Score Higher on Verified UiPath-AAAv1 | UiPath Certified Professional Agentic Automation Associate (UiAAA) Exam Questions with Answers

Dis correct — includingwell-designed examplesin prompts is a key part offew-shot learning, which helps LLM-based agents better understand thetask structure, output style, and expected behavior.

UiPath encourages the use of examples for:

Classification(e.g., labeling sentiment, email categories)

Transformation tasks(e.g., turning unstructured text into tables)

Step-by-step instructions(e.g., troubleshooting flows)

These examples serve two purposes:

Pattern induction: The model picks up on consistent structures or rules used across examples.

Generalization: With diverse examples, the agent can apply logic to unseen but similar cases.

Best practice:

Usetypical, real-world examplesrepresentative of the data the agent will encounter.

Keep formatsclear and consistentacross input-output pairs.

Pair examples withexplicit instructionsin the system or user prompt.

Option A is flawed — focusing only on edge cases can confuse the model.

B is false — omitting examples forces the LLM to guess the structure, reducing accuracy.

C is misleading — examples improve performance butdo not guarantee perfect output; testing and evaluation are still required.

In short,prompt engineering with examples is essentialto buildingreliable, generalizable, and scalableAI agents.

Cis correct — an agentic story that falls into the"Do Later"quadrant typically representshigh feasibility but low impact.

In UiPath’sImpact vs. Feasibility Matrix, used during theAgentic Discoveryphase, automation ideas are evaluated on:

Feasibility(ease of implementation)

Impact(business value, time saved, ROI)

Quadrants:

Quick Wins: High impact, high feasibility

Do Later: Low impact, high feasibility

Strategic Bets: High impact, low feasibility

Avoid/Backlog: Low on both

‘Do Later’ agentic stories are often simple to automate but don’t deliver meaningful outcomes — e.g., automating low-volume tasks or internal reports with limited audience.

Focusing onimpactful use casesensures agent development time translates to real business value — one of the key lessons from UiPath’s agentic blueprint methodology.

The correct answer isC— the most critical aspect of designing a few-shot prompt in UiPath'sLLM-driven agent frameworkis selecting examples that arediverse,representative, andrelevantto the actual data the agent will encounter in production.

In afew-shot structured prompt, examples are used to demonstrate a pattern the model should follow. UiPath recommends:

Usingrealistic examplesfrom actual user inputs or support tickets

Coveringedge casesor variations in phrasing and tone

Matching thedesired output structureexactly (e.g., Input: ..., Output: ...)

These patterns help the LLMinfer the task correctlyandmaintain consistency, especially when processing unstructured inputs like email subjects.

Option A is incorrect — introducing incorrect labels degrades performance and adds confusion.

B is wrong — the number of examples depends on thetask complexity and token budget. Sometimes 3–5 is ideal.

D undermines task alignment — random examples reduce accuracy and coherence.

UiPath'sPrompt Engineering best practicesprioritizegrounded, contextually rich inputs, particularly when automating classification tasks like spam detection, triage, or intent recognition. High-quality, task-aligned examples lead tomore reliable, human-like agents.