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RPA Process Design and Best Practices Flashcards

6 cards from real RPA practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

Read the first 6 RPA Process Design and Best Practices flashcards as text
  1. What is the purpose of an 'automation pipeline' in an RPA program?

    Answer: A structured process for identifying, evaluating, developing, and deploying automation opportunities

    An automation pipeline is the end-to-end governance process — from discovery and assessment through development, testing, and production deployment.

  2. Which best describes 'Intelligent Process Automation (IPA)'?

    Answer: Combining RPA with AI capabilities like NLP and ML to handle unstructured data and decisions

    IPA extends RPA by integrating AI technologies like NLP, OCR, and machine learning to handle unstructured inputs and make judgment-based decisions.

  3. What is 'bot monitoring' best practice primarily focused on?

    Answer: Detecting and alerting on bot failures, exceptions, and performance deviations in real time

    Effective bot monitoring uses dashboards and alerts to detect failures, SLA breaches, and performance issues as they happen, enabling rapid response.

  4. What is a 'bot maintenance' plan important for in RPA operations?

    Answer: Ensuring bots are updated when target applications or business processes change

    Bot maintenance plans account for the fact that applications change (UI updates, new versions), requiring bots to be updated to remain functional.

  5. What does 'total cost of ownership (TCO)' include when evaluating an RPA program?

    Answer: All costs including licensing, infrastructure, development, testing, maintenance, and support

    TCO encompasses every cost over the automation lifecycle — licenses, infrastructure, development, ongoing maintenance, and operational support.

  6. What is the difference between 'unit testing' and 'integration testing' in RPA?

    Answer: Unit testing validates individual bot components while integration testing validates end-to-end system interactions

    Unit testing checks individual workflow components in isolation, while integration testing validates that the bot correctly interacts with all connected systems end-to-end.

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