MogiExamはIT認定試験に関連する資料の専門の提供者として、受験生の皆さんに最も優秀な試験1z0-1157-26参考書を提供することを目標としています。他のサイトと比較して、MogiExamは皆さんにもっと信頼されています。なぜでしょうか。それはMogiExamは長年の経験を持っていて、ずっとIT認定試験の研究に取り組んでいて、試験についての多くの規則を総括しましたから。そうすると、MogiExamの1z0-1157-26教材は高い的中率を持つことができます。これはまた試験の合格率を保証します。従って、MogiExamは皆の信頼を得ました。
| Section | Objectives |
|---|---|
| Topic 1: Introduction to MCP | - Model Context Protocol fundamentals
|
| Topic 2: Introduction to AI Agents | - Agent development concepts
|
| Topic 3: Agentic AI for Oracle AI Database | - Oracle AI Database agentic AI capabilities
|
| Topic 4: LangChain for AI Agents | - LangChain fundamentals
|
| Topic 5: OCI Enterprise AI Agents | - OCI Enterprise AI platform
|
| Topic 6: OpenAI Responses API and Agents SDK | - OpenAI agent development
|
MogiExamは、1z0-1157-26の実際のテストの品質を非常に重視しています。 すべての製品は厳格な検査プロセスを受けます。 さらに、さまざまな種類の1z0-1157-26学習資料間でランダムチェックが行われます。 1z0-1157-26学習教材の品質はあなたの信頼に値します。 試験を準備するための最も重要なことは、重要なポイントを確認することです。 優れた1z0-1157-26試験問題により、合格率は他の受験者よりもはるかに高くなっています。 1z0-1157-26のAgentic AI Foundations Associate試験の準備にはショートカットがあります。
質問 # 39
Which message format does MCP use for client-server communication?
正解：B
解説：
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.
質問 # 40
Compared with a standalone LLM call, an AI agent architecture commonly adds which capabilities?
正解：C
解説：
A standalone LLM request typically consists of supplying input and receiving model-generated output. An AI agent adds an orchestration layer that enables the model to participate in a broader execution loop. Oracle's Enterprise AI Agents architecture explicitly combines model interaction with tools, memory, conversation state, reasoning, and multi-step orchestration . Tools allow an agent to retrieve information or perform actions through File Search, Function Calling, Code Interpreter, or MCP Calling. Memory preserves relevant state within or across conversations, while iterative execution enables the agent to evaluate intermediate results and determine subsequent actions until the task is complete. These capabilities do not require changing the transformer's architecture, increasing its training speed, or providing native graphical-interface rendering.
Therefore, tool access, memory handling, and iterative execution are the defining additions described by option A. Oracle Docs
質問 # 41
Which MCP primitive is model-controlled and used to perform actions?
正解：A
解説：
In MCP, Tools are the primitive explicitly designed to be model-controlled. They represent executable functions that an MCP server exposes so that a language model can take actions, retrieve information, query databases, invoke APIs, modify files, or perform computations. The uploaded question set identifies Tools as the correct answer.
The official MCP specification defines three principal server primitives with different control models:
Prompts are user-controlled , Resources are application-controlled , and Tools are model-controlled . Tools can be discovered by the model-facing application and invoked automatically according to the model's contextual interpretation of the user's request.
Resources differ because they primarily provide contextual data such as file contents or database schemas.
Prompts provide reusable templates or instructions normally selected through user interaction. "Schemas" are not one of the three MCP primitives in this control hierarchy; schemas describe structures such as tool parameters rather than constituting a standalone primitive.
Therefore, A is correct.
Study Guide reference/topic: Model Context Protocol (MCP) Fundamentals - MCP primitives, Tools, Resources, Prompts, control hierarchy, and tool invocation.
質問 # 42
Why are docstrings especially important when defining LangChain tools with the @tool decorator?
正解：C
解説：
When LangChain's @tool decorator is applied to a Python function, the function's documentation becomes part of the metadata shown to the language model. LangChain explicitly states that, by default, the function's docstring becomes the tool description , helping the model understand when the capability should be selected.
This metadata is operationally significant because tool selection is model-driven. A clear description communicates the tool's purpose, appropriate usage conditions, expected arguments, and semantic boundaries.
LangChain's context-engineering guidance emphasizes that tool names, descriptions, argument names, and argument descriptions guide the model's reasoning about when and how a particular tool should be invoked.
Docstrings therefore affect agent reliability, not Python runtime performance. They do not determine which underlying model provider is used, and they have no cryptographic role in protecting function parameters.
Poor or ambiguous descriptions can cause an LLM to choose an inappropriate tool or supply unsuitable arguments even when the Python implementation itself is technically correct.
Consequently, D precisely captures why docstrings are particularly important for @tool -defined LangChain functions. The uploaded source confirms the same answer.
Study Guide reference/topic: LangChain for AI Agents - @tool decorator, tool descriptions, docstrings, schemas, tool selection, and context engineering.
質問 # 43
Why is chunking necessary before generating embeddings for large documents?
正解：A
解説：
Embedding models accept inputs only up to their supported input-size or token limits. Large documents can exceed those limits and therefore must be divided into smaller segments before embedding generation. The course source explicitly identifies overcoming token limits as the reason for chunking.
Oracle AI Vector Search includes native chunking functionality that can split text according to characters, words, or model vocabulary tokens. Oracle's Vector Search guidance specifically states that input length must remain within the token limits of the embedding model and provides configurable maximum chunk sizes.
Chunking also provides an important retrieval benefit. Instead of representing an entire long document with one coarse embedding, the system creates embeddings for semantically meaningful sections. A similarity query can then retrieve only the chunks most relevant to the user's question, reducing irrelevant context and improving retrieval-augmented generation precision.
Chunking does not intentionally remove semantic meaning; well-designed chunking attempts to preserve it. It does not automatically provide encryption, and SQL storage format is not its underlying purpose.
Therefore, A is correct.
Study Guide reference/topic: Agentic AI for Oracle AI Database - document chunking, embedding token limits, vector generation, semantic retrieval, and RAG.
質問 # 44
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