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A Oracle Agentic AI Foundations Associate credential carries weight because Oracle sets the standards employers trust. Fast2test helps you earn it with 60 expert-written 1z0-1157-26 practice questions, reviewed and refreshed for 2026.

Oracle 1z0-1157-26 Exam Overview:

Certification Vendor:Oracle
Exam Name:Agentic AI Foundations Associate
Exam Number:1Z0-1157-26
Available Languages:Japanese, English
Real Exam Qty:40
Exam Price:Free
Passing Score:65%
Related Certifications:Agentic AI Certified Foundations Associate
Exam Format:Multiple Choice
Exam Duration:60 minutes
Sample Questions:Oracle 1z0-1157-26 Sample Questions
Exam Way:Online certification exam
Pre Condition:No formal prerequisite exam is stated. Oracle recommends basic knowledge of LLM concepts, practical Python knowledge, and basic Oracle Cloud Infrastructure knowledge.
Official Syllabus URL:https://www.oracle.com/education/certification/

Oracle 1z0-1157-26 Exam Syllabus Topics:

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

                                                                Oracle 1z0-1157-26 FAQs: What Every Candidate Asks

                                                                The 1z0-1157-26 exam, officially titled Agentic AI Foundations Associate, is the qualifying test for the Oracle Certification certification from Oracle, a credential at the Associate level. Passing it proves you have the skills employers look for in certified professionals, and it can also support progress toward related credentials such as Agentic AI Certified Foundations Associate.

                                                                The Oracle Agentic AI Foundations Associate exam gives you 60 minutes to work through 40 questions. That is a steady pace with little room for second-guessing, so train yourself to read each question once, flag the difficult ones, and keep moving. Before exam day, sit at least two full timed sessions in the Fast2test test engine — when the clock feels familiar, it stops being a threat.

                                                                You need 65% to pass, and the official registration fee is Free. Fall short and you pay that fee in full again for every retake, which makes solid preparation the cheaper option by far. Work through the Fast2test practice questions until you score comfortably above the passing mark, then book your seat.

                                                                No formal prerequisite exam is stated. Oracle recommends basic knowledge of LLM concepts, practical Python knowledge, and basic Oracle Cloud Infrastructure knowledge. Eligibility rules can change over time, so before you register, confirm the latest requirements on the official exam page: Oracle 1z0-1157-26 exam overview.

                                                                Yes. Fast2test offers a free PDF demo for the Oracle Agentic AI Foundations Associate exam so you can judge the quality of our questions and answers before paying anything. Every purchase also comes with 365 days of free updates, and once that period expires you can extend your update service at a 50% discount.

                                                                Your purchase is protected by a 100% money-back guarantee. If you sit the corresponding 1z0-1157-26 exam within 60 days of buying and do not pass, send us a scan of your exam enrollment slip together with your official Score Report (PDF) within 2 days of the exam date — the candidate name must match the payer's name — and we will process your full refund within 7 days. Please note that exams taken within 3 days of purchase, materials downloaded without ever sitting the exam, free products, and expired orders are not covered. If you would rather not have a refund, you can exchange your order for two exam products of equal value, free of charge, and keep the update service on your original purchase.

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                                                                The Oracle Agentic AI Foundations Associate syllabus is organized into 6 domains. The main areas include Introduction to MCP, OpenAI Responses API and Agents SDK, and LangChain for AI Agents. Scroll up to the Exam Topics section above for the complete, current outline before you plan your study schedule.

                                                                Oracle Agentic AI Foundations Associate Sample Questions:

                                                                Question 1

                                                                In the OpenAI Agents SDK, how does a Handoff differ from the Manager pattern?

                                                                A. A Handoff keeps control; a Manager transfers control.
                                                                B. A Handoff transfers control; a Manager keeps control.
                                                                C. A Handoff uses async execution; a Manager uses sync execution.
                                                                D. A Handoff calls tools directly; a Manager only routes messages.


                                                                Question 2

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

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


                                                                Question 3

                                                                In the context of MCP, what does the "USB-C for AI" analogy emphasize?

                                                                A. MCP makes AI models physically faster.
                                                                B. MCP needs specialized hardware to run.
                                                                C. MCP is a standardized interface.
                                                                D. MCP is preinstalled on all modern computers.


                                                                Question 4

                                                                What is the purpose of the Vector Stores API?

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


                                                                Question 5

                                                                Which approaches can generate embeddings for Oracle AI Vector Search workflows?

                                                                A. Train a custom embedding model and manually insert vectors row by row
                                                                B. Use external REST APIs exclusively, since in-database embedding is not supported
                                                                C. Export data to a Python environment, generate embeddings, then reimport via SQL Loader
                                                                D. Use ONNX models in-database or external REST-based embedding providers


                                                                Solutions:

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

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