Oracle인증 1z0-1157-26시험을 한방에 편하게 통과하여 자격증을 취득하려면 시험전 공부가이드가 필수입니다. ExamPassdump에서 연구제작한 Oracle인증 1z0-1157-26덤프는Oracle인증 1z0-1157-26시험을 패스하는데 가장 좋은 시험준비 공부자료입니다. ExamPassdump덤프공부자료는 엘리트한 IT전문자들이 자신의 노하우와 경험으로 최선을 다해 연구제작한 결과물입니다.IT인증자격증을 취득하려는 분들의 곁은ExamPassdump가 지켜드립니다.
| Section | Objectives |
|---|---|
| Topic 1: Building Agents with LangChain and OpenAI Agent Stack | - OpenAI Agents SDK usage - LangChain components and chains |
| Topic 2: Oracle AI Database for Agentic AI | - Agentic AI capabilities in Oracle AI Database - Oracle AI Vector Search |
| Topic 3: Enterprise Agent Development and Governance | - Function calling and tool integration - Multi-agent systems and handoffs - Guardrails, agent tracing and monitoring |
| Topic 4: Agent Fundamentals and Reasoning Patterns | - Agent reasoning patterns and workflows - AI agent core concepts and architectures |
| Topic 5: OCI Enterprise AI Platform | - OCI Enterprise AI services overview - OCI Enterprise AI Agents and Knowledge Bases |
| Topic 6: Implementing Model Context Protocol (MCP) | - MCP fundamentals and integration |
ExamPassdump의 Oracle인증 1z0-1157-26덤프를 공부하여Oracle인증 1z0-1157-26시험을 패스하는건 아주 간단한 일입니다.저희 사이트에서 제작한Oracle인증 1z0-1157-26덤프공부가이드는 실제시험의 모든 유형과 범위가 커버되어있어 높은 적중율을 자랑합니다.시험에서 불합격시 덤프비용은 환불신청 가능하기에 안심하고 시험준비하시면 됩니다.
질문 # 31
Which description defines memory poisoning in AI-agent systems?
정답：B
설명：
Memory poisoning is an agent-security attack in which malicious, misleading, or attacker-controlled information is introduced into memory that the agent may reuse in future reasoning or actions. The uploaded course source defines it as malicious content inserted into persistent memory stores and identifies C as correct.
Oracle's current AI Agent Memory security guidance explains why persistent memory must be treated as a security-sensitive surface. Model-derived memories, summaries, context cards, metadata, and retrieved records can become persistent state and later be inserted into prompts. Oracle therefore advises treating memory-derived content as untrusted and emphasizes that write-capable memory paths can influence future prompts and retrieval results.
The broader agent-security definition is also explicit in OWASP's Agentic AI guidance: memory poisoning involves malicious data being persisted in agent memory so that it can influence future sessions or behaviors.
This differs from temporary context-window pressure, SQL injection, or physical RAM corruption. The essential security property is persistence : compromised memory can affect later reasoning long after the original malicious interaction.
Therefore, C is correct.
Study Guide reference/topic: Introduction to AI Agents - agent memory, persistent state, memory poisoning, prompt injection persistence, and agent security.
질문 # 32
What is Oracle AI Database Private Agent Factory?
정답：D
설명：
Oracle AI Database Private Agent Factory is a no-code platform for building, testing, and deploying intelligent AI agents . The uploaded question set marks D as correct, and current Oracle documentation independently confirms that definition.
Oracle describes Private Agent Factory as a platform intended for both business users and engineers. It provides an Agent Builder with visual and drag-and-drop capabilities, enabling users to construct intelligent assistants and workflows without writing conventional application code. The platform can combine pre-built agents, custom agents, reusable templates, enterprise data, LLMs, APIs, databases, and external tools.
The strategic purpose is to lower the engineering barrier for enterprise agent creation while retaining governance and integration with Oracle AI Database capabilities. Current releases include pre-built agents and workflow automation functionality for rapidly creating business-oriented agentic solutions.
It is not an embedding backup product, dedicated Kubernetes deployment manager, or physical training appliance. Those alternatives describe unrelated infrastructure or administration capabilities.
Therefore, D is directly supported by Oracle documentation.
Study Guide reference/topic: Agentic AI for Oracle AI Database - Private Agent Factory, no-code Agent Builder, pre-built agents, custom agents, workflows, and enterprise integration.
질문 # 33
Which OCI services are used for observability and auditing of deployed AI agents?
정답：B
설명：
OCI production AI architectures use the standard OCI observability and governance services to provide operational visibility and accountability. OCI Logging collects and centralizes service and application logs; OCI Generative AI hosted applications can expose deployment logs that open directly in OCI Logging and the Observability and Management service. OCI Monitoring supplies metrics and alarms for monitoring resource health and operational conditions. OCI Audit records calls made to supported OCI public API endpoints, providing an authoritative record of administrative and resource-management actions for investigation and compliance. Oracle's architecture guidance specifically recommends enabling OCI Logging, OCI Monitoring, and OCI Audit logs for critical AI-platform components. The services in the other options have legitimate OCI purposes, but they do not collectively represent the principal observability-and-auditing stack. Therefore, option A is the verified combination. Oracle Docs
질문 # 34
What does the @function_tool decorator do in the OpenAI Agents SDK?
정답：A
설명：
The @function_tool decorator converts an ordinary Python function into a FunctionTool that can be exposed to an agent for model-directed invocation. The Agents SDK automatically derives important tool metadata: by default, the Python function name becomes the tool name, its docstring supplies the tool description, and the function signature is converted into a JSON schema describing the expected arguments. This structured representation allows the language model to determine when the function is relevant and generate valid arguments for it. The decorator does not inherently expose the function as a REST endpoint, persist return values to disk, or guarantee automatic retries whenever execution fails. This mechanism corresponds closely to OCI Enterprise AI Agents' Function Calling model, where application-controlled functions extend an agent beyond pure model generation and enable controlled interaction with external business logic. OpenAI GitHub
질문 # 35
When is MCP most valuable?
정답：D
설명：
MCP delivers its greatest architectural value when an AI application must interact with capabilities that exist outside the application's own process , particularly external services, databases, SaaS platforms, APIs, or cloud-hosted tools. Instead of implementing a proprietary integration contract for every agent/tool combination, an MCP server exposes those capabilities using a standardized protocol.
The MCP specification describes the protocol as a standardized mechanism for integrating LLM applications with external data sources and tools . Its tools specification further states that MCP servers can expose capabilities that query databases, call APIs, perform computations, or otherwise interact with external systems.
For trivial local functions such as add() or multiply() , a normal in-process function-tool definition is usually simpler because no separate server protocol is needed. Similarly, an application that has no external dependencies gains relatively little from introducing MCP solely for prompts or local output parsers.
Thus the decisive use case is integration across system or deployment boundaries, especially where capabilities should be reusable by multiple MCP-compatible clients.
Therefore, D is the correct answer and is also explicitly marked correct in the uploaded source.
Study Guide reference/topic: Model Context Protocol (MCP) Fundamentals - external tools, MCP servers, interoperability, reusable integrations, and remote services.
질문 # 36
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