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UiPath Certified Professional Agentic Automation Associate (UiAAA) Sample Questions:

1. What type of agents can be invoked using the 'Start and wait for external agent' feature in UiPath Maestro?

A) Only UiPath Orchestrator robots.
B) Agents that do not require any input or output variables.
C) External agents like Salesforce or ServiceNow.
D) Agents configured exclusively within the same project.

2. An agent uses Web Search, Slack integration, and a custom process to resolve IT support tickets. The agent must:
* Retrieve relevant troubleshooting steps from the web.
* Notify the user via Slack if a solution is found.
* Escalate unresolved tickets via a custom process.
Which evaluation strategy ensures comprehensive coverage while avoiding redundancy?

A) Group evaluations into sets: Valid web results triggering Slack notifications, Invalid web results triggering escalations, Edge cases.
B) Use random input sampling across all tools and rely on the default "LLM-as-a-Judge" assertion.
C) Create more than 30 evaluations for Slack notifications, more than 30 for web searches, and more than
30 for escalation processes.
D) Create 30 evaluations for Slack notifications, 30 for web searches, and 30 for escalation processes.

3. What is the main purpose of using a context grounding strategy with an ECS Index in Agents designer canvas in Studio Web?

A) To define static rules for retrieving data from the index.
B) To keep the ECS Index stored in a shared Orchestrator folder.
C) To retrieve data based on the user's current session or inputs.
D) To limit the number of results retrieved from the ECS Index.

4. Why is mapping processes a critical step in identifying opportunities for agentic automation?

A) It examines broader workflows without focusing on individual steps, missing granular opportunities for automation.
B) It assumes mapping processes is sufficient to complete automation implementation without considering task dependencies or broader workflows.
C) It prioritizes identifying potential ROI metrics before establishing specific process mapping, potentially overlooking optimization areas.
D) It allows pinpointing specific steps or sub-tasks within a workflow that could be automated, improving efficiency and reducing errors.

5. A developer is working on fine-tuning an LLM for generating step-by-step automation guides. After providing a detailed example prompt, they notice inconsistencies in the way the LLM interprets certain technical terms. What could be the reason for this behavior?

A) The LLM's interpretation is solely based on the frequency of terms within the training dataset, rendering technical nuances irrelevant during generation.
B) The LLM's tokenization process may have split complex technical terms into multiple tokens, causing slight variations in how the model interprets and weights their relationships within the context of the prompt.
C) The LLM does not rely on tokenization for understanding prompts; instead, misinterpretation arises from inadequate pre-programmed definitions of technical terms.
D) The inconsistency is related to the token limit defined for the prompt's length, which affects the LLM's ability to complete a response rather than its understanding of technical terms.

Solutions: