Oracle Agentic AI Foundations Associate : 1z0-1157-26

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Exam Code: 1z0-1157-26

Exam Name: Agentic AI Foundations Associate

Updated: Sep 12, 2026

Q & A: 60 Questions and Answers

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Oracle 1z0-1157-26 Exam Syllabus Topics:

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

                                                                Oracle Agentic AI Foundations Associate Sample Questions:

                                                                Question #1

                                                                Which approaches are supported by OCI Enterprise AI Agents?

                                                                • A. Call the Responses API directly or deploy a hosted agent application.
                                                                • B. Deploy using Python-only or Java-only options.
                                                                • C. Build a frontend agent or a backend agent.
                                                                • D. Select a free or paid deployment model.
                                                                Reveal Solution  Discussion  0

                                                                Correct Answer: A  🗳️

                                                                Question #2

                                                                Which prompt addition is used for zero-shot Chain-of-Thought prompting?

                                                                • A. Let's think step by step.
                                                                • B. Disable all external retrieval tools.
                                                                • C. Upload a structured CSV first.
                                                                • D. Set temperature equal to zero.
                                                                Reveal Solution  Discussion  0

                                                                Correct Answer: A  🗳️

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

                                                                Question #3

                                                                In OpenAI Agents SDK, how does the model select which tool to call?

                                                                • A. It uses tool names, descriptions, and schemas.
                                                                • B. It calls every registered tool before answering.
                                                                • C. It selects a tool randomly unless hardcoded.
                                                                • D. It selects the first registered tool by name.
                                                                Reveal Solution  Discussion  0

                                                                Correct Answer: A  🗳️

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

                                                                Question #4

                                                                When is MCP most valuable?

                                                                • A. When building a single-agent app with no external dependencies.
                                                                • B. For simple local utilities like add() and multiply() .
                                                                • C. When tools are external or cloud-hosted.
                                                                • D. When defining prompts and output parsers inside a single LangChain chain.
                                                                Reveal Solution  Discussion  0

                                                                Correct Answer: C  🗳️

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

                                                                Question #5

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

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

                                                                Correct Answer: D  🗳️

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

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