1z0-1157-26 Testengine & 1z0-1157-26 Vorbereitungsfragen

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

                                                            >> 1z0-1157-26 Testengine <<

                                                            1z0-1157-26 Vorbereitungsfragen - 1z0-1157-26 Originale Fragen

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                                                            Oracle Agentic AI Foundations Associate 1z0-1157-26 Prüfungsfragen mit Lösungen (Q46-Q51):

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

                                                            Antwort: A

                                                            Begründung:
                                                            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.


                                                            47. Frage
                                                            Which approaches are supported by OCI Enterprise AI Agents?

                                                            Antwort: D


                                                            48. Frage
                                                            Which message format does MCP use for client-server communication?

                                                            Antwort: A

                                                            Begründung:
                                                            MCP uses JSON-RPC 2.0 as the underlying message protocol for communication between MCP clients and MCP servers. JSON-RPC provides a structured representation for requests, responses, errors, and one-way notifications while remaining independent of the underlying transport. This separation is important because the same protocol semantics can operate over STDIO or Streamable HTTP.
                                                            The MCP architecture documentation states that the data layer implements a JSON-RPC 2.0-based exchange protocol defining message structures and semantics. It also explains that the transport layer abstracts communication details, allowing the same JSON-RPC message format to operate across supported transports.
                                                            The MCP specification similarly requires messages between clients and servers to follow JSON-RPC structures, including methods, parameters, IDs for requests, and result/error structures for responses.
                                                            SOAP/XML is a different web-service protocol family; GraphQL is primarily a query language and API runtime; Protocol Buffers is a binary serialization technology. None is the MCP-defined wire-message format.
                                                            Therefore, option B is correct and agrees with the uploaded answer key.
                                                            Study Guide reference/topic: Model Context Protocol (MCP) Fundamentals - JSON-RPC 2.0, requests, responses, notifications, and transport independence.


                                                            49. Frage
                                                            Which statement describes the purpose of the OpenAI Responses API?

                                                            Antwort: D

                                                            Begründung:
                                                            The OpenAI Responses API is an inference and agent-interaction interface. At its fundamental level, an application supplies input together with a selected model and optional instructions, tools, or other configuration; the model then produces a response containing generated output. The uploaded course material states this core purpose directly and identifies C as correct.
                                                            OpenAI's current API reference defines the Responses endpoint as creating a model response from text, image, or file inputs and returning generated text, structured JSON, tool calls, or other supported response items. The input field provides content to the model, while the response object's output array contains items generated by the model.
                                                            Although modern Responses API functionality extends beyond simple text generation-for example, built-in tools, function calling, conversation state, structured outputs, and agentic workflows-the basic abstraction remains model input followed by generated model output.
                                                            It is not a prompt-compression billing service, a local model-hosting environment, or a foundation-model training API. Those alternatives describe completely different system responsibilities.
                                                            Therefore, C accurately expresses the core purpose being tested.
                                                            Study Guide reference/topic: OpenAI Responses API and Agents SDK - Responses endpoint, model input, generated output, tools, and agentic workflows.


                                                            50. Frage
                                                            Which native column type does Oracle AI Database use for storing vector embeddings?

                                                            Antwort: B

                                                            Begründung:
                                                            Oracle AI Database provides a native VECTOR data type specifically for storing vector embeddings. The uploaded course source identifies VECTOR as the correct native column type.
                                                            Oracle's official AI Vector Search documentation states that the built-in VECTOR data type provides the foundation for storing embeddings directly alongside relational business data. A table can therefore define a vector column in the same way it defines conventional Oracle columns, for example doc_vector VECTOR .
                                                            This native representation is important because Oracle AI Database can apply vector-specific SQL operations and vector indexes directly to stored embeddings. Applications can combine similarity search with relational predicates, JSON processing, graph operations, spatial queries, and standard SQL without moving embeddings into a separate specialized vector database.
                                                            Although Oracle may internally use storage mechanisms such as SecureFiles for vector representation, BLOB is not the logical SQL column type developers use for AI Vector Search embeddings . JSON and VARCHAR2 are likewise general-purpose data types and do not provide native vector semantics.
                                                            Therefore, A is correct.
                                                            Study Guide reference/topic: Agentic AI for Oracle AI Database - VECTOR data type, vector columns, embedding storage, vector indexes, and AI Vector Search.


                                                            51. Frage
                                                            ......

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                                                            1z0-1157-26 Vorbereitungsfragen: https://www.zertpruefung.ch/1z0-1157-26_exam.html