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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q102-Q107):

NEW QUESTION # 102
If Label X in UiPath Communications Mining has 80% precision at a given confidence threshold, what output should this provide?

A. For every 100 messages, 80 would be labelled correctly as 'Label X', and 20 would be labelled incorrectly.
B. For every 100 messages, 20 would be labelled correctly as 'Label X', and 80 would be labelled incorrectly.
C. For every 100 messages which should have been labelled as 'Label X', 80 were labelled and 20 were missed.
D. For every 100 messages which should have been labelled as 'Label X 20 were labelled and 80 were missed.

Answer: A

Explanation:
If Label X has 80% precision at a given confidence threshold, this means that out of every 100 messages predicted to have Label X, 80 are correctly labelled, and 20 are incorrectly assigned the label. Precision measures the correctness of predictions made, so in this case, 80% of the predictions were correct, while 20% were false positives

NEW QUESTION # 103
How do the prediction mechanisms for labels and general fields differ in the UiPath Communications Mining platform?

A. Labels predictions are based on assigned span of text and entities are predicted solely from metadata properties.
B. Label predictions are made based on the text of the message as well as some metadata properties, while general fields are learnt from the assigned span of text and the context of the text surrounding that span.
C. Labels and general fields are both predicted based on the text of the message, with no consideration given to metadata properties or contextual surrounding text.
D. Label predictions rely solely on metadata properties, general fields are learnt from the presence of certain key phrases in the text.

Answer: B

NEW QUESTION # 104
What function in the train.py file is responsible for persisting the trained model?

A. evaluate(self, evaluation_directory)
B. __init_(self)
C. train(setf. trainmg_directory}
D. save(self)

Answer: D

Explanation:
In the context of a machine learning pipeline, specifically within the train.py file in UiPath AI models, the function responsible for persisting the trained model is typically named save(self). This function is invoked after the model has been trained, and it ensures that the model's state, weights, and configurations are saved to disk or another persistent storage medium. The saved model can then be reused for inference or further fine- tuning in future executions.
For more details, refer to:
* UiPath AI Center Documentation: Training Models
* Model Persistence: Saving Machine Learning Models in AI Center

NEW QUESTION # 105
How do you use the Generative Classifier within UiPath Document Understanding Cloud APIs to classify a document as either an "Invoice" or a "Receipt"?

A. 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.
B. 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.
C. 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.
D. 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.

Answer: C

Explanation:
In UiPath Document Understanding Cloud APIs, the Generative Classifier is used to classify documents by leveraging a generative AI model. To classify a document as either an "Invoice" or a "Receipt," a POST request must be sent to the /api/framework/projects/{projectId}/classifiers/generative_classifier/classification endpoint. The body of this request should contain prompts specifying the classification types (in this case,
"Invoice" and "Receipt") along with their corresponding descriptions. This allows the model to correctly classify incoming documents based on these predefined prompts.
For further reading, refer to:
* UiPath Document Understanding API Documentation: Cloud API for Classification
* Generative Classifier APIs: UiPath Classification APIs

NEW QUESTION # 106
What are the languages supported by the generic Document Understanding ML Package?

A. Languages using the Latin alphabet, the Cyrillic alphabet, the Greek left-to-right alphabet. Japanese, and Chinese.
B. Languages using the Cyrillic alphabet, the Greek left-to-right alphabet, and Chinese.
C. Languages using the Greek left-to-right alphabet. Japanese, and Chinese.
D. Languages using the Latin alphabet (like Italian, French. Portuguese. Spanish, and Romanian), and the Greek left-to-right alphabet.

Answer: A

NEW QUESTION # 107
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