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Oracle 1z0-1157-26 Exam Syllabus Topics:

SectionWeightObjectives
OCI Enterprise AI Agents25%- OCI Enterprise AI platform and agent services
  • 1. OCI Enterprise AI Agents building blocks: Responses API, tools, memory, and vector stores
    • 2. OCI Enterprise AI platform services for the enterprise AI agent lifecycle
      • 3. OCI Enterprise AI Agents development, orchestration, and execution
        • 4. Building and running AI agents with OCI Enterprise AI Agents
          • 5. Deployment and scaling options
            Introduction to AI Agents15%- AI agent fundamentals
            • 1. Safety considerations and guardrail techniques
              • 2. Core components of an AI agent: LLM, tools, and orchestration loop
                • 3. Agent reasoning patterns: Chain-of-Thought and ReAct
                  • 4. Differentiate AI agents from traditional chatbots and rule-based workflows
                    LangChain for AI Agents5%- LangChain fundamentals and agent construction
                    • 1. LangChain tools, prompts, and chains
                      • 2. LangChain core abstractions: chat models, prompts, tools, and agents
                        • 3. LangChain agent reasoning and tool execution flow
                          Agentic AI for Oracle AI Database25%- Oracle AI Database agentic AI capabilities
                          • 1. VECTOR data type, vector embeddings, and similarity search
                            • 2. Oracle AI Vector Search, Select AI, and MCP integration
                              • 3. Grounding agent responses with enterprise data from Oracle AI Database
                                • 4. Select AI for natural-language interaction with Oracle AI Database
                                  • 5. Oracle AI Vector Search workflow: document chunking, embedding generation, similarity search, and retrieval
                                    • 6. Oracle AI Database Private Agent Factory
                                      • 7. Oracle Autonomous AI Database MCP Server
                                        OpenAI Responses API and Agents SDK15%- OpenAI agent stack
                                        • 1. Agents SDK primitives: Agent, Runner, Tool, Handoffs, and Guardrails
                                          • 2. Guardrails for validating inputs, outputs, and agent actions
                                            • 3. Multi-agent design patterns and handoffs
                                              • 4. Function calling and tools
                                                • 5. OpenAI Responses API for agentic applications
                                                  Model Context Protocol (MCP) Fundamentals15%- MCP architecture and integration
                                                  • 1. Integrating MCP capabilities into agentic AI workflows
                                                    • 2. JSON-RPC 2.0 message format
                                                      • 3. MCP hosts, clients, servers, tools, resources, and prompts
                                                        • 4. Role of MCP in standardizing integration between AI agents and external tools
                                                          • 5. MCP transport options including stdio and Streamable HTTP

                                                            Oracle Agentic AI Foundations Associate Sample Questions:

                                                            Which native column type does Oracle AI Database use for storing vector embeddings?

                                                            • A. VECTOR
                                                            • B. VARCHAR2
                                                            • C. JSON
                                                            • D. BLOB
                                                            Reveal Solution  Discussion  0

                                                            Correct Answer: A  🗳️

                                                            Explanation: Only visible for Prep4away members. You can sign-up / login (it's free).

                                                            In the given LangChain chain, what is the role of StrOutputParser()? chain = prompt

                                                            • A. StrOutputParser()
                                                            • B. It stores tool execution history.
                                                            • C. It sends the prompt to the model API.
                                                            • D. It extracts plain text from the model response.
                                                            Reveal Solution  Discussion  0

                                                            Correct Answer: D  🗳️

                                                            Explanation: Only visible for Prep4away members. You can sign-up / login (it's free).

                                                            In a production MCP architecture, where are tool implementations hosted?

                                                            • A. Hardcoded inside the agent file using @tool decorators.
                                                            • B. On a separate MCP server.
                                                            • C. Inside the LLM's training weights.
                                                            • D. In the MCP client application that invokes the tools.
                                                            Reveal Solution  Discussion  0

                                                            Correct Answer: B  🗳️

                                                            Explanation: Only visible for Prep4away members. You can sign-up / login (it's free).

                                                            What integration problem does MCP address?

                                                            • A. Latency in cloud-hosted models
                                                            • B. Token-limit problem in large language models
                                                            • C. N × M custom-connector problem between AI apps and tools
                                                            • D. GPU memory fragmentation during inference
                                                            Reveal Solution  Discussion  0

                                                            Correct Answer: C  🗳️

                                                            Explanation: Only visible for Prep4away members. You can sign-up / login (it's free).

                                                            Which MCP primitive is model-controlled and used to perform actions?

                                                            • A. Resources
                                                            • B. Tools
                                                            • C. Prompts
                                                            • D. Schemas
                                                            Reveal Solution  Discussion  0

                                                            Correct Answer: B  🗳️

                                                            Explanation: Only visible for Prep4away members. You can sign-up / login (it's free).

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