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

Oracle Agentic AI Foundations Associate Sample Questions:

Question #1

Which description defines memory poisoning in AI-agent systems?

A. Malicious content inserted into persistent memory stores.
B. A SQL injection attack against a database.
C. Hardware instability affecting RAM reliability.
D. Long prompts causing temporary context loss.

Reveal Solution Discussion 0

Correct Answer: A 🗳️