Pass Your Next UiPath-AAAv1 Certification Exam Easily & Hassle Free [Q11-Q25]

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Pass Your Next UiPath-AAAv1 Certification Exam Easily & Hassle Free

Free UiPath UiPath-AAAv1 Exam Question Practice Exams

Q11. When exploring agentic automation discovery, which dimension ensures the solution aligns with the responsibilities and challenges of the individuals involved?

Focusing solely on task dependencies while neglecting the daily pain points of individuals executing these tasks.

Assessing structured and unstructured knowledge contexts required for the tasks but excluding the personas performing these operations.

Defining the role or persona by considering the people performing the tasks and their needs, challenges, and responsibilities.

Mapping systems, applications, and tools without understanding how they interact with human roles.

Q12. What are the characteristics of an agentic story within the ‘Do later’ quadrant in the impact and feasibility matrix?

High feasibility and High Impact

Low feasibility and High Impact

High feasibility and Low Impact

Low feasibility and Low Impact

Q13. When creating an Action app, what is the purpose of defining the “Approve” and “Deny” outcomes within the Action schema?

To ensure the app validates search results and prevents faulty submissions.

To guide the agent’s next steps based on the review results of Input/Output properties.

To save user input as mandatory action schema properties during automation execution.

To dynamically update user-facing form labels with the action result.

Q14. A business is looking to automate its workflows and has both structured, repetitive tasks (like data entry) and unstructured, exception-heavy processes (such as responding to diverse customer queries). How should they combine agents and robots (RPA) to achieve optimal automation results?

Use robots (RPA) for the structured, repetitive tasks, leveraging their rule-based approach for reliability and precision, while agents handle the unstructured processes by using their adaptive decision-making capabilities.

Use agents exclusively, as they can cover both structured workflows and dynamic environments due to their probabilistic and adaptive nature.

Use robots (RPA) exclusively, as they are capable of adapting to dynamic workflows with exception handling and learning capabilities.

Use agents for the structured, repetitive tasks, as they can follow deterministic rules efficiently while robots (RPA) handle unstructured workflows requiring adaptability, decision-making capabilities and contextual awareness.

Q15. Why would you choose the Argument input method for an activity field?

Applies one constant value you enter during design every time the agent executes the activity.

Receives a runtime value from an agent input argument defined earlier in the workflow.

Lets the agent infer the field value at runtime using the Description and its reasoning.

Prompts a person to supply the value each time the field is evaluated at runtime.

Q16. What is the primary role of guardrails in tools?

Guardrails are used exclusively to automate all tool corrections without the possibility of triggering human intervention.

Guardrails control unexpected behaviors within tool calls deterministically, allowing developers to configure conditions for human intervention and escalations.

Guardrails are designed to apply only after tool execution, without influencing pre-execution conditions.

Guardrails only validate tool inputs during development and do not address unpredictable behaviors at runtime.

Q17. Which configuration area defines what the agent should do after a human resolves the escalation?

Assignment recipient list

Agent Memory toggle

Inputs description fields

Outcome behavior section

Q18. When you want a connector field value to be inferred dynamically at run time, which input method should you select in the activity tool?

Static value

Clear value

Prompt

Argument

Q19. An agent is being designed to generate step-by-step troubleshooting guides for software issues. Testing shows that the guides lack clarity and include redundant steps, confusing users. What is the best refinement for the prompt?

Add generic examples to allow the agent to experiment with the step format.

Enable the agent to generate longer troubleshooting guides for completeness.

Provide clear instructions to make steps actionable, concise, and free of redundancies.

Avoid explaining each step in detail to simplify the prompt.

Q20. Which similarity search function is leveraged when Context Grounding is used by UiPath Products like Agents?

ReLu similarity search

Cosine similarity search

Softmax similarity search

Sigmoid similarity search

Q21. A company launches a marketing campaign powered by generative AI and agentic AI technologies. What best describes how their roles differ?

Generative AI produces outputs like text or images, while agentic AI focuses on intelligent automation, decision-making, and dynamic task execution.

Generative AI focuses on improving customer sentiment analysis, while agentic AI refines campaign content.

Both generative AI and agentic AI collaborate to create promotional materials.

Agentic AI designs campaign materials, while generative AI handles customer queries in real-time.

Q22. 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?

Use random input sampling across all tools and rely on the default “LLM-as-a-Judge” assertion.

Create more than 30 evaluations for Slack notifications, more than 30 for web searches, and more than
30 for escalation processes.

Group evaluations into sets: Valid web results triggering Slack notifications, Invalid web results triggering escalations, Edge cases.

Create 30 evaluations for Slack notifications, 30 for web searches, and 30 for escalation processes.

Q23. How does adjusting the “Number of results” setting affect the agent’s use of context from indexes?

It modifies the similarity threshold for chunk retrieval and lowers the number of tokens used.

It makes the agent ignore all context completely, resulting in outputs that are entirely disconnected from the indexed data, regardless of its relevance to the query or prompt provided.

It changes the number of chunks returned, impacting both the size of the grounding payload and the filtering of relevant information.

It selects which Orchestrator folder to use, determining the location of stored workflows and deciding which set of predefined rules will apply during data retrieval and processing.

Q24. What steps must be completed when creating evaluations from scratch for a new evaluation set in UiPath?

Assign evaluators immediately after creating the new evaluation set name, then configure inputs and expected outputs later.

Add a name to the evaluation set, provide input values and expected output, save each evaluation, and assign evaluators before running the evaluation set.

The evaluation set can only be created using imported JSON data from previous evaluations of other agents.

Once the evaluation set is created, all included evaluations are automatically scored based only on input values and expected outputs.

Q25. How long does a key-value pair stored in Agent Memory remain available before it expires by default?

3 months

6 months

12 months

Until the agent version is updated, after which key-value pairs are automatically cleared