Oracle인증 1z0-1157-26시험취득 의향이 있는 분이 이 글을 보게 될것이라 믿고Itcertkr에서 출시한 Oracle인증 1z0-1157-26덤프를 강추합니다. Itcertkr의Oracle인증 1z0-1157-26덤프는 최강 적중율을 자랑하고 있어 시험패스율이 가장 높은 덤프자료로서 뜨거운 인기를 누리고 있습니다. IT인증시험을 패스하여 자격증을 취득하려는 분은Itcertkr제품에 주목해주세요.
| Section | Objectives |
|---|---|
| Topic 1: Oracle AI Database for Agentic AI | - Oracle AI Vector Search - Agentic AI capabilities in Oracle AI Database |
| Topic 2: Enterprise Agent Development and Governance | - Function calling and tool integration - Multi-agent systems and handoffs - Guardrails, agent tracing and monitoring |
| Topic 3: Implementing Model Context Protocol (MCP) | - MCP fundamentals and integration |
| Topic 4: Building Agents with LangChain and OpenAI Agent Stack | - OpenAI Agents SDK usage - LangChain components and chains |
| 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 |
Itcertkr의 Oracle 인증 1z0-1157-26시험덤프공부자료 출시 당시 저희는 이런 크나큰 인지도를 갖출수 있을지 생각도 못했었습니다. 저희를 믿어주시고 구매해주신 분께 너무나도 감사한 마음에 더욱 열심히 해나가자는 결심을 하였습니다. Oracle 인증 1z0-1157-26덤프자료는Itcertkr의 전문가들이 최선을 다하여 갈고닦은 예술품과도 같습니다.100% 시험에서 패스하도록 저희는 항상 힘쓰고 있습니다.
질문 # 46
Which prompt addition is used for zero-shot Chain-of-Thought prompting?
정답：B
설명：
Zero-shot Chain-of-Thought prompting encourages a language model to decompose a problem into intermediate reasoning steps without supplying worked examples in the prompt. The classic prompting addition associated with this technique is "Let's think step by step." The technique is "zero-shot" because the user does not provide demonstrations showing how comparable problems should be solved; instead, a short natural-language instruction encourages stepwise decomposition.
OpenAI's published prompting guidance historically illustrates this exact technique, explaining that instructing a model to reason through a sequence of steps can improve performance on tasks requiring decomposition. The OpenAI Cookbook specifically gives "Let's think step by step" as an example of an instruction used to elicit a series of reasoning steps.
Setting temperature to zero affects sampling variability rather than creating Chain-of-Thought prompting.
Uploading structured data is unrelated to the reasoning technique, and disabling retrieval tools changes the model's information-access environment rather than prompting its problem-solving structure.
Accordingly, C is the intended and technically correct answer. The uploaded source also marks this exact phrase as correct.
Study Guide reference/topic: Introduction to AI Agents - prompting strategies, reasoning decomposition, zero-shot Chain-of-Thought, and agent reasoning patterns.
질문 # 47
Which statement describes the STDIO transport in MCP?
정답：A
설명：
In MCP, STDIO is designed for local process-based communication. Under this transport, the host or client application launches the MCP server as a subprocess and exchanges protocol messages through the server's standard input ( stdin ) and standard output ( stdout ). The uploaded examination source identifies this behavior as the correct definition.
STDIO should be contrasted with Streamable HTTP , which is designed for independently running, network- accessible MCP servers and remote communication. STDIO is particularly suitable for local integrations where the MCP server executable can run on the same machine as the host application.
The transport mechanism does not change MCP's underlying message semantics. MCP communication still uses JSON-RPC structures; STDIO does not replace JSON with an unrelated plain-text protocol.
Authentication requirements such as OAuth are also not an inherent requirement of STDIO. OAuth and HTTP-oriented authentication concerns primarily arise in remote server architectures.
Therefore, the defining STDIO behavior is local subprocess execution coupled with stdin/stdout message exchange.
Answer C correctly captures the architecture described by MCP and by the uploaded course source.
Study Guide reference/topic: Model Context Protocol (MCP) Fundamentals - STDIO transport, local MCP servers, subprocess lifecycle, JSON-RPC, and Streamable HTTP comparison.
질문 # 48
Assume an agent has access to the tools multiply(a, b) and divide(a, b). A user asks: "What is 15 multiplied by
8, then divided by 3?" In the OpenAI Agents SDK, how does the agent loop handle this multi-step task?
정답：D
설명：
The OpenAI Agents SDK implements an iterative agent loop in which the model determines which available capability should be invoked, receives the resulting observation, and can then make another tool call based on that updated context. Consequently, the model first requests multiply(15, 8) . The function executes and returns 120 ; that tool output is supplied back to the model. The model then determines that the remaining operation requires divide(120, 3) and requests the second tool.
OpenAI describes Agents as LLMs equipped with tools and explains that the SDK runtime manages repeated model/tool interactions until the workflow produces final output. Function tools expose schemas and executable implementations to this orchestration process.
The Runner is responsible for coordinating the loop; it does not independently substitute its own arithmetic logic for the model's tool decisions. Similarly, the SDK does not synthesize a new combined function when two distinct tools are required, nor does it invoke every available tool without reason.
The uploaded question source explicitly marks the sequential multiply-then-divide behavior as correct.
Study Guide reference/topic: OpenAI Responses API and Agents SDK - agent loop, sequential function calling, tool observations, and Runner orchestration.
질문 # 49
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 # 50
Which statement describes an LLM-based AI agent?
정답：C
설명：
An LLM-based AI agent is not simply a foundation model or chatbot user interface. It is a system in which an LLM acts as a reasoning and decision-making component while an orchestration layer gives it access to instructions, tools, state, external information, and potentially other agents. This architecture enables the system to decide which actions to perform and in what sequence to pursue a defined objective.
OpenAI describes an agent as an LLM equipped with instructions, tools, and handoffs, allowing it to plan, use tools to gather information or take actions, and delegate tasks when appropriate. Oracle similarly explains that AI agents use tools to communicate with external systems and dynamically determine which tools or integrations to use and in which order to achieve a goal.
An agent therefore does not require training an entirely new model architecture. The underlying LLM may be an existing pretrained model. What makes the system agentic is the combination of model reasoning with orchestration, tool execution, observations, state, and iterative decision-making.
Accordingly, D provides the correct architectural definition and matches the answer supplied in the uploaded source.
Study Guide reference/topic: Introduction to AI Agents - LLM-based agents, reasoning, tools, orchestration, actions, observations, and agent loops.
질문 # 51
......
Itcertkr에서는 가장 최신이자 최고인Oracle인증 1z0-1157-26시험덤프를 제공해드려 여러분이 IT업계에서 더 순조롭게 나아가도록 최선을 다해드립니다. Oracle인증 1z0-1157-26덤프는 최근 실제시험문제를 연구하여 제작한 제일 철저한 시험전 공부자료입니다. Oracle인증 1z0-1157-26시험준비자료는 Itcertkr에서 마련하시면 기적같은 효과를 안겨드립니다.
1z0-1157-26합격보장 가능 인증덤프: https://www.itcertkr.com/1z0-1157-26_exam.html