擁有 Oracle 認證可以證明考生能夠勝任這個職位。往往能力強的考生嘆息道：“如果可以擁有本證書，這個職位鐵定是我的。”那為什麼不儘早讓考試順利過關了。越早擁有 Oracle 認證，可以比別人多一份選擇理想工作的。但是如何能順利過關完成Oracle 認證成了技術人員最頭疼的問題。如果你需要幫助，KaoGuTi 能幫助每個IT人士，因為它的 1z0-1157-26 測試題庫和 1z0-1157-26 學習指南可以幫助你通過真正的考試。
| Section | Objectives |
|---|---|
| Oracle AI Database for Agentic AI | - Agentic AI capabilities in Oracle AI Database - Oracle AI Vector Search |
| OCI Enterprise AI Platform | - OCI Enterprise AI services overview - OCI Enterprise AI Agents and Knowledge Bases |
| Implementing Model Context Protocol (MCP) | - MCP fundamentals and integration |
| Enterprise Agent Development and Governance | - Guardrails, agent tracing and monitoring - Function calling and tool integration - Multi-agent systems and handoffs |
| Building Agents with LangChain and OpenAI Agent Stack | - LangChain components and chains - OpenAI Agents SDK usage |
| Agent Fundamentals and Reasoning Patterns | - Agent reasoning patterns and workflows - AI agent core concepts and architectures |
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問題 #18
Which statement describes the purpose of the OpenAI Responses API?
答案：D
解題說明：
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.
問題 #19
What is the architectural advantage of Autonomous AI Database MCP Server over a separately deployed third- party MCP server?
答案：C
問題 #20
How do On-Demand serving and Dedicated AI Clusters differ for OCI Enterprise AI Models?
答案：D
解題說明：
OCI Generative AI provides both On-Demand and Dedicated serving modes. The fundamental architectural distinction is resource isolation. On-Demand model inference uses Oracle-managed shared infrastructure, making it suitable for straightforward consumption of supported pretrained models without reserving dedicated hardware. Dedicated mode assigns dedicated GPU resources to the customer's workload and provides predictable, isolated serving capacity.
Oracle states that Dedicated AI Clusters are compute resources dedicated to a customer's models and are not shared with other tenancies. They are used for hosting supported pretrained models and, critically, for fine- tuning and hosting custom or imported models. The course question correspondingly identifies B as correct.
Option A ignores the infrastructure-isolation difference. Option C is technically impossible because all model inference requires compute resources; On-Demand simply abstracts shared infrastructure. Option D is also incorrect because On-Demand is not limited to embedding models-OCI provides multiple supported model categories through managed inference.
Therefore, B accurately captures both shared-versus-dedicated serving and the importance of Dedicated AI Clusters for custom-model workloads.
Study Guide reference/topic: OCI Enterprise AI Agents - Enterprise AI Models, On-Demand serving, Dedicated AI Clusters, custom models, and GPU isolation.
問題 #21
In the given LangChain chain, what is the role of StrOutputParser()? chain = prompt
答案：D
解題說明：
StrOutputParser is a LangChain output parser used to convert a language-model response into a standard Python string. In an LCEL pipeline such as prompt | model | StrOutputParser() , the prompt prepares the model input, the model generates an AIMessage or equivalent model output, and StrOutputParser extracts the textual content so downstream application code receives plain text.
LangChain's official documentation demonstrates this exact pattern by composing a prompt, chat model, and StrOutputParser() into a chain and then invoking the resulting runnable. The parser therefore operates after model inference ; it does not send the request to the model and does not maintain agent or tool history.
The uploaded source presents the chain fragment across separate lines and explicitly identifies "It extracts plain text from the model response" as the correct response.
This output-parsing stage is especially useful because it isolates application code from provider-specific response-object structures and provides a predictable string output from a LangChain runnable.
Study Guide reference/topic: LangChain for AI Agents - LCEL chains, Runnable composition, model output handling, and StrOutputParser.
問題 #22
Which four behaviors does every Select AI Agent perform?
答案：B
解題說明：
Oracle Select AI Agent is architected around four foundational behaviors: Planning, Tool Use, Reflection, and Memory Management . Oracle documentation describes these as the framework's principal layers. Planning interprets the user's objective, decomposes it into ordered actions, and identifies appropriate capabilities. Tool Use invokes mechanisms such as NL2SQL, RAG, PL/SQL procedures, or external REST services. Reflection evaluates observations returned by those tools and determines whether the current plan should continue, be revised, or use another capability. Memory preserves context and useful information, supporting coherent multi-turn interactions and longer-term continuity.
Oracle explicitly states that Select AI Agent combines planning, tool use, reflection, and memory and implements a ReAct-style agentic pattern in which the agent reasons, acts through tools, evaluates observations, and continues toward the goal.
The alternative answer sets describe generic information-retrieval or operational lifecycle stages but do not correspond to Oracle's defined Select AI Agent architecture. Consequently, B reproduces the four documented agent behaviors and is the correct answer in the supplied question set.
Study Guide reference/topic: Agentic AI for Oracle AI Database - Select AI Agent architecture, Planning, Tool Use, Reflection, Memory, and ReAct.
問題 #23
......
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