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:
SectionObjectives
Topic 1: Introduction to AI Agents- AI agent fundamentals and architecture
  • 1. Agent reasoning patterns including Chain-of-Thought and ReAct
    • 2. Core agent components: LLMs, tools, and orchestration loops
      • 3. AI agents, traditional chatbots, and rule-based systems
        • 4. Safety, guardrails, and responsible agentic workflows
          - Agent development concepts
          • 1. Function calling and tool use
            • 2. Multi-agent design patterns and handoffs
              • 3. OpenAI Agents SDK guardrails
                Topic 2: LangChain for AI Agents- LangChain fundamentals
                • 1. Building agents with LangChain
                  • 2. Tools, tool schemas, and tool execution
                    • 3. LangChain and LangChain Expression Language
                      • 4. Agent invocation and orchestration flow
                        Topic 3: Agentic AI for Oracle AI Database- Oracle AI Database agentic AI capabilities
                        • 1. Oracle Autonomous AI Database MCP Server
                          • 2. Document chunking, embedding generation, and retrieval
                            • 3. Grounding agent responses with enterprise data
                              • 4. Select AI
                                • 5. Oracle AI Vector Search
                                  • 6. Vector data types, embeddings, and similarity search
                                    • 7. Oracle AI Database Private Agent Factory
                                      Topic 4: OpenAI Responses API and Agents SDK- OpenAI agent development
                                      • 1. Guardrails and tracing
                                        • 2. OpenAI Agents SDK
                                          • 3. Responses API
                                            • 4. Function calling and tools
                                              • 5. Multi-agent handoffs
                                                Topic 5: Introduction to MCP- Model Context Protocol fundamentals
                                                • 1. Tool discovery and interoperability
                                                  • 2. MCP concepts and architecture
                                                    • 3. MCP clients and servers
                                                      Topic 6: OCI Enterprise AI Agents- OCI Enterprise AI platform
                                                      • 1. Deployment and scaling
                                                        • 2. OCI Enterprise AI Agents service
                                                          • 3. Responses API, tools, memory, and vector stores
                                                            • 4. Agent development, orchestration, and execution
                                                              • 5. Building and running AI agents
                                                                Oracle Agentic AI Foundations Associate Sample Questions:
                                                                Question #1

                                                                What integration problem does MCP address?

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

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                                                                Question #2

                                                                Why are docstrings especially important when defining LangChain tools with the @tool decorator?

                                                                • A. They tell the LLM when and how to use the tool.
                                                                • B. They determine which API provider LangChain uses.
                                                                • C. They encrypt tool arguments before execution.
                                                                • D. They improve Python execution speed.
                                                                Answer: A

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                                                                Question #3

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

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

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                                                                Question #4

                                                                What is the architectural advantage of Autonomous AI Database MCP Server over a separately deployed third- party MCP server?

                                                                • A. It eliminates the need for SQL queries entirely.
                                                                • B. It uses a common MCP host for all database connections.
                                                                • C. It moves all query execution to a separate compute tier outside the database.
                                                                • D. It reduces operational overhead and enforces security inside the database.
                                                                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 model calls every available arithmetic tool before answering
                                                                • B. The model calls multiply(15, 8), receives the result, then calls divide(120, 3)
                                                                • C. The SDK automatically combines all arithmetic operations into a single tool call
                                                                • D. The Runner performs all arithmetic internally without involving the model or tools
                                                                Answer: B

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