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Oracle 1z0-1157-26 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Agentic AI for Oracle AI Database25%- Oracle AI Database agentic AI capabilities
  • 1. Grounding agent responses with enterprise data from Oracle AI Database
    • 2. Oracle Autonomous AI Database MCP Server
      • 3. Select AI for natural-language interaction with Oracle AI Database
        • 4. Oracle AI Vector Search workflow: document chunking, embedding generation, similarity search, and retrieval
          • 5. Oracle AI Database Private Agent Factory
            • 6. Oracle AI Vector Search, Select AI, and MCP integration
              • 7. VECTOR data type, vector embeddings, and similarity search
                Topic 2: Introduction to AI Agents15%- AI agent fundamentals
                • 1. Core components of an AI agent: LLM, tools, and orchestration loop
                  • 2. Agent reasoning patterns: Chain-of-Thought and ReAct
                    • 3. Safety considerations and guardrail techniques
                      • 4. Differentiate AI agents from traditional chatbots and rule-based workflows
                        Topic 3: Model Context Protocol (MCP) Fundamentals15%- MCP architecture and integration
                        • 1. JSON-RPC 2.0 message format
                          • 2. Integrating MCP capabilities into agentic AI workflows
                            • 3. MCP hosts, clients, servers, tools, resources, and prompts
                              • 4. MCP transport options including stdio and Streamable HTTP
                                • 5. Role of MCP in standardizing integration between AI agents and external tools
                                  Topic 4: LangChain for AI Agents5%- LangChain fundamentals and agent construction
                                  • 1. LangChain tools, prompts, and chains
                                    • 2. LangChain agent reasoning and tool execution flow
                                      • 3. LangChain core abstractions: chat models, prompts, tools, and agents
                                        Topic 5: OpenAI Responses API and Agents SDK15%- OpenAI agent stack
                                        • 1. Function calling and tools
                                          • 2. Agents SDK primitives: Agent, Runner, Tool, Handoffs, and Guardrails
                                            • 3. OpenAI Responses API for agentic applications
                                              • 4. Guardrails for validating inputs, outputs, and agent actions
                                                • 5. Multi-agent design patterns and handoffs
                                                  Topic 6: OCI Enterprise AI Agents25%- OCI Enterprise AI platform and agent services
                                                  • 1. Deployment and scaling options
                                                    • 2. OCI Enterprise AI platform services for the enterprise AI agent lifecycle
                                                      • 3. Building and running AI agents with OCI Enterprise AI Agents
                                                        • 4. OCI Enterprise AI Agents development, orchestration, and execution
                                                          • 5. OCI Enterprise AI Agents building blocks: Responses API, tools, memory, and vector stores

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                                                            Oracle Agentic AI Foundations Associate Sample Questions (Q41-Q46):

                                                            NEW QUESTION # 41
                                                            Which prompt addition is used for zero-shot Chain-of-Thought prompting?

                                                            Answer: D


                                                            NEW QUESTION # 42
                                                            Which OCI services are used for observability and auditing of deployed AI agents?

                                                            Answer: C

                                                            Explanation:
                                                            OCI production AI architectures use the standard OCI observability and governance services to provide operational visibility and accountability. OCI Logging collects and centralizes service and application logs; OCI Generative AI hosted applications can expose deployment logs that open directly in OCI Logging and the Observability and Management service. OCI Monitoring supplies metrics and alarms for monitoring resource health and operational conditions. OCI Audit records calls made to supported OCI public API endpoints, providing an authoritative record of administrative and resource-management actions for investigation and compliance. Oracle's architecture guidance specifically recommends enabling OCI Logging, OCI Monitoring, and OCI Audit logs for critical AI-platform components. The services in the other options have legitimate OCI purposes, but they do not collectively represent the principal observability-and-auditing stack. Therefore, option A is the verified combination. Oracle Docs


                                                            NEW QUESTION # 43
                                                            In JSON-RPC 2.0, what is the difference between a request and a notification?

                                                            Answer: A

                                                            Explanation:
                                                            The defining distinction is the presence of a request identifier and the expectation of a corresponding response. In JSON-RPC 2.0, a normal request contains an id value so that the sender can correlate the response with the request. A notification deliberately omits the ID because no response is expected. The supplied course material identifies exactly this distinction.
                                                            MCP uses JSON-RPC 2.0 as its underlying messaging protocol. Its architecture documentation explicitly states that clients and servers exchange requests and responses, while notifications are used where no response is required. MCP's notification examples contain no id field, and the documentation explains that this follows JSON-RPC notification semantics.
                                                            The difference has nothing to do with whether data is structured, whether encryption is enabled, or which transport is used. Both requests and notifications can carry structured JSON parameters. Security belongs to the transport/authentication layer, while MCP can transmit JSON-RPC messages over supported transports such as STDIO or Streamable HTTP.
                                                            Therefore, B is the precise protocol-level distinction.
                                                            Study Guide reference/topic: Model Context Protocol (MCP) Fundamentals - JSON-RPC 2.0 requests, responses, IDs, and notifications.


                                                            NEW QUESTION # 44
                                                            What is long-term memory in OCI Enterprise AI Agents?

                                                            Answer: A

                                                            Explanation:
                                                            OCI Enterprise AI Agents uses long-term memory to preserve useful information beyond the lifetime of an individual conversation. Oracle documents long-term memory as durable memory across conversations , associated through a subject identifier within an OCI Generative AI project. When enabled, important information can be extracted from conversations, converted into embeddings, persisted, and retrieved during subsequent interactions involving the same subject. This differs from short-term memory, which primarily maintains or compacts context within an ongoing conversation. Long-term memory is therefore not the model's pretraining corpus, a fixed training dataset, or general-purpose container block storage. It is an agent- oriented context mechanism designed to improve continuity and personalization while keeping memory governed within project boundaries. Consequently, option C precisely reflects Oracle's documented Enterprise AI Agents architecture. Oracle Docs


                                                            NEW QUESTION # 45
                                                            Which approaches are supported by OCI Enterprise AI Agents?

                                                            Answer: B


                                                            NEW QUESTION # 46
                                                            ......

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