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Oracle 1z0-1157-26 - Agentic AI Foundations Associate

Last Update Sep 30, 2026

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Total Questions: 57
Free Practice Questions: 34

Which statement describes an LLM-based AI agent?

Options:

A.

A newly trained standalone model type.

B.

A chatbot interface with additional visual components.

C.

A specialized transformer architecture.

D.

A model-based system that uses tools and orchestration.

Answer
D
Explanation

An LLM-based AI agent is not simply a foundation model or chatbot user interface. It is a system in which an LLM acts as a reasoning and decision-making component while an orchestration layer gives it access to instructions, tools, state, external information, and potentially other agents. This architecture enables the system to decide which actions to perform and in what sequence to pursue a defined objective.

OpenAI describes an agent as an LLM equipped with instructions, tools, and handoffs, allowing it to plan, use tools to gather information or take actions, and delegate tasks when appropriate. Oracle similarly explains that AI agents use tools to communicate with external systems and dynamically determine which tools or integrations to use and in which order to achieve a goal.

An agent therefore does not require training an entirely new model architecture. The underlying LLM may be an existing pretrained model. What makes the system agentic is the combination of model reasoning with orchestration, tool execution, observations, state, and iterative decision-making.

Accordingly, D provides the correct architectural definition and matches the answer supplied in the uploaded source.

Study Guide reference/topic: Introduction to AI Agents — LLM-based agents, reasoning, tools, orchestration, actions, observations, and agent loops.

In OpenAI Agents SDK, how does the model select which tool to call?

Options:

A.

It selects the first registered tool by name.

B.

It uses tool names, descriptions, and schemas.

C.

It calls every registered tool before answering.

D.

It selects a tool randomly unless hardcoded.

Answer
B
Explanation

Tool selection in the OpenAI Agents SDK is model-driven. Each function tool exposes structured metadata that gives the model enough information to determine whether the tool is appropriate and how it should be invoked. The SDK represents a function tool using a name , description , and JSON parameter schema . OpenAI's SDK reference explicitly defines these properties as information shown to the LLM, while function-tool helpers automatically generate the parameter schema from the Python function signature and derive descriptions from documentation when available.

During an agent run, the model evaluates the user's request together with the available tool definitions. It can then select an appropriate tool and generate arguments conforming to that tool's schema. This mechanism is fundamentally semantic and contextual: meaningful names and descriptions tell the model what a tool does, while schemas describe the arguments required to execute it.

There is no rule requiring the first registered tool to be selected, every tool to be invoked, or random selection. Such behavior would undermine agentic reasoning and dynamic orchestration. Consequently, B is the technically correct answer and is explicitly identified as correct in the uploaded question set.

Study Guide reference/topic: OpenAI Responses API and Agents SDK — Function Tools, tool metadata, JSON schemas, tool selection, and model-driven invocation.

===============

Agent has multiply(a,b) and divide(a,b) . User: "What is 15 multiplied by 8, then divided by 3?" How does the OpenAI Agents SDK handle this?

Options:

A.

The Runner performs all arithmetic internally without involving the model or tools.

B.

The model calls every available arithmetic tool before answering.

C.

The SDK automatically combines all arithmetic operations into a single tool call.

D.

The model calls multiply(15, 8) , receives the result, then calls divide(120, 3) .

Answer
D
Explanation

The OpenAI Agents SDK uses an iterative agent loop for multi-step tool execution. In this scenario, the model first determines that it needs the multiply tool and generates a call with the arguments 15 and 8 . The application executes that tool and returns 120 as a tool result. The model receives the updated conversation state, recognizes that another operation remains, and subsequently requests divide(120, 3) . The resulting value is then available for the final response. The uploaded course source explicitly specifies this sequence.

OpenAI's Agents SDK documentation confirms that the Runner repeatedly calls the LLM, executes requested tools, appends their results, and runs the model again until final output is produced.

The Runner does not independently decide to calculate the arithmetic itself. Nor does the SDK automatically merge unrelated function calls into one synthetic operation. Likewise, an agent does not invoke every registered tool indiscriminately; the model selects the tools required by the current task.

Therefore, D accurately describes the sequential model/tool interaction.

Study Guide reference/topic: OpenAI Responses API and Agents SDK — agent loop, sequential tool calls, tool results, Runner orchestration, and multi-step execution.

===============

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