Oracle인증 1z0-1157-26시험은 IT인증자격증중 가장 인기있는 자격증을 취득하는 필수시험 과목입니다. Oracle인증 1z0-1157-26시험을 패스해야만 자격증 취득이 가능합니다. ExamPassdump의Oracle인증 1z0-1157-26는 최신 시험문제 커버율이 높아 시험패스가 아주 간단합니다. Oracle인증 1z0-1157-26덤프만 공부하시면 아무런 우려없이 시험 보셔도 됩니다. 시험합격하면 좋은 소식 전해주세요.
| Section | Objectives |
|---|---|
| Topic 1: Enterprise Agent Development and Governance | - Multi-agent systems and handoffs - Guardrails, agent tracing and monitoring - Function calling and tool integration |
| Topic 2: Building Agents with LangChain and OpenAI Agent Stack | - OpenAI Agents SDK usage - LangChain components and chains |
| Topic 3: Implementing Model Context Protocol (MCP) | - MCP fundamentals and integration |
| Topic 4: Oracle AI Database for Agentic AI | - Agentic AI capabilities in Oracle AI Database - Oracle AI Vector Search |
| Topic 5: OCI Enterprise AI Platform | - OCI Enterprise AI services overview - OCI Enterprise AI Agents and Knowledge Bases |
| Topic 6: Agent Fundamentals and Reasoning Patterns | - Agent reasoning patterns and workflows - AI agent core concepts and architectures |
Oracle 1z0-1157-26 덤프가 고객님의 기대를 가득 채워드릴수 있도록 정말로 노력하고 있는 ExamPassdump랍니다. Oracle 1z0-1157-26 덤프는 pdf버전과 소프트웨어버전으로만 되어있었는데 최근에는 휴대폰에서가 사용가능한 온라인버전까지 개발하였습니다. 날따라 새로운 시스템을 많이 개발하여 고객님께 더욱 편하게 다가갈수 있는 ExamPassdump가 되겠습니다.
질문 # 14
Which message format does MCP use for client-server communication?
정답：A
설명：
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.
질문 # 15
Which three model categories are available through OCI Enterprise AI Models?
정답：B
설명：
OCI Enterprise AI Models provides managed foundation-model capabilities oriented around three principal inference tasks: Chat, Embeddings, and Rerank . Chat models generate conversational or instructional responses and form the reasoning/generation foundation for many agentic applications. Embed models transform text or other supported content into numerical vector representations, enabling semantic search, recommendations, clustering, classification, and retrieval-augmented generation. Rerank models take an initial collection of retrieved candidates and reorder them according to relevance to a query, improving retrieval quality before selected context is passed to a generative model.
Oracle's current OCI Generative AI documentation explicitly identifies Chat, Embeddings, and Rerank as core Enterprise AI Model tasks. Robotics is not one of the defined Enterprise AI model categories, while clustering and classification are applications of embeddings rather than independent model categories. SQL, NoSQL, and Graph describe database technologies rather than generative-model classes.
The uploaded question source also identifies the Chat/Embed/Rerank combination as the correct selection.
Study Guide reference/topic: OCI Enterprise AI Agents - Enterprise AI Models, chat inference, embeddings, reranking, and model-supported agent workflows.
질문 # 16
What is the purpose of OCI Enterprise AI Governance?
정답：A
설명：
OCI Enterprise AI Governance provides the control framework required to operate generative and agentic AI workloads securely in enterprise environments. Oracle defines governance as a combination of infrastructure protection, access control, network security, and runtime safety mechanisms. Key capabilities include OCI IAM policies , which determine who can access and manage Generative AI resources; Private Endpoints , which prevent model traffic from requiring public network exposure; Zero Trust Packet Routing , which introduces identity-aware network enforcement; and Guardrails , which apply safety and compliance controls to model inputs and outputs.
Oracle Guardrails specifically support mechanisms including content moderation, prompt-injection detection, and personally identifiable information detection. These controls address AI-specific operational and security risks rather than model lifecycle rollback or performance optimization.
Therefore, option D accurately expresses the purpose of Enterprise AI Governance. Model version management, runtime implementation, and latency monitoring may be operational concerns in an AI platform, but they are not the principal governance function described by OCI. The uploaded examination source also identifies D as the correct answer.
Study Guide reference/topic: OCI Enterprise AI Agents - Enterprise AI Governance, IAM, Private Endpoints, Zero Trust Packet Routing, and Guardrails.
질문 # 17
Which approaches can generate embeddings for Oracle AI Vector Search workflows?
정답：C
설명：
Oracle AI Vector Search supports both in-database embedding generation and external embedding providers , making A the correct answer. The supplied question source identifies the same combination.
For in-database processing, Oracle AI Database includes an ONNX runtime. Compatible ONNX embedding models can be imported as database objects and invoked directly through SQL using functions such as VECTOR_EMBEDDING . This allows vectorization to occur without moving source data outside the database.
Oracle also supports REST-based embedding generation. DBMS_VECTOR.UTL_TO_EMBEDDING and related APIs can call external or local providers. Officially documented providers include OCI Generative AI, Cohere, OpenAI, Google AI, Hugging Face, Vertex AI, Mistral, Ollama, and Private AI, depending on the operation and configuration.
Manual Python export/import workflows are technically possible in custom architectures, but they are not the supported approaches being tested. Option C is explicitly false because Oracle supports in-database ONNX inference.
Therefore, A correctly captures Oracle's native and external embedding-generation strategies.
Study Guide reference/topic: Agentic AI for Oracle AI Database - ONNX Runtime, VECTOR_EMBEDDING, DBMS_VECTOR, REST embedding providers, and AI Vector Search.
질문 # 18
Which set lists built-in tool categories supported by OCI Enterprise AI Agents?
정답：C
설명：
OCI Enterprise AI Agents supports a defined set of OpenAI-compatible agent tools through the OCI Responses API. Oracle's current documentation identifies File Search, Code Interpreter, Function Calling, and MCP Calling as supported tool categories.
File Search allows an agent to retrieve relevant information from indexed content and vector stores. Code Interpreter provides a controlled environment for computational or programmatic analysis. Function Calling lets the model request execution of application-defined functions with structured parameters. MCP Calling enables the agent to discover and invoke capabilities made available by remote Model Context Protocol servers. Together, these mechanisms allow an LLM to move beyond text generation and perform retrieval, computation, application actions, and standardized external-system integration.
Oracle additionally provides supporting agent resources such as Files, Vector Stores, Containers, Conversations, Projects, and memory capabilities, while SQL Search/NL2SQL is available as an OCI-native agent capability.
SSH, FTP, RDP, VCN routing, load balancing, SMS, and fax are not the four built-in tool categories identified in the OCI Enterprise AI Agents curriculum. Consequently, A is correct and agrees with the uploaded question set.
Study Guide reference/topic: OCI Enterprise AI Agents - File Search, Code Interpreter, Function Calling, MCP Calling, Vector Stores, and agent tools.
질문 # 19
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