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Oracle 1Z0-1157-26 Exam Syllabus

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Before starting your 1Z0-1157-26 exam preparation, it is recommended to review the complete Oracle Agentic AI Foundations Associate exam syllabus and carefully go through the exam objectives listed below. Once you understand the exam structure and objectives, you should practice using our free 1Z0-1157-26 questions. We also provide premium 1Z0-1157-26 practice test, fully updated according to the latest exam objectives, to help you accurately assess your preparedness for the actual exam.

Oracle
Vendor
1Z0-1157-26
Exam Code
49
Total Questions
6
Total Exam Domains

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1Z0-1157-26 EXAM QUESTIONS

Oracle 1Z0-1157-26 Exam Objectives

Section 1: Introduction to AI Agents
Weight:
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  • Differentiate AI agents, traditional chatbots, and rule-based workflows based on autonomy, reasoning, and tool use
  • Describe the core components of an AI agent and their role in agent execution: LLM, Tools, Orchestration (Loop)
  • Describe agent reasoning patterns: Chain-of-Thought (CoT) and ReAct (Reasoning and Acting)
  • Explain safety considerations and guardrail techniques for AI agents
Section 2: LangChain for AI Agents
Weight:
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  • Describe LangChain core abstractions: chat models, prompts, tools, and agents and the role each plays in agent construction
  • Apply LangChain tools, prompts, and chains to build an AI agent
  • Explain the reasoning and tool execution flow within a LangChain agent
Section 3: Model Context Protocol (MCP) Fundamentals
Weight:
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  • Explain the role of the Model Context Protocol (MCP) in standardizing integration between AI agents and external tools
  • Explain MCP core components: hosts, clients, servers, tools, resources, and prompts and the role each plays in agent-tool integration
  • Describe the MCP message format (JSON-RPC 2.0) and transport options (stdio and Streamable HTTP)
  • Integrate MCP capabilities into an Agentic AI workflow
Section 4: OpenAI Responses API and Agents SDK
Weight:
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  • Explain how the OpenAI Responses API supports agentic applications
  • Explain how the core primitives of the OpenAI Agents SDK: Agent, Runner, Tool, Handoffs, and Guardrails support building agentic workflows
  • Apply function calling and tools to extend agent capabilities using the OpenAI Agents SDK
  • Explain multi-agent design patterns and how handoffs route work between specialized agents
  • Explain how guardrails in the OpenAI Agents SDK validate inputs, outputs, and agent actions to control agent behavior
Section 5: OCI Enterprise AI Agents
Weight:
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  • Describe OCI Enterprise AI platform services that support the enterprise AI agent lifecycle
  • Explain how the OCI Enterprise AI Agents service enables agent development, orchestration, and execution
  • Describe the building blocks of OCI Enterprise AI Agents, including the Responses API, tools, memory, and vector stores
  • Apply OCI Enterprise AI Agents capabilities to build and run a basic AI agent
  • Describe deployment and scaling options for OCI Enterprise AI Agents
Section 6: Agentic AI for Oracle AI Database
Weight:
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  • Explain how Oracle AI Database supports agentic AI workloads through Oracle AI Vector Search, Select AI, and MCP integration
  • Describe Oracle AI Vector Search concepts: VECTOR data type, vector embeddings, and similarity search
  • Explain the Oracle AI Vector Search workflow from document chunking and embedding generation to similarity search and retrieval
  • Apply Oracle AI Vector Search to ground agent responses by retrieving relevant enterprise data from Oracle AI Database
  • Explain how Oracle AI Database Private Agent Factory enables no-code AI agent creation
  • Explain how Select AI enables natural-language interaction with data in Oracle AI Database
  • Explain how the Oracle Autonomous AI Database MCP Server exposes database capabilities to MCP clie
Info
https://mylearn.oracle.com/ou/exam-unproctored/agentic-ai-foundations-associate-1z0-1157-26/163239/273973