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

Exam Code: 1z0-1157-26

Exam Name: Agentic AI Foundations Associate

Updated: Sep 11, 2026

Q & A: 60 Questions and Answers

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Oracle 1z0-1157-26 Exam Syllabus Topics:

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

Oracle Agentic AI Foundations Associate Sample Questions:

Question #1

Which message format does MCP use for client-server communication?

A. JSON-RPC 2.0
B. GraphQL
C. Protocol Buffers
D. SOAP/XML

Answer: A