Oracle 1z0-1157-26 Q&A - in .pdf

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

SectionWeightObjectives
Topic 1: OCI Enterprise AI Agents25%- OCI Enterprise AI platform and agent services
  • 1. OCI Enterprise AI platform services for the enterprise AI agent lifecycle
    • 2. OCI Enterprise AI Agents development, orchestration, and execution
      • 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. Deployment and scaling options
            Topic 2: OpenAI Responses API and Agents SDK15%- OpenAI agent stack
            • 1. Agents SDK primitives: Agent, Runner, Tool, Handoffs, and Guardrails
              • 2. Multi-agent design patterns and handoffs
                • 3. Function calling and tools
                  • 4. Guardrails for validating inputs, outputs, and agent actions
                    • 5. OpenAI Responses API for agentic applications
                      Topic 3: 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. Safety considerations and guardrail techniques
                            • 4. Agent reasoning patterns: Chain-of-Thought and ReAct
                              Topic 4: 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. Select AI for natural-language interaction with Oracle AI Database
                                    • 4. Grounding agent responses with enterprise data from Oracle AI Database
                                      • 5. Oracle AI Vector Search workflow: document chunking, embedding generation, similarity search, and retrieval
                                        • 6. Oracle AI Database Private Agent Factory
                                          • 7. Oracle Autonomous AI Database MCP Server
                                            Topic 5: LangChain for AI Agents5%- LangChain fundamentals and agent construction
                                            • 1. LangChain agent reasoning and tool execution flow
                                              • 2. LangChain core abstractions: chat models, prompts, tools, and agents
                                                • 3. LangChain tools, prompts, and chains
                                                  Topic 6: Model Context Protocol (MCP) Fundamentals15%- MCP architecture and integration
                                                  • 1. MCP transport options including stdio and Streamable HTTP
                                                    • 2. Integrating MCP capabilities into agentic AI workflows
                                                      • 3. MCP hosts, clients, servers, tools, resources, and prompts
                                                        • 4. JSON-RPC 2.0 message format
                                                          • 5. Role of MCP in standardizing integration between AI agents and external tools

                                                            Oracle Agentic AI Foundations Associate Sample Questions:

                                                            Question #1

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

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

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                                                            Question #2

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

                                                            • A. Dedicated AI Clusters
                                                            • B. Free Tier compute instances
                                                            • C. Shared On-Demand inference
                                                            • D. OCI Object Storage buckets
                                                            Answer: A

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                                                            Question #3

                                                            Agent has multiply(a,b) and divide(a,b) . User: "What is 15 multiplied by 8, then divided by 3?" How does the OpenAI Agents SDK handle this?

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

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                                                            Question #4

                                                            Which description defines memory poisoning in AI-agent systems?

                                                            • A. Long prompts causing temporary context loss.
                                                            • B. A SQL injection attack against a database.
                                                            • C. Hardware instability affecting RAM reliability.
                                                            • D. Malicious content inserted into persistent memory stores.
                                                            Answer: D

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                                                            Question #5

                                                            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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