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Oracle 1z0-1157-26 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Introduction to AI Agents	15%	- AI agent fundamentals 1. Differentiate AI agents from traditional chatbots and rule-based workflows 2. Core components of an AI agent: LLM, tools, and orchestration loop 3. Agent reasoning patterns: Chain-of-Thought and ReAct 4. Safety considerations and guardrail techniques
Topic 2: LangChain for AI Agents	5%	- LangChain fundamentals and agent construction 1. LangChain agent reasoning and tool execution flow 2. LangChain core abstractions: chat models, prompts, tools, and agents 3. LangChain tools, prompts, and chains
Topic 3: Agentic AI for Oracle AI Database	25%	- Oracle AI Database agentic AI capabilities 1. Oracle AI Vector Search workflow: document chunking, embedding generation, similarity search, and retrieval 2. Oracle AI Database Private Agent Factory 3. Grounding agent responses with enterprise data from Oracle AI Database 4. Select AI for natural-language interaction with Oracle AI Database 5. VECTOR data type, vector embeddings, and similarity search 6. Oracle AI Vector Search, Select AI, and MCP integration 7. Oracle Autonomous AI Database MCP Server
Topic 4: Model Context Protocol (MCP) Fundamentals	15%	- MCP architecture and integration 1. JSON-RPC 2.0 message format 2. MCP hosts, clients, servers, tools, resources, and prompts 3. Integrating MCP capabilities into agentic AI workflows 4. Role of MCP in standardizing integration between AI agents and external tools 5. MCP transport options including stdio and Streamable HTTP
Topic 5: OpenAI Responses API and Agents SDK	15%	- OpenAI agent stack 1. Function calling and tools 2. Multi-agent design patterns and handoffs 3. Agents SDK primitives: Agent, Runner, Tool, Handoffs, and Guardrails 4. Guardrails for validating inputs, outputs, and agent actions 5. OpenAI Responses API for agentic applications
Topic 6: OCI Enterprise AI Agents	25%	- OCI Enterprise AI platform and agent services 1. Building and running AI agents with OCI Enterprise AI Agents 2. OCI Enterprise AI Agents development, orchestration, and execution 3. OCI Enterprise AI platform services for the enterprise AI agent lifecycle 4. Deployment and scaling options 5. OCI Enterprise AI Agents building blocks: Responses API, tools, memory, and vector stores

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Oracle Agentic AI Foundations Associate 認定 1z0-1157-26 試験問題 (Q37-Q42):

質問 # 37
According to the MCP architecture model, which statement describes the client-server relationship?

A. An MCP client maintains a dedicated connection to an MCP server.
B. An MCP server is used by one client during its lifetime.
C. A single MCP client communicates with multiple servers through one shared MCP connection.
D. MCP clients and servers run on separate machines.

正解：A

解説：
MCP follows a host-client-server architecture in which the host creates an MCP client for each MCP server it connects to. The official architecture specifies that each MCP client maintains a dedicated connection with its corresponding MCP server . A host connecting to several servers therefore normally manages several MCP client instances rather than multiplexing all servers through one shared client connection. MCP also does not require clients and servers to reside on different machines. STDIO-based servers commonly execute locally, while Streamable HTTP supports remotely deployed servers. Likewise, a remote MCP server can serve many MCP clients, so a server is not permanently restricted to one client. This client-server separation is particularly relevant to OCI Enterprise AI Agents because MCP Calling enables OCI agent workflows to consume capabilities exposed by remote MCP servers through a standardized integration model.

質問 # 38
What is the architectural advantage of Autonomous AI Database MCP Server over a separately deployed third- party MCP server?

A. It eliminates the need for SQL queries entirely.
B. It uses a common MCP host for all database connections.
C. It moves all query execution to a separate compute tier outside the database.
D. It reduces operational overhead and enforces security inside the database.

正解：D

質問 # 39
What is the strategic theme behind agentic AI capabilities in Oracle AI Database?

A. Bringing AI capabilities natively into the database.
B. Selling vector products without database integration
C. Removing SQL functionality in favor of REST APIs
D. Replacing the database with a vector-only system

正解：A

解説：
Oracle's strategic direction is to integrate AI capabilities directly into Oracle AI Database , allowing conventional relational data, vector embeddings, semantic retrieval, natural-language interfaces, and autonomous agent functionality to operate within the database platform rather than requiring a separate AI- only data tier.
Oracle AI Vector Search illustrates this strategy. The database provides a native VECTOR data type, vector- distance functions, vector indexes, and semantic similarity search alongside traditional relational data and SQL operations. This allows embeddings and enterprise business records to remain together under existing transactional, security, and governance controls.
Select AI reinforces the same architecture. Oracle documents that Select AI runs natively inside Autonomous AI Database and Oracle AI Database, while Select AI Agent provides autonomous reasoning, tools, reflection, memory, RAG, NL2SQL, PL/SQL integration, and REST interactions within the database-oriented agent framework.
Oracle is therefore extending SQL and database functionality rather than eliminating it. Nor is Oracle replacing the relational database with a vector-only system. The strategic objective is convergence: enterprise data plus native AI capabilities in one governed database environment. Option B is therefore correct.
Study Guide reference/topic: Agentic AI for Oracle AI Database - native AI integration, Select AI, Select AI Agent, AI Vector Search, and converged data architecture.

質問 # 40
In JSON-RPC 2.0, what is the difference between a request and a notification?

A. A request includes an ID and expects a reply, while a notification does not.
B. A request can carry structured data, while a notification can carry only text.
C. A request uses HTTP, while a notification uses a different transport protocol.
D. A request is sent unencrypted, while a notification is encrypted before processing.

正解：A

解説：
The defining distinction is the presence of a request identifier and the expectation of a corresponding response. In JSON-RPC 2.0, a normal request contains an id value so that the sender can correlate the response with the request. A notification deliberately omits the ID because no response is expected. The supplied course material identifies exactly this distinction.
MCP uses JSON-RPC 2.0 as its underlying messaging protocol. Its architecture documentation explicitly states that clients and servers exchange requests and responses, while notifications are used where no response is required. MCP's notification examples contain no id field, and the documentation explains that this follows JSON-RPC notification semantics.
The difference has nothing to do with whether data is structured, whether encryption is enabled, or which transport is used. Both requests and notifications can carry structured JSON parameters. Security belongs to the transport/authentication layer, while MCP can transmit JSON-RPC messages over supported transports such as STDIO or Streamable HTTP.
Therefore, B is the precise protocol-level distinction.
Study Guide reference/topic: Model Context Protocol (MCP) Fundamentals - JSON-RPC 2.0 requests, responses, IDs, and notifications.

質問 # 41
How do On-Demand serving and Dedicated AI Clusters differ for OCI Enterprise AI Models?

A. On-Demand supports only embedding models while Dedicated AI Clusters support generative models.
B. On-Demand uses shared infrastructure while Dedicated AI Clusters provide isolated GPUs required for custom models.
C. On-Demand doesn't use processors while Dedicated AI Clusters use isolated GPUs for all workloads.
D. Both options use identical infrastructure but are differentiated by pricing and branding only.

正解：B

解説：
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.

質問 # 42
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