Fast2testはIT認定試験を受験した多くの人々を助けました。また、受験生からいろいろな良い評価を得ています。Fast2testの1z0-1157-26問題集の合格率が100%に達することも数え切れない受験生に証明された事実です。もし試験の準備をするために大変を感じているとしたら、ぜひFast2testの1z0-1157-26問題集を見逃さないでください。これは試験の準備をするために非常に効率的なツールですから。この問題集はあなたが少ない労力で最高の結果を取得することができます。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: LangChain for AI Agents | 5% | - LangChain fundamentals and agent construction
|
| Topic 2: OCI Enterprise AI Agents | 25% | - OCI Enterprise AI platform and agent services
|
| Topic 3: Introduction to AI Agents | 15% | - AI agent fundamentals
|
| Topic 4: Agentic AI for Oracle AI Database | 25% | - Oracle AI Database agentic AI capabilities
|
| Topic 5: OpenAI Responses API and Agents SDK | 15% | - OpenAI agent stack
|
| Topic 6: Model Context Protocol (MCP) Fundamentals | 15% | - MCP architecture and integration
|
市場で最高の1z0-1157-26テストトレントを提供する世界的なリーダーとして、Fast2testは、専門家によって何度もチェックされている1z0-1157-26試験問題の更新情報を提供することを約束し、消費者の大半が、統合サービスの構築に努めています。さらに、認定トレーニングアプリケーションだけでなく、インタラクティブな共有とアフターサービスでも画期的な成果を達成しました。 1z0-1157-26トレーニングブレインダンプを購入する価値があります。
質問 # 14
What is the architectural advantage of Autonomous AI Database MCP Server over a separately deployed third- party MCP server?
正解：C
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 # 15
Which description defines memory poisoning in AI-agent systems?
正解：A
解説：
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.
質問 # 16
Which SQL function computes distance between vectors in Oracle AI Vector Search?
正解：C
解説：
Oracle AI Vector Search uses the SQL function VECTOR_DISTANCE() as its principal mechanism for computing mathematical distance between two vector representations. The function accepts two vector expressions and can optionally accept a distance metric. Oracle describes VECTOR_DISTANCE as the main vector-distance function and supports metrics appropriate to similarity-search workloads, with cosine behavior available according to the query and vector-index configuration.
Vector distance is fundamental to semantic retrieval because an embedding model represents meaning as numerical coordinates in multidimensional space. A query embedding can therefore be compared with stored embeddings, and results can be ranked according to their calculated distance. Oracle's documentation demonstrates this pattern using ORDER BY VECTOR_DISTANCE(...) to identify vectors semantically closest to the query vector.
Oracle also provides shorthand functions such as L1_DISTANCE , L2_DISTANCE , COSINE_DISTANCE , and INNER_PRODUCT , but none of the alternative names supplied in this question- VECTOR_SCORE , SCORE_SIMILARITY , or EMBEDDING_DISTANCE -is the principal Oracle SQL function being tested.
Therefore, C is unequivocally correct and matches the uploaded source answer.
Study Guide reference/topic: Agentic AI for Oracle AI Database - Oracle AI Vector Search, VECTOR_DISTANCE, distance metrics, and similarity search.
質問 # 17
Which statement describes an LLM-based AI agent?
正解：C
解説：
An LLM-based AI agent is not simply a foundation model or chatbot user interface. It is a system in which an LLM acts as a reasoning and decision-making component while an orchestration layer gives it access to instructions, tools, state, external information, and potentially other agents. This architecture enables the system to decide which actions to perform and in what sequence to pursue a defined objective.
OpenAI describes an agent as an LLM equipped with instructions, tools, and handoffs, allowing it to plan, use tools to gather information or take actions, and delegate tasks when appropriate. Oracle similarly explains that AI agents use tools to communicate with external systems and dynamically determine which tools or integrations to use and in which order to achieve a goal.
An agent therefore does not require training an entirely new model architecture. The underlying LLM may be an existing pretrained model. What makes the system agentic is the combination of model reasoning with orchestration, tool execution, observations, state, and iterative decision-making.
Accordingly, D provides the correct architectural definition and matches the answer supplied in the uploaded source.
Study Guide reference/topic: Introduction to AI Agents - LLM-based agents, reasoning, tools, orchestration, actions, observations, and agent loops.
質問 # 18
What integration problem does MCP address?
正解：C
解説：
MCP addresses the integration fragmentation created when multiple AI applications must independently connect to multiple external tools, services, and data sources. The uploaded course material characterizes this explicitly as the N × M custom-connector problem : without a common interoperability layer, every application-to-tool pairing can require a separate integration.
The official MCP architecture supports this framing by defining a standardized client-server protocol. MCP hosts establish clients that communicate with MCP servers, while servers expose reusable capabilities such as tools, resources, and prompts. A compatible AI application therefore consumes capabilities through the MCP protocol rather than requiring a completely bespoke protocol implementation for each downstream system.
MCP's tool-discovery mechanism further allows clients to obtain standardized names, descriptions, and schemas dynamically.
The problem is architectural interoperability, not GPU allocation, inference latency, or context-window limitations. Those issues require separate model, infrastructure, or prompt-management techniques. MCP instead standardizes the boundary between AI applications and external capabilities, reducing duplicated connector logic and enabling reusable integrations.
Study Guide reference/topic: Model Context Protocol (MCP) Fundamentals - interoperability, MCP client
/server architecture, tool discovery, and the N × M integration problem.
質問 # 19
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