ocr
  • C. eval.use_cuda
  • D. eval.enable_qpu

Answer: A

Explanation:
The environment variable eval.redo_ocr is relevant for Evaluation pipelines because it allows you to rerun OCR when running the pipeline to assess the impact of OCR on extraction accuracy. This assumes an OCR engine was configured when the ML Package was created. The other options are not valid environment variables for Evaluation pipelines.
References: Document Understanding - Evaluation Pipelines


NEW QUESTION # 182
How has UiPath improved the experience for handling failed queue transactions in the 2023.10 release?

  • A. Recording and storing failed queue transactions for debugging purposes.
  • B. Introducing real-time error alerts to notify users immediately when a queue transaction fails.
  • C. Enhancing the logging capabilities to provide more detailed insights into each transaction.
  • D. Implementing a sophisticated automatic retry mechanism to handle failed transactions without manual intervention.

Answer: A

Explanation:
In the UiPath 2023.10 release, a key improvement introduced was the ability to record and store failed queue transactions for debugging purposes. This feature helps users by capturing the failure details and making them accessible directly in Orchestrator. The recording of failed transactions is enabled at the process level, within the Video section of the Job recording settings. It is intended to facilitate easier troubleshooting of failed automations by providing insights into the moments leading to the failure, which can be reviewed later for debugging


NEW QUESTION # 183
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