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Oracle 1z0-1157-26 Exam Syllabus Topics:

Section	Objectives
Topic 1: Introduction to AI Agents	- Agent development concepts 1. Multi-agent design patterns and handoffs 2. Function calling and tool use 3. OpenAI Agents SDK guardrails - AI agent fundamentals and architecture 1. AI agents, traditional chatbots, and rule-based systems 2. Safety, guardrails, and responsible agentic workflows 3. Agent reasoning patterns including Chain-of-Thought and ReAct 4. Core agent components: LLMs, tools, and orchestration loops
Topic 2: OCI Enterprise AI Agents	- OCI Enterprise AI platform 1. OCI Enterprise AI Agents service 2. Deployment and scaling 3. Responses API, tools, memory, and vector stores 4. Agent development, orchestration, and execution 5. Building and running AI agents
Topic 3: Introduction to MCP	- Model Context Protocol fundamentals 1. MCP concepts and architecture 2. MCP clients and servers 3. Tool discovery and interoperability
Topic 4: Agentic AI for Oracle AI Database	- Oracle AI Database agentic AI capabilities 1. Oracle AI Vector Search 2. Grounding agent responses with enterprise data 3. Oracle Autonomous AI Database MCP Server 4. Document chunking, embedding generation, and retrieval 5. Select AI 6. Oracle AI Database Private Agent Factory 7. Vector data types, embeddings, and similarity search
Topic 5: OpenAI Responses API and Agents SDK	- OpenAI agent development 1. Multi-agent handoffs 2. Guardrails and tracing 3. OpenAI Agents SDK 4. Function calling and tools 5. Responses API
Topic 6: LangChain for AI Agents	- LangChain fundamentals 1. Tools, tool schemas, and tool execution 2. LangChain and LangChain Expression Language 3. Building agents with LangChain 4. Agent invocation and orchestration flow

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최신 Oracle Certification 1z0-1157-26 무료샘플문제 (Q32-Q37):

질문 # 32
According to the MCP architecture model, which statement describes the client-server relationship?

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

정답：B

설명：
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.

질문 # 33
Which tasks is handled automatically by LangChain when using agent.invoke()?

A. Managing conversation state, formatting API requests, and routing tool execution across the agent loop.
B. Training the foundation model from scratch.
C. Building tool schemas, parsing tool calls, and orchestrating execution loops.
D. Optimizing GPU memory allocation and distributing model inference across hardware accelerators.,,

정답：C

설명：
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.

질문 # 34
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. Replacing the database with a vector-only system
D. Removing SQL functionality in favor of REST APIs

정답：A

설명：
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.

질문 # 35
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 reduces operational overhead and enforces security inside the database.
C. It uses a common MCP host for all database connections.
D. It moves all query execution to a separate compute tier outside the database.

정답：B

질문 # 36
Which approaches can generate embeddings for Oracle AI Vector Search workflows?

A. Export data to a Python environment, generate embeddings, then reimport via SQL Loader
B. Use external REST APIs exclusively, since in-database embedding is not supported
C. Use ONNX models in-database or external REST-based embedding providers
D. Train a custom embedding model and manually insert vectors row by row

정답：C

설명：
Oracle AI Vector Search supports both in-database embedding generation and external embedding providers , making A the correct answer. The supplied question source identifies the same combination.
For in-database processing, Oracle AI Database includes an ONNX runtime. Compatible ONNX embedding models can be imported as database objects and invoked directly through SQL using functions such as VECTOR_EMBEDDING . This allows vectorization to occur without moving source data outside the database.
Oracle also supports REST-based embedding generation. DBMS_VECTOR.UTL_TO_EMBEDDING and related APIs can call external or local providers. Officially documented providers include OCI Generative AI, Cohere, OpenAI, Google AI, Hugging Face, Vertex AI, Mistral, Ollama, and Private AI, depending on the operation and configuration.
Manual Python export/import workflows are technically possible in custom architectures, but they are not the supported approaches being tested. Option C is explicitly false because Oracle supports in-database ONNX inference.
Therefore, A correctly captures Oracle's native and external embedding-generation strategies.
Study Guide reference/topic: Agentic AI for Oracle AI Database - ONNX Runtime, VECTOR_EMBEDDING, DBMS_VECTOR, REST embedding providers, and AI Vector Search.

질문 # 37
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