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

Oracle 1z0-1157-26 Actual PDF
  • Exam Code: 1z0-1157-26
  • Exam Name: Agentic AI Foundations Associate
  • Updated: Sep 14, 2026
  • Q & A: 60 Questions and Answers
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Oracle 1z0-1157-26 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Introduction to AI Agents15%- AI agent fundamentals
  • 1. Differentiate AI agents from traditional chatbots and rule-based workflows
    • 2. Core components of an AI agent: LLM, tools, and orchestration loop
      • 3. Safety considerations and guardrail techniques
        • 4. Agent reasoning patterns: Chain-of-Thought and ReAct
          Topic 2: 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 Autonomous AI Database MCP Server
                      • 7. Oracle AI Database Private Agent Factory
                        Topic 3: Model Context Protocol (MCP) Fundamentals15%- MCP architecture and integration
                        • 1. MCP transport options including stdio and Streamable HTTP
                          • 2. Role of MCP in standardizing integration between AI agents and external tools
                            • 3. MCP hosts, clients, servers, tools, resources, and prompts
                              • 4. JSON-RPC 2.0 message format
                                • 5. Integrating MCP capabilities into agentic AI workflows
                                  Topic 4: OpenAI Responses API and Agents SDK15%- OpenAI agent stack
                                  • 1. Agents SDK primitives: Agent, Runner, Tool, Handoffs, and Guardrails
                                    • 2. Function calling and tools
                                      • 3. OpenAI Responses API for agentic applications
                                        • 4. Multi-agent design patterns and handoffs
                                          • 5. Guardrails for validating inputs, outputs, and agent actions
                                            Topic 5: 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. Deployment and scaling options
                                                • 3. OCI Enterprise AI Agents development, orchestration, and execution
                                                  • 4. OCI Enterprise AI platform services for the enterprise AI agent lifecycle
                                                    • 5. Building and running AI agents with OCI Enterprise AI Agents
                                                      Topic 6: 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

                                                            Oracle Agentic AI Foundations Associate Sample Questions:

                                                            Question #1

                                                            What is Oracle AI Database Private Agent Factory?

                                                            • A. A backup utility for embeddings
                                                            • B. A no-code platform for building agents
                                                            • C. A managed orchestration layer for deploying to Kubernetes
                                                            • D. A hardware appliance for training models on-premises
                                                            Reveal Solution  Discussion  0

                                                            Correct Answer: B  🗳️

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

                                                            Question #2

                                                            Which approaches are supported by OCI Enterprise AI Agents?

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

                                                            Correct Answer: D  🗳️

                                                            Question #3

                                                            Which OCI capability is required for serving fine-tuned or imported custom models?

                                                            • A. Dedicated AI Clusters
                                                            • B. OCI Object Storage buckets
                                                            • C. Shared On-Demand inference
                                                            • D. Free Tier compute instances
                                                            Reveal Solution  Discussion  0

                                                            Correct Answer: A  🗳️

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                                                            Question #4

                                                            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 TestPDF members. You can sign-up / login (it's free).

                                                            Question #5

                                                            When is MCP most valuable?

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

                                                            Correct Answer: C  🗳️

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

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