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Oracle 1z0-1157-26 Exam Syllabus Topics:

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

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Oracle 1z0-1157-26試験解答、1z0-1157-26合格受験記

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Oracle Agentic AI Foundations Associate 認定 1z0-1157-26 試験問題 (Q28-Q33):

質問 # 28
Why is chunking necessary before generating embeddings for large documents?

A. To convert text into SQL-compliant rows.
B. To overcome token limits for large documents.
C. To automatically encrypt content before indexing.
D. To remove semantic meaning from the document.

正解：B

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 # 29
Which authentication approach should be used for production-grade access to OCI Enterprise AI services?

A. Browser session cookies stored with application code
B. OCI IAM authentication with signed requests and IAM policies
C. Hardcoded credentials checked into source repositories
D. Anonymous access to tenancy-level resources

正解：B

解説：
For production OCI Enterprise AI workloads, Oracle recommends OCI IAM-based authentication rather than long-lived development credentials. The uploaded source identifies OCI IAM authentication with signed requests and IAM policies as the correct production architecture.
Oracle's OCI Responses API authentication documentation distinguishes service API keys used for testing and early development from IAM authentication intended for production and OCI-managed environments.
IAM-based authentication uses OCI identity principals and request-signing mechanisms and allows authorization to be controlled through centralized IAM policies. Oracle specifically recommends IAM when applications execute in services such as OCI Functions or Oracle Kubernetes Engine, when long-lived API keys should be avoided, or when fine-grained centralized access control is required.
IAM policies also implement least privilege by defining exactly which users, groups, resource principals, or workloads can access individual Generative AI resource types.
Browser cookies, anonymous tenancy access, and credentials committed into source repositories violate standard enterprise security practices and significantly increase credential-exposure risk.
Therefore, A is the only production-grade authentication approach among the choices.
Study Guide reference/topic: OCI Enterprise AI Agents - OCI IAM, signed requests, policies, principals, least privilege, and production authentication.

質問 # 30
What is the architectural advantage of Autonomous AI Database MCP Server over a separately deployed third- party MCP server?

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

正解：A

解説：
Oracle Autonomous AI Database includes a managed MCP Server that is natively integrated with the database rather than requiring customers to deploy and operate a separate MCP infrastructure tier. Oracle states that the service eliminates the need to manage customer-side MCP server infrastructure, directly reducing deployment and operational overhead. It also integrates with database identity, authorization, governance, auditing, network controls, database roles, Virtual Private Database policies, ACLs, and private endpoints.
Architecturally, this is significant because MCP-exposed Select AI Agent tools remain close to the database security boundary. The managed multi-tenant MCP layer can expose approved tools while relying on established database governance controls. Oracle's architecture describes a Unified Security Layer, managed MCP Server, and Select AI Agent Framework working together as an integrated stack.
The capability does not remove SQL, move database execution outside the database, or require a universal MCP host. Instead, its primary advantage is minimizing additional infrastructure while preserving strong database-native control over access and operations. Thus C is technically correct and matches the uploaded source.
Study Guide reference/topic: Agentic AI for Oracle AI Database - Autonomous AI Database MCP Server, native security integration, governance, and managed MCP infrastructure.

質問 # 31
Which approaches are supported by OCI Enterprise AI Agents?

A. Call the Responses API directly or deploy a hosted agent application.
B. Select a free or paid deployment model.
C. Deploy using Python-only or Java-only options.
D. Build a frontend agent or a backend agent.

正解：A

解説：
OCI Generative AI defines two principal approaches for developing enterprise-grade agentic applications.
The first is to build agents using the OCI Responses API , an API-first model that allows developers to control agent interactions through an OpenAI-compatible interface. The second is to deploy hosted agentic applications using OCI Generative AI Applications and Deployments, where OCI manages substantial portions of the application runtime infrastructure. Oracle explicitly documents these as the two main Enterprise AI Agent approaches and notes that they can also be combined in hybrid architectures.
The Responses API approach is appropriate when developers want direct programmatic control over models, tools, context, and agent behavior without independently managing inference infrastructure. Hosted agent applications are appropriate when custom agent runtimes need managed container deployment, networking, identity, storage integration, scaling, and production lifecycle support. OCI's broader Generative AI architecture positions these mechanisms within its Enterprise AI Agents layer.
The supported architecture is therefore not divided according to Python versus Java, pricing categories, or frontend versus backend classification. Option A reproduces Oracle's documented deployment choices precisely and matches the supplied examination source.
Study Guide reference/topic: OCI Enterprise AI Agents - OCI Responses API, Applications, Deployments, hosted agentic applications, and hybrid architectures.

質問 # 32
Which approaches are supported by OCI Enterprise AI Agents?

A. Call the Responses API directly or deploy a hosted agent application.
B. Select a free or paid deployment model.
C. Deploy using Python-only or Java-only options.
D. Build a frontend agent or a backend agent.

正解：A

質問 # 33
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