DumpTOP Oracle 1z0-1157-26덤프의 질문들과 답변들은 100%의 지식 요점과 적어도 98%의Oracle 1z0-1157-26시험 문제들을 커버하는 수년동안 가장 최근의Oracle 1z0-1157-26 시험 요점들을 컨설팅 해 온 시니어 프로 IT 전문가들의 그룹에 의해 구축 됩니다. Oracle 1z0-1157-26 시험적중율 높은 덤프로 시험패스하세요.
| Section | Objectives |
|---|---|
| Oracle AI Database for Agentic AI | - Oracle AI Vector Search - Agentic AI capabilities in Oracle AI Database |
| Agent Fundamentals and Reasoning Patterns | - AI agent core concepts and architectures - Agent reasoning patterns and workflows |
| Implementing Model Context Protocol (MCP) | - MCP fundamentals and integration |
| OCI Enterprise AI Platform | - OCI Enterprise AI Agents and Knowledge Bases - OCI Enterprise AI services overview |
| Building Agents with LangChain and OpenAI Agent Stack | - OpenAI Agents SDK usage - LangChain components and chains |
| Enterprise Agent Development and Governance | - Guardrails, agent tracing and monitoring - Function calling and tool integration - Multi-agent systems and handoffs |
DumpTOP의 Oracle인증 1z0-1157-26덤프는 다른 덤프판매 사이트보다 저렴한 가격으로 여러분들께 가볍게 다가갑니다. Oracle인증 1z0-1157-26덤프는 기출문제와 예상문제로 되어있어 시험패스는 시간문제뿐입니다.
질문 # 44
Which OCI capability is required for serving fine-tuned or imported custom models?
정답：A
설명：
OCI Generative AI uses Dedicated AI Clusters to provide the isolated compute infrastructure required for fine- tuning and hosting custom model workloads. Oracle defines Dedicated AI Clusters as compute resources dedicated to a customer's models rather than shared with other tenancies. They can be created specifically for fine-tuning or for hosting model endpoints.
Oracle's current model onboarding workflow confirms the requirement. For imported models, the process includes importing the model, creating a hosting Dedicated AI Cluster , creating an endpoint, and then invoking the model. Fine-tuned models similarly require dedicated clusters for fine-tuning and subsequent hosting.
Shared On-Demand inference is appropriate for supported Oracle-hosted pretrained models, but it does not provide the dedicated isolated serving environment required by these custom model workflows. Object Storage can be an input location for model artifacts or training data, but it is storage rather than model-serving infrastructure. General-purpose Free Tier compute is likewise not the managed Generative AI capability Oracle specifies for custom-model serving.
Thus, B is correct and agrees with the uploaded course material.
Study Guide reference/topic: OCI Enterprise AI Agents - Dedicated AI Clusters, imported models, fine- tuned custom models, hosting clusters, and endpoints.
질문 # 45
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 # 46
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 # 47
Which prompt addition is used for zero-shot Chain-of-Thought prompting?
정답：B
질문 # 48
How are tool calls handled between the LLM and the application?
정답：A
설명：
For application-defined function tools, an LLM does not inherently execute the external operation itself.
Instead, the model produces a structured tool-call request identifying the selected function and supplying arguments. The application or agent runtime then interprets that request, applies appropriate validation or authorization, invokes the corresponding implementation, and returns the result to the model for subsequent reasoning.
The OpenAI Responses API defines function calls as custom tools supplied by the developer that enable the model to request execution of application code using typed arguments. The OpenAI Agents SDK makes the separation explicit: a FunctionTool contains the tool name, description, parameter JSON schema, and an on_invoke_tool implementation that actually executes when the runtime processes the model-generated arguments.
This boundary is critical for security. The model proposes an action; controlled application/runtime logic performs the action. The model is therefore not automatically granted direct database, filesystem, network, or operating-system privileges merely because a tool has been described to it.
Options A, B, and D incorrectly remove this enforcement boundary. Consequently, C is the correct architectural description and matches the supplied question source.
Study Guide reference/topic: OpenAI Responses API and Agents SDK - function calling, structured tool calls, application execution, validation, schemas, and security boundaries.
질문 # 49
......
우리DumpTOP 에서 제공하는 학습가이드에는 IT전문가들이 만들어낸 시험대비 자료들과Oracle 1z0-1157-26인증시험의 완벽한 문제와 답들입니다. 그리고 우리DumpTOP에서는 IT업계에서의 높은 신뢰감으로 여러분들한테 100%보장을 드립니다. 우리에 믿음을 드리기 위하여Oracle 1z0-1157-26관련자료의 일부분 문제와 답 등 샘플을 무료로 다운받아 체험해볼수 있게 제공합니다.
1z0-1157-26최신 인증시험 기출문제: https://www.dumptop.com/Oracle/1z0-1157-26-dump.html