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Oracle 1z0-1157-26 Exam Syllabus Topics:

Section	Weight	Objectives
LangChain for AI Agents	5%	- LangChain fundamentals and agent construction 1. LangChain agent reasoning and tool execution flow 2. LangChain core abstractions: chat models, prompts, tools, and agents 3. LangChain tools, prompts, and chains
Model Context Protocol (MCP) Fundamentals	15%	- MCP architecture and integration 1. JSON-RPC 2.0 message format 2. Integrating MCP capabilities into agentic AI workflows 3. MCP transport options including stdio and Streamable HTTP 4. MCP hosts, clients, servers, tools, resources, and prompts 5. Role of MCP in standardizing integration between AI agents and external tools
OCI Enterprise AI Agents	25%	- OCI Enterprise AI platform and agent services 1. OCI Enterprise AI Agents building blocks: Responses API, tools, memory, and vector stores 2. Building and running AI agents with OCI Enterprise AI Agents 3. OCI Enterprise AI platform services for the enterprise AI agent lifecycle 4. OCI Enterprise AI Agents development, orchestration, and execution 5. Deployment and scaling options
OpenAI Responses API and Agents SDK	15%	- OpenAI agent stack 1. OpenAI Responses API for agentic applications 2. Multi-agent design patterns and handoffs 3. Guardrails for validating inputs, outputs, and agent actions 4. Function calling and tools 5. Agents SDK primitives: Agent, Runner, Tool, Handoffs, and Guardrails
Introduction to AI Agents	15%	- AI agent fundamentals 1. Agent reasoning patterns: Chain-of-Thought and ReAct 2. Safety considerations and guardrail techniques 3. Core components of an AI agent: LLM, tools, and orchestration loop 4. Differentiate AI agents from traditional chatbots and rule-based workflows
Agentic AI for Oracle AI Database	25%	- Oracle AI Database agentic AI capabilities 1. Grounding agent responses with enterprise data from Oracle AI Database 2. Oracle AI Database Private Agent Factory 3. Oracle Autonomous AI Database MCP Server 4. Oracle AI Vector Search workflow: document chunking, embedding generation, similarity search, and retrieval 5. Select AI for natural-language interaction with Oracle AI Database 6. VECTOR data type, vector embeddings, and similarity search 7. Oracle AI Vector Search, Select AI, and MCP integration

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Oracle Agentic AI Foundations Associate Sample Questions (Q34-Q39):

NEW QUESTION # 34
Which prompt addition is used for zero-shot Chain-of-Thought prompting?

A. Upload a structured CSV first.
B. Disable all external retrieval tools.
C. Let's think step by step.
D. Set temperature equal to zero.

Answer: C

NEW QUESTION # 35
Which MCP primitive is model-controlled and used to perform actions?

A. Resources
B. Tools
C. Prompts
D. Schemas

Answer: B

Explanation:
In MCP, Tools are the primitive explicitly designed to be model-controlled. They represent executable functions that an MCP server exposes so that a language model can take actions, retrieve information, query databases, invoke APIs, modify files, or perform computations. The uploaded question set identifies Tools as the correct answer.
The official MCP specification defines three principal server primitives with different control models:
Prompts are user-controlled , Resources are application-controlled , and Tools are model-controlled . Tools can be discovered by the model-facing application and invoked automatically according to the model's contextual interpretation of the user's request.
Resources differ because they primarily provide contextual data such as file contents or database schemas.
Prompts provide reusable templates or instructions normally selected through user interaction. "Schemas" are not one of the three MCP primitives in this control hierarchy; schemas describe structures such as tool parameters rather than constituting a standalone primitive.
Therefore, A is correct.
Study Guide reference/topic: Model Context Protocol (MCP) Fundamentals - MCP primitives, Tools, Resources, Prompts, control hierarchy, and tool invocation.

NEW QUESTION # 36
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?

A. The Runner performs all arithmetic internally without involving the model or tools.
B. The model calls multiply(15, 8) , receives the result, then calls divide(120, 3) .
C. The model calls every available arithmetic tool before answering.
D. The SDK automatically combines all arithmetic operations into a single tool call.

Answer: B

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.

NEW QUESTION # 37
Assume an agent has access to the tools multiply(a, b) and divide(a, b). A user asks: "What is 15 multiplied by
8, then divided by 3?" In the OpenAI Agents SDK, how does the agent loop handle this multi-step task?

A. The model calls multiply(15, 8), receives the result, then calls divide(120, 3)
B. The model calls every available arithmetic tool before answering
C. The Runner performs all arithmetic internally without involving the model or tools
D. The SDK automatically combines all arithmetic operations into a single tool call

Answer: A

Explanation:
The OpenAI Agents SDK implements an iterative agent loop in which the model determines which available capability should be invoked, receives the resulting observation, and can then make another tool call based on that updated context. Consequently, the model first requests multiply(15, 8) . The function executes and returns 120 ; that tool output is supplied back to the model. The model then determines that the remaining operation requires divide(120, 3) and requests the second tool.
OpenAI describes Agents as LLMs equipped with tools and explains that the SDK runtime manages repeated model/tool interactions until the workflow produces final output. Function tools expose schemas and executable implementations to this orchestration process.
The Runner is responsible for coordinating the loop; it does not independently substitute its own arithmetic logic for the model's tool decisions. Similarly, the SDK does not synthesize a new combined function when two distinct tools are required, nor does it invoke every available tool without reason.
The uploaded question source explicitly marks the sequential multiply-then-divide behavior as correct.
Study Guide reference/topic: OpenAI Responses API and Agents SDK - agent loop, sequential function calling, tool observations, and Runner orchestration.

NEW QUESTION # 38
Which behavior is NOT a characteristic of modern LLM-based AI agents?

A. Using external APIs or databases
B. Pursuing goals across multiple steps
C. Requiring every execution path to be predefined
D. Adjusting behavior based on observations

Answer: C

Explanation:
Modern LLM-based agents are specifically designed to avoid requiring every possible execution path to be predetermined. The uploaded course material therefore correctly identifies "Requiring every execution path to be predefined" as the behavior that is NOT characteristic of an agent.
OpenAI defines agents as systems capable of independently accomplishing workflows using an LLM to manage workflow execution and make decisions. An agent can determine when a workflow is complete, correct its actions after receiving observations, and dynamically select tools according to the current state.
This differs fundamentally from conventional deterministic automation in which developers encode every branch and execution path beforehand.
Agents commonly pursue objectives across multiple reasoning-and-action cycles. They can invoke external APIs, databases, search systems, or other tools; inspect the resulting observations; and choose subsequent actions. A typical agent loop continues until an exit condition is reached rather than following one permanently fixed sequence.
Predetermined rules may still be used for safety, permissions, and guardrails, but the complete path toward the goal does not need to be pre-scripted.
Therefore, D is the correct answer.
Study Guide reference/topic: Introduction to AI Agents - autonomy, agent loops, observations, dynamic tool use, multi-step goal execution, and deterministic workflows.

NEW QUESTION # 39
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