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[Hardware] 2026 Latest Valid UiPath-AAAv1 Test Syllabus Help You Pass UiPath-AAAv1 Easily

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【Hardware】 2026 Latest Valid UiPath-AAAv1 Test Syllabus Help You Pass UiPath-AAAv1 Easily

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UiPath Certified Professional Agentic Automation Associate (UiAAA) Sample Questions (Q39-Q44):NEW QUESTION # 39
When passing runtime data into an Agent, which approach ensures the input argument is actually available inside the user prompt at execution time?
  • A. Use single braces like {CUSTOMER_EMAIL}, because the platform automatically normalizes the identifier.
  • B. Declare the argument in the system prompt; any text surrounded by angle brackets (e.g.,
    <CUSTOMER_EMAIL>) will be substituted automatically.
  • C. Create the argument in Data Manager and reference it verbatim inside double curly braces, e.g.,
    {{CUSTOMER_EMAIL}}, so the name matches exactly.
  • D. Simply mention the variable name in plain prose-the Agent will infer the value from the workflow without special syntax.
Answer: C
Explanation:
Bis correct - to pass runtime values into an agent's prompt in UiPath, you must:
* Declare the variable inData Manager
* Reference it inside theuser/system promptusingdouble curly braces, e.g., {{CUSTOMER_EMAIL}} This ensures the platform can:
* Substitute values at runtime
* Maintain traceability between arguments and prompts
* Provide context grounding for the LLM
Option A is incorrect - angle brackets are not used for substitution.
C is wrong - single braces {} are not valid for UiPath's binding syntax.
D is unreliable - LLMs do not infer values from prose without structured substitution.
This technique ensures consistentparameter injectionfor context-aware agent behavior.

NEW QUESTION # 40
When adding an index for querying data stored in CSV files, what advanced feature does UiPath Context Grounding provide to optimize retrieval?
  • A. Automatic conversion of CSV data into native XLSX files for enhanced compatibility.
  • B. Support for structured queries tailored specifically for CSV data.
  • C. Embedding data from CSV files into JSON templates for improved semantic similarity.
  • D. Streaming support for real-time ingestion and CSV query execution without indexing.
Answer: C
Explanation:
Dis correct - UiPathContext Groundingsupports querying unstructured and semi-structured data, including CSV files, by embedding their content intosemantic representationssuch asJSON-formatted chunksduring indexing.
Here's how this works for CSVs:
* UiPathparses the tabular dataand maps each row or section into asemantically rich format(e.g., JSON)
* These JSON-structured embeddings are then stored inECS Indexes(Enterprise Context Store)
* When an LLM agent queries the index, it retrieves themost contextually relevant data, even across large datasets This unlocks:
* Smarter question answeringfrom tabular data
* Cross-referencing multiple fieldsin a single query
* Enhanced LLM understanding by transforming flat rows intorelational, structured prompts Option A is misleading - LLMs rely onsemantic similarity, not SQL-like structured queries.
B is false - CSV is not auto-converted into XLSX.
C is incorrect - streaming is not yet supported; indexing is a prerequisite.
In short, UiPath enablessemantic grounding of structured datalike CSVs by reformatting them intoJSON- style embeddings, improving retrieval quality, summarization, and task-specific use cases.

NEW QUESTION # 41
What is one of the key benefits of providing RAG as a service to UiPath generative AI experiences?
  • A. It eliminates the need for knowledge bases by integrating all proprietary data directly into generative applications.
  • B. It reduces the risk of hallucination by referencing ground truth data stores.
  • C. It directly increases the LLM context window size without any interaction with knowledge bases.
  • D. It exclusively provides access to historical data sources without supporting real-time updates.
Answer: B
Explanation:
The correct answer is A - RAG (Retrieval-Augmented Generation) enhances generative AI experiences in UiPath by providing grounded, context-relevant data at runtime, which significantly reduces hallucinations.
Here's how it works:
When an LLM receives a query, RAG pulls relevant documents or snippets from enterprise data sources (like knowledge bases, SharePoint, Confluence).
This content is passed to the LLM as context, enabling the model to respond using ground truth, not generic or fabricated knowledge.
UiPath's GenAI platform and agentic agents use RAG to:
Enrich prompt context
Drive document-based answers
Support fact-checked decisions in customer service, HR, IT, etc.
Option B is false - RAG doesn't alter the LLM's context window.
C is incorrect - RAG works because it queries live knowledge bases.
D is wrong - RAG supports real-time dynamic data, not just historical.

NEW QUESTION # 42
What steps must be completed when creating evaluations from scratch for a new evaluation set in UiPath?
  • A. The evaluation set can only be created using imported JSON data from previous evaluations of other agents.
  • 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. Assign evaluators immediately after creating the new evaluation set name, then configure inputs and expected outputs later.
  • D. Once the evaluation set is created, all included evaluations are automatically scored based only on input values and expected outputs.
Answer: B
Explanation:
Bis correct - creating a newevaluation setin UiPath involves a multi-step process designed to enable qualitative and quantitative review of agent behavior.
Steps include:
* Namingthe evaluation set
* Addinginput promptsandexpected outputs
* Saving each test item (often called "evaluations")
* Assigning evaluators, who will manually or automatically score the results This process enablestestable, repeatable evaluationof agent behavior before deployment - ensuring the model produces correct, useful, and safe outputs.
Options A and C are incorrect:
* A reverses the order: inputs and expected outputs are neededbeforeevaluators.
* C is false - evaluation setscan be built from scratch.D implies scoring is automatic, but human reviewers or comparison logic are often required for nuanced evaluations.
This aligns with UiPath's best practices inagent validationand post-deployment assurance.

NEW QUESTION # 43
Why is mapping processes a critical step in identifying opportunities for agentic automation?
  • A. It examines broader workflows without focusing on individual steps, missing granular opportunities for automation.
  • B. It prioritizes identifying potential ROI metrics before establishing specific process mapping, potentially overlooking optimization areas.
  • C. It allows pinpointing specific steps or sub-tasks within a workflow that could be automated, improving efficiency and reducing errors.
  • D. It assumes mapping processes is sufficient to complete automation implementation without considering task dependencies or broader workflows.
Answer: C
Explanation:
Cis correct - mapping processes during agentic discovery is essential because it allows teams tozoom into specific tasks or sub-processeswhere agentic automation can deliver the highest value.
UiPath'sAgentic Design Blueprintmethodology emphasizes this as afoundational step. By creating detailed
"as-is" process maps, teams can:
* Spotrepetitive tasks(ideal for RPA)
* Findjudgment-based decisions(ideal for agents)
* Highlightescalation points, delays, and handoffs
This clarity helps identify:
* Which actions can be automated
* Which roles require agent augmentation
* What context (data or documents) is needed
Option A skips process mapping and risks missing real value.
B is too high-level - real insights come from step-level granularity.
D is misleading - mapping is necessary butnot sufficientfor full implementation.
Accurate process mapping creates avisual and logical foundationfor designing agents that integrate seamlessly into workflows - targeting the right problems and unlocking measurable ROI.

NEW QUESTION # 44
......
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