1z0-1157-26の学習質問は、文化レベルの種類に関係なく、さまざまなレベルのユーザーに適しています。たとえ文化レベルが高くても、1z0-1157-26トレーニング資料で自分に合ったものを見つけることができます。学習方法。それで、1z0-1157-26学習教材のすべてのユーザーにとって、絶好の機会であり、さまざまなタイプから選択できます。また、ますます多くの学生が1z0-1157-26テストガイドを選択します。 Agentic AI Foundations Associateの学習質問を選択してください！
| Section | Objectives |
|---|---|
| Topic 1: Oracle AI Database for Agentic AI | - Oracle AI Vector Search - Agentic AI capabilities in Oracle AI Database |
| Topic 2: Agent Fundamentals and Reasoning Patterns | - Agent reasoning patterns and workflows - AI agent core concepts and architectures |
| Topic 3: OCI Enterprise AI Platform | - OCI Enterprise AI Agents and Knowledge Bases - OCI Enterprise AI services overview |
| Topic 4: Implementing Model Context Protocol (MCP) | - MCP fundamentals and integration |
| Topic 5: Enterprise Agent Development and Governance | - Guardrails, agent tracing and monitoring - Multi-agent systems and handoffs - Function calling and tool integration |
| Topic 6: Building Agents with LangChain and OpenAI Agent Stack | - LangChain components and chains - OpenAI Agents SDK usage |
PassTestのOracleの1z0-1157-26問題集の内容の正確性に対して、私たちはベストな水準に達するのを追求します。PassTestが提供した問題と解答はIT領域のエリートたちが研究して、実践して開発されたものです。それは十年過ぎのIT認証経験を持っています。PassTestは他のネットサイトより早い速度で、君が簡単にOracleの1z0-1157-26試験に合格することを保証します。
質問 # 29
Which OCI capability is required for serving fine-tuned or imported custom models?
正解：B
解説：
OCI Generative AI uses Dedicated AI Clusters to provide the isolated compute infrastructure required for fine- tuning and hosting custom model workloads. Oracle defines Dedicated AI Clusters as compute resources dedicated to a customer's models rather than shared with other tenancies. They can be created specifically for fine-tuning or for hosting model endpoints.
Oracle's current model onboarding workflow confirms the requirement. For imported models, the process includes importing the model, creating a hosting Dedicated AI Cluster , creating an endpoint, and then invoking the model. Fine-tuned models similarly require dedicated clusters for fine-tuning and subsequent hosting.
Shared On-Demand inference is appropriate for supported Oracle-hosted pretrained models, but it does not provide the dedicated isolated serving environment required by these custom model workflows. Object Storage can be an input location for model artifacts or training data, but it is storage rather than model-serving infrastructure. General-purpose Free Tier compute is likewise not the managed Generative AI capability Oracle specifies for custom-model serving.
Thus, B is correct and agrees with the uploaded course material.
Study Guide reference/topic: OCI Enterprise AI Agents - Dedicated AI Clusters, imported models, fine- tuned custom models, hosting clusters, and endpoints.
質問 # 30
Which native column type does Oracle AI Database use for storing vector embeddings?
正解：C
解説：
Oracle AI Database provides a native VECTOR data type specifically for storing vector embeddings. The uploaded course source identifies VECTOR as the correct native column type.
Oracle's official AI Vector Search documentation states that the built-in VECTOR data type provides the foundation for storing embeddings directly alongside relational business data. A table can therefore define a vector column in the same way it defines conventional Oracle columns, for example doc_vector VECTOR .
This native representation is important because Oracle AI Database can apply vector-specific SQL operations and vector indexes directly to stored embeddings. Applications can combine similarity search with relational predicates, JSON processing, graph operations, spatial queries, and standard SQL without moving embeddings into a separate specialized vector database.
Although Oracle may internally use storage mechanisms such as SecureFiles for vector representation, BLOB is not the logical SQL column type developers use for AI Vector Search embeddings . JSON and VARCHAR2 are likewise general-purpose data types and do not provide native vector semantics.
Therefore, A is correct.
Study Guide reference/topic: Agentic AI for Oracle AI Database - VECTOR data type, vector columns, embedding storage, vector indexes, and AI Vector Search.
質問 # 31
What is the architectural advantage of Autonomous AI Database MCP Server over a separately deployed third- party MCP server?
正解：A
質問 # 32
Which OCI services are used for observability and auditing of deployed AI agents?
正解：B
解説：
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
質問 # 33
What does the @function_tool decorator do in the OpenAI Agents SDK?
正解：C
解説：
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
質問 # 34
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1z0-1157-26資料勉強: https://www.passtest.jp/Oracle/1z0-1157-26-shiken.html