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NEW QUESTION # 17
What is the difference between training an ML model using a CPU versus using a GPU in UiPath Cloud Platform?
Answer: B
Explanation:
In the context of UiPath Cloud Platform, training a machine learning (ML) model using a GPU (Graphics Processing Unit) is significantly faster than using a CPU (Central Processing Unit). This is because GPUs are designed to handle parallel processing, which is highly beneficial for the matrix and vector computations required in ML model training. GPUs can perform more calculations simultaneously compared to CPUs, leading to faster training times, especially with large and complex datasets1.
The UiPath documentation specifically recommends using GPU training for large and production datasets, as CPU training is much slower and should be used sparingly, for small datasets for demo or testing purposes1. This aligns with the general understanding that for deep learning training, GPUs should be used due to their significant speed advantage over CPUs23.
NEW QUESTION # 18
What would be the most cost effective way to extract an email address from a structured document?
Answer: A
Explanation:
The most cost-effective way to extract an email address from a structured document is by using the RegEx extractor. This method is efficient because regular expressions can be tailored to match the specific pattern of email addresses without the need for more complex and resource-intensive tools. The RegEx extractor is particularly suitable for structured documents where the format is predictable and consistent, making it easy to identify and extract email addresses based on their common patterns1.
References: The use of RegEx for extracting information from structured documents is a common practice in RPA and is supported by discussions in the UiPath Community Forum, where similar methods are recommended for data extraction tasks1.
NEW QUESTION # 19
Which of the following are structured documents that can be processed with Document Understanding?
Answer: B
Explanation:
Structured documents that can be processed with Document Understanding include passports, invoices, purchase orders, and utility bills. These documents have a fixed format or structure, which makes them suitable for processing through Document Understanding technologies that extract and digitize information based on predefined templates and rules.References: UiPath Documentation on Document Understanding at
https://docs.uipath.com/.
NEW QUESTION # 20
What is an example of a feasibility-based prioritization criterion?
Answer: A
Explanation:
A feasibility-based prioritization criterion refers to factors that make a process more suitable for automation.
Having all inputs standardized and digital is an example of such a criterion because it indicates that the process is likely to be more straightforward to automate. Standardized, digital inputs reduce the complexity of the automation task and increase the likelihood of successful implementation. This is in contrast to processes with non-standardized or non-digital inputs, which may require additional steps such as digitization or standardization before they can be automated effectively1.
References: The explanation is based on the UiPath Community Forum discussions and the UiPath Documentation Portal's information on feasibility and complexity in process assessment for RPA implementation1.
NEW QUESTION # 21
Which of the following principles applies during the Process Documentation stage?
Answer: C
Explanation:
During the Process Documentation stage, it is a best practice and often a requirement to have at least one review of the documentation created, such as the Process Design Document (PDD). This ensures that all stakeholders agree on the process as documented and that it accurately represents the business needs and technical requirements.References: UiPath Documentation on Process Documentation athttps://docs.uipath.com/.
NEW QUESTION # 22
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