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2026 New UiPath-AAAv1 Exam Preparation - Realistic Reliable UiPath Certified Professional Agentic Automation Associate (UiAAA) Braindumps Book Pass Guaranteed QuizFrom your first contact with our UiPath-AAAv1 practice guide, you can enjoy our excellent service. Before you purchase UiPath-AAAv1 exam questions, you can consult our online customer service. Even if you choose to use our trial version of our UiPath-AAAv1 Study Materials first, we will not give you any differential treatment. As long as you have questions on the UiPath-AAAv1 learning guide, we will give you the professional suggestions. UiPath Certified Professional Agentic Automation Associate (UiAAA) Sample Questions (Q51-Q56):NEW QUESTION # 51
In which scenario is a deterministic evaluation more appropriate than a model-graded one?
A. When open-ended reasoning needs to be scored.
B. When the correct output is known and fixed.
C. When evaluating the tone and helpfulness of agent responses.
D. When the response quality depends on user satisfaction.
Answer: B
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 # 52
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. Clear value
B. Prompt
C. Argument
D. Static value
Answer: C
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 # 53
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 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.
B. The LLM does not rely on tokenization for understanding prompts; instead, misinterpretation arises from inadequate pre-programmed definitions of technical terms.
C. 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.
D. The LLM's interpretation is solely based on the frequency of terms within the training dataset, rendering technical nuances irrelevant during generation.
Answer: A
Explanation:
Cis correct - LLMs like those used in UiPath's Agentic Automation rely heavily ontokenization, which breaks input text into subword units (tokens). When complex technical terms (e.g., "UiPath.Orchestrator.
API") aresplit across multiple tokens, the model may not interpret themconsistently or accurately, especially if:
* They're rare or domain-specific
* Appear in different token contexts
* Are inconsistently represented in training data
This is a common challenge in fine-tuning LLMs fortechnical documentation, where small changes in tokenization can shift meaning or relevance weighting. It's why UiPath emphasizesprompt engineeringand context groundingto mitigate misinterpretation.
A is incorrect because thetoken limitaffects response length, not term understanding.
B is misleading - frequency matters, butsemantic relationshipsalso influence interpretation.
D is factually wrong - LLMs absolutely rely on tokenization and arenot rule-basedwith pre-programmed definitions.
Understanding how tokenization impacts prompt fidelity is critical when building agents that use LLMs to generatestep-by-step or technical outputs.
NEW QUESTION # 54
An agent is built to extract customer feedback sentiment. You want to show the LLM how to classify it as
'Positive', 'Neutral', or 'Negative'. Which few-shot design is most helpful?
A. "Text" Use a multiple-choice table with numerical ratings from 1-5.
B. Input: "I love the new design, very intuitive!" Output: "Positive"
Input: "Nothing special, just works." Output: "Neutral"
Input: "Terrible experience, won't use again." Output: "Negative"
C. Input: "The app is okay I guess." # Output:
D. Options: List words like: "great, okay, bad" and map them to tone.
Answer: B
Explanation:
Dis correct - this example follows thegold standard for few-shot prompting, as defined in UiPath's Prompt Engineering methodology. The format usesclearly labeled input-output pairs, giving the agent:
* Consistent structure to follow
* Explicit tone classification
* Variety across sentiment categories
Each example models the task exactly as it should be performed:
* Input: [Text]
* Output: [Label] (Positive, Neutral, Negative)
This design teaches the agenthow to recognize patterns in user tone, even with subtle expressions. It works especially well in LLM-powered agents that handlefeedback analysis,review classification, orcustomer support automation.
Option A (listing keywords) lacks structure and will not generalize well.
B is incomplete - there's no output for the model to learn from.
C uses a rating scale, which doesn't match the classification labels needed.
UiPath emphasizes thatwell-structured few-shot examplesimprove LLM accuracy dramatically - especially when working with ambiguous or emotionally nuanced language.
This approach improvessentiment classification precision, reduces hallucination, and ensures consistent labeling across varied input phrasing - making the agent more reliable in real-world scenarios.
NEW QUESTION # 55
When exploring agentic automation discovery, which dimension ensures the solution aligns with the responsibilities and challenges of the individuals involved?
A. Defining the role or persona by considering the people performing the tasks and their needs, challenges, and responsibilities.
B. Assessing structured and unstructured knowledge contexts required for the tasks but excluding the personas performing these operations.
C. Focusing solely on task dependencies while neglecting the daily pain points of individuals executing these tasks.
D. Mapping systems, applications, and tools without understanding how they interact with human roles.
Answer: A
Explanation:
Cis the correct answer - apersona-centered approachis a cornerstone of UiPath'sAgentic Discovery and Blueprint Designmethodology.
When identifying automation opportunities, UiPath stresses:
* Understanding the actual people behind the process
* Mapping theirpain points,repetitive tasks,decision fatigue, andworkflow bottlenecks
* Designing agents thatserve that roleand embed naturally into their day-to-day responsibilities This ensures agents are:
* Valuable(they solve the right problems)
* Adoptable(they fit into how people actually work)
* Sustainable(they evolve with user needs)
Options A, B, and D areanti-patterns- each represents a discovery flaw where automation is misaligned due toignoring human context.
Persona definition is essential for designing agents thatact as reliable digital coworkers, not just process bots.
NEW QUESTION # 56
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