Oracle 1z0-1157-26 real exam prep : Agentic AI Foundations Associate

  • Exam Code: 1z0-1157-26
  • Exam Name: Agentic AI Foundations Associate
  • Updated: Sep 13, 2026
  • Q&As: 60 Questions and Answers

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Oracle 1z0-1157-26 Exam Syllabus Topics:

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

                                                            Oracle Agentic AI Foundations Associate Sample Questions:

                                                            Question #1

                                                            Which Python package is installed first for a simple OCI Responses API setup?

                                                            • A. openai
                                                            • B. django
                                                            • C. boto3
                                                            • D. requests-html
                                                            Answer: A

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                                                            Question #2

                                                            Which statement describes the purpose of the OpenAI Responses API?

                                                            • A. It hosts open-source models locally on the developer's own hardware.
                                                            • B. It trains language models from scratch on customer-supplied data.
                                                            • C. It compresses prompts to reduce the number of tokens billed.
                                                            • D. It sends input to an OpenAI model and returns generated output.
                                                            Answer: D

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

                                                            Question #3

                                                            What is the default distance metric for VECTOR_DISTANCE in Oracle for non-BINARY vectors?

                                                            • A. Euclidean (L2) distance
                                                            • B. COSINE distance
                                                            • C. Levenshtein edit distance
                                                            • D. Bitwise XOR distance
                                                            Answer: B

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                                                            Question #4

                                                            Which SQL function computes distance between vectors in Oracle AI Vector Search?

                                                            • A. VECTOR_SCORE()
                                                            • B. SCORE_SIMILARITY()
                                                            • C. EMBEDDING_DISTANCE()
                                                            • D. VECTOR_DISTANCE()
                                                            Answer: D

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                                                            Question #5

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

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