VCESoft的經驗豐富的專家團隊開發出了針對Oracle 1z0-1157-26 認證考試的有效的培訓計畫，很適合參加Oracle 1z0-1157-26 認證考試的考生。VCESoft為你提供的都是高品質的產品，可以讓你參加Oracle 1z0-1157-26 認證考試之前做模擬考試，可以為你參加考試做最好的準備。
| Section | Objectives |
|---|---|
| Topic 1: Agentic AI for Oracle AI Database | - Oracle AI Database agentic AI capabilities
|
| Topic 2: Introduction to AI Agents | - AI agent fundamentals and architecture
|
| Topic 3: OCI Enterprise AI Agents | - OCI Enterprise AI platform
|
| Topic 4: Introduction to MCP | - Model Context Protocol fundamentals
|
| Topic 5: LangChain for AI Agents | - LangChain fundamentals
|
| Topic 6: OpenAI Responses API and Agents SDK | - OpenAI agent development
|
“如果放棄了，那比賽同時也就結束了。”這是來自安西教練的一句大家都熟知的名言。比賽是這樣，同樣考試也是這樣的。有很多人因為沒有充分的時間準備考試從而放棄了參加1z0-1157-26認證考試。但是，如果使用了好的資料，即使只有很短的時間來準備，你也完全可以以高分通過1z0-1157-26考試。不相信嗎？VCESoft的考古題就是這樣的資料。趕快試一下吧。
問題 #28
What is the purpose of OCI Enterprise AI Governance?
答案：C
解題說明：
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.
問題 #29
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?
答案：A
解題說明：
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.
問題 #30
Which responsibilities are handled by OCI Enterprise AI Agents?
答案：A
解題說明：
OCI Enterprise AI Agents provides the operational and orchestration capabilities required to run agentic applications at production scale. The uploaded course source identifies these responsibilities as hosted endpoints, runtime scaling, session management, and observability . Current Oracle documentation supports that architectural classification.
OCI Generative AI Applications provide a managed runtime for agent workloads and centralize configuration for scaling, storage, networking, authentication, and deployments. Active deployments expose managed endpoints, while autoscaling controls can increase or decrease replicas according to workload metrics. OCI's Responses API also provides conversation state, Conversations, memory, and related context-management facilities for stateful agent interaction. Operational visibility is supported through OCI metrics, monitoring, endpoint telemetry, tracing, and hosted application logs integrated with OCI Observability and Management.
Document chunking/indexing is a retrieval-processing responsibility rather than the complete agent-platform role. Prompt definition remains application logic, and OCI network routing is handled by underlying OCI networking services.
Therefore, C best represents the production responsibilities of the Enterprise AI Agents layer.
Study Guide reference/topic: OCI Enterprise AI Agents - managed runtime, deployments, autoscaling, endpoints, conversations, memory, monitoring, and observability.
問題 #31
Which tasks is handled automatically by LangChain when using agent.invoke()?
答案：C
解題說明：
When a LangChain agent is invoked through agent.invoke() , the agent runtime abstracts the core mechanics required for model-mediated tool execution. LangChain tools expose structured inputs and outputs, and tool definitions provide the model with schemas derived from Python type information or explicitly supplied schemas. The framework then coordinates model responses containing tool calls, dispatches the corresponding tools, passes observations back to the model, and repeats the process until the agent reaches a termination condition.
This is a major distinction between directly binding tools to a standalone chat model and using a LangChain agent. LangChain documentation states that, with a model alone, the developer must execute returned tool calls and feed results back manually; when an agent is used, the agent loop handles that tool-execution loop .
Thus, the course's intended abstraction is captured by B: building/exposing tool schemas, processing model tool calls, and orchestrating iterative execution. GPU memory distribution and model training are infrastructure/model-development responsibilities, not functions of agent.invoke() .
The supplied question source explicitly marks B as the expected answer.
Study Guide reference/topic: LangChain for AI Agents - agent.invoke(), tool schemas, tool-call parsing, ToolNode execution, and iterative agent loops.
問題 #32
Which set lists built-in tool categories supported by OCI Enterprise AI Agents?
答案：A
解題說明：
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.
問題 #33
......
雖然有其他的線上Oracle的1z0-1157-26考試培訓資源在市場上，但我們VCESoft Oracle的1z0-1157-26考試培訓資料是最好的。因為我們會定期更新，始終提供準確的Oracle的1z0-1157-26考試認證資料，我們VCESoft Oracle的1z0-1157-26考試培訓資料提供一年的免費更新，你會得到最新的更新了的VCESoft Oracle的1z0-1157-26考試培訓資料。
1z0-1157-26考試資料: https://www.vcesoft.com/1z0-1157-26-pdf.html