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

  • Exam Code: 1z0-1157-26
  • Exam Name: Agentic AI Foundations Associate
  • Updated: Sep 08, 2026     Q & A: 60 Questions and Answers

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Oracle 1z0-1157-26 Exam Syllabus Topics:

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

                                                                Oracle Agentic AI Foundations Associate Sample Questions:

                                                                Question 1

                                                                In JSON-RPC 2.0, what is the difference between a request and a notification?

                                                                A. A request is sent unencrypted, while a notification is encrypted before processing.
                                                                B. A request can carry structured data, while a notification can carry only text.
                                                                C. A request uses HTTP, while a notification uses a different transport protocol.
                                                                D. A request includes an ID and expects a reply, while a notification does not.


                                                                Question 2

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

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


                                                                Question 3

                                                                What is the purpose of the Vector Stores API?

                                                                A. Translating text between languages.
                                                                B. Encrypting Object Storage buckets.
                                                                C. Streaming video into AI agents.
                                                                D. Indexing and retrieving data by meaning.


                                                                Question 4

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

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


                                                                Question 5

                                                                Why is chunking necessary before generating embeddings for large documents?

                                                                A. To convert text into SQL-compliant rows.
                                                                B. To overcome token limits for large documents.
                                                                C. To automatically encrypt content before indexing.
                                                                D. To remove semantic meaning from the document.


                                                                Solutions:

                                                                Question 1
                                                                Answer: D
                                                                Question 2
                                                                Answer: A
                                                                Question 3
                                                                Answer: D
                                                                Question 4
                                                                Answer: A
                                                                Question 5
                                                                Answer: B

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