UiPath UiPath-AAAv1 Exam Overview:
| Certification Vendor: | UiPath |
| Exam Name: | UiPath Certified Professional Agentic Automation Associate (UiAAA) |
| Exam Number: | UiPath-AAAv1 |
| Available Languages: | English |
| Real Exam Qty: | 60 |
| Related Certifications: | UiPath Certified Professional Agentic Automation Professional (UiPath-AAPv1) |
| Exam Duration: | 90 minutes |
| Exam Format: | Multiple Choice, Multiple Select |
| Exam Price: | $150 USD |
| Certificate Validity Period: | 3 years |
| Passing Score: | 70% |
| Sample Questions: | UiPath UiPath-AAAv1 Sample Questions |
| Exam Way: | Online proctored exam or test center delivery via Pearson VUE. |
| Pre Condition: | Basic understanding of automation concepts, prompt engineering, and exposure to UiPath Studio Web and large language models is recommended. |
| Official Syllabus URL: | https://academy.uipath.com/certifications |
UiPath UiPath-AAAv1 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Agentic Evaluations | - Evaluation and Optimization
|
| Topic 2: Agentic Discovery | - Identifying Automation Opportunities
|
| Topic 3: Agent Blueprint Design | - Designing Intelligent Agents
|
| Topic 4: Prompt Engineering | - Prompt Design Techniques
|
| Topic 5: Agentic AI and Automation Concepts | - Foundations of Agentic Automation
|
| Topic 6: Context Grounding and Escalations | - Enterprise-Ready Agent Design
|
UiPath Certified Professional Agentic Automation Associate (UiAAA) Sample Questions:
1. What steps must be completed when creating evaluations from scratch for a new evaluation set in UiPath?
A) Once the evaluation set is created, all included evaluations are automatically scored based only on input values and expected outputs.
B) Add a name to the evaluation set, provide input values and expected output, save each evaluation, and assign evaluators before running the evaluation set.
C) The evaluation set can only be created using imported JSON data from previous evaluations of other agents.
D) Assign evaluators immediately after creating the new evaluation set name, then configure inputs and expected outputs later.
2. When is it appropriate to rely on Clipboard AI inside Autopilot for Everyone for a copy-and-paste task?
A) When you plan to paste several different tables in succession during the same chat and expect Autopilot for Everyone to queue each paste automatically.
B) Whenever you need to paste any content regardless of operating system, file type, or the number of pastes.
C) When you are using macOS and want Autopilot for Everyone to perform a copy and paste on a Linux VM.
D) When you are working on a Windows machine and need to perform a single AI-powered paste of a table (for example, from a PDF) into another application directly from the chat interface.
3. For what primary reason should you supply a description for every input and output argument in an agent?
A) Clear descriptions help the agent understand how to use each argument effectively while generating or returning results.
B) Descriptions cause Orchestrator triggers to pre-populate the arguments automatically, eliminating manual mapping.
C) Adding descriptions forces Studio Web to treat all arguments as mandatory fields that block deployment if left empty.
D) Argument descriptions are required only for input arguments; output arguments are inherently self- explanatory and do not benefit from them.
4. When creating an Action app, what is the purpose of defining the "Approve" and "Deny" outcomes within the Action schema?
A) To guide the agent's next steps based on the review results of Input/Output properties.
B) To save user input as mandatory action schema properties during automation execution.
C) To ensure the app validates search results and prevents faulty submissions.
D) To dynamically update user-facing form labels with the action result.
5. What is a key feature of zero-shot prompting?
A) It requires at least one example in the prompt for efficient completion.
B) It ensures the model has been fine-tuned for all tasks it encounters.
C) This is necessary for complex or nuanced scenarios.
D) The model performs tasks without prior examples or training specific to the request.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: D | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: D |

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