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UiPath Certified Professional Agentic Automation Associate (UiAAA) Sample Questions (Q57-Q62):NEW QUESTION # 57
When you want a connector field value to be inferred dynamically at run time, which input method should you select in the activity tool?
- A. Static value
- B. Argument
- C. Prompt
- D. Clear value
Answer: B
Explanation:
The correct answer isD- selecting"Argument"allows a field value in an activity (such as a connector or tool call) to bedynamically inferred at runtime, based on variables, agent state, or previous node outputs.
UiPath Autopilot™ and Studio Web use the"Argument"option inactivity configurationto passdynamic values, especially in agentic workflows where:
* Outputs of one step must inform inputs of the next
* Contextual reasoning or prompt outputs need to feed tool parameters
* Escalation decisions or classifications affect API calls or record updates This is fundamental in making agent behavioradaptive and responsive to user context- a key trait of UiPath's agentic orchestration layer.
Other options:
* A (Static value) is hardcoded
* B (Clear value) wipes any existing input
* C (Prompt) is used when engaging the LLM, not connectors
NEW QUESTION # 58
How does adjusting the "Number of results" setting affect the agent's use of context from indexes?
- A. 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.
- B. 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.
- C. It modifies the similarity threshold for chunk retrieval and lowers the number of tokens used.
- D. It changes the number of chunks returned, impacting both the size of the grounding payload and the filtering of relevant information.
Answer: D
Explanation:
The correct answer isC. In UiPath'sContext Groundingconfiguration, the"Number of results"setting directly affects how manychunks of indexed knowledgeare retrieved and passed to the LLM at runtime.
These chunks come from preprocessed documents and are used to build thegrounding payload- the content added to the agent's prompt for context-aware generation.
By increasing the number of results:
* The LLM has access tomore context, which can improve response quality if the added information is relevant.
* However, it alsoincreases the token load, which can reduce prompt space or introduce irrelevant noise if poorly tuned.
Reducing the number of results leads tomore focused prompts, with only top-ranked relevant chunks (based oncosine similarity) included. This is crucial when using large indexes or when LLM context windows are limited.
Option A confuses this setting with similarity threshold tuning, which is a separate parameter.
Option B is false - the agent doesnot ignore contextunless context grounding is disabled.
Option D misrepresents the function - Orchestrator folder selection is unrelated to this retrieval setting.
In summary, the "Number of results" setting allows fine-tuning ofhow much supporting context is retrieved and passed to the model. It is a key control in optimizing performance, precision, and relevance of grounded agent responses.
NEW QUESTION # 59
In which scenario is a deterministic evaluation more appropriate than a model-graded one?
- A. When evaluating the tone and helpfulness of agent responses.
- B. When the response quality depends on user satisfaction.
- C. When the correct output is known and fixed.
- D. When open-ended reasoning needs to be scored.
Answer: C
Explanation:
Cis correct -deterministic evaluationsare best suited for cases where thecorrect output is known and fixed
, allowing for binary or rule-based validation.
Examples include:
* Exact matches (e.g., status: "Approved")
* Regex pattern checks
* Structured JSON outputs
* Correct field extraction (e.g., invoice number = INV-2023-0021)
UiPath supportsdeterministic evaluationusing logic like:
* "Output equals Expected"
* "Contains X and Y"
* "JSON schema is valid"
This is distinct frommodel-graded evaluations, which are used when outputs areopen-endedorqualitative(e.
g., summarization, sentiment, tone). These require LLM-based grading to assess whether the output is "good enough" even if it varies slightly.
Option A and B refer tosubjective assessmentsbetter suited formodel-graded scoring.
D implies feedback-driven quality, again requiringflexible interpretation, not deterministic checking.
Deterministic methods offerspeed, clarity, and automationin validation - ideal for tasks where there'sonly one right answer.
NEW QUESTION # 60
A team is designing an agent to convert plain text meeting notes into a formatted agenda (e.g., structured bullet points). Despite providing a few example transformations in the prompt, the agent generates agendas in inconsistent formats. What critical step was likely overlooked?
- A. Adding clear instructions detailing the output format.
- B. Including constraints to limit the length of the agenda for simplicity.
- C. Adding randomized formatting examples to test the agent's creativity.
- D. Providing only examples without additional context about the task.
Answer: A
Explanation:
This is a repeat of Question 16, and the correct answer remains A.
Even when few-shot examples are included, omitting clear formatting instructions leads to inconsistent outputs, which can break downstream processes in agentic automation.
UiPath's Prompt Engineering guidance emphasizes that instruction clarity is as important as examples - especially when output format matters (like agendas, classifications, or structured text).
An optimal prompt includes:
A task description (e.g., "Convert meeting notes into a 3-section agenda") Clear format instructions (e.g., use bullet points, bold headers) Few-shot examples Optional constraints like length or tone Without that first element - clear instructions - the LLM has to guess the output format, leading to variance and unreliability.
NEW QUESTION # 61
Which of the following is an essential aspect of crafting a comprehensive agent story during the validation stage?
- A. Generalizing automation opportunities across all processes and roles without tailoring solutions based on specific personas or organizational contexts.
- B. Brainstorming automation use cases without validating personas or critically evaluating existing processes, focusing purely on agent capabilities.
- C. Starting immediately with agent behavior prototyping using tools like the Agents designer canvas in Studio Web without assessing mapped automations or impacted systems.
- D. Understanding the daily pain points and inefficiencies of the selected role to identify tasks that consume unnecessary time and potential gains from agent intervention.
Answer: D
Explanation:
The correct answer isB- UiPath'sAgentic Blueprint Design processemphasizes the importance of grounding automation opportunities inreal user context and operational pain points.
During thevalidation stage, developers and stakeholders assess:
* Specific persona rolesand responsibilities
* Current pain pointsandtime-consuming tasks
* Impact potentialof agent assistance
This ensures the agent story reflectsvalue-driven automation, not just technical ambition. It also validates that the agent solves areal bottleneck- such as handling repetitive approvals, prioritizing requests, or managing context-based escalations.
UiPath warns against the pitfalls outlined in A, C, and D:
* A and D overlookpersona-centricity, which is essential.
* C skips the criticaldiscovery and mappingphase that should come before prototyping.
By focusing on B, teams ensure their agent ispurpose-builtfor the right users, delivering measurable outcomes aligned to business needs.
NEW QUESTION # 62
......
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