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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: LangChain for AI Agents | 5% | - LangChain fundamentals and agent construction
|
| Topic 2: Agentic AI for Oracle AI Database | 25% | - Oracle AI Database agentic AI capabilities
|
| Topic 3: OpenAI Responses API and Agents SDK | 15% | - OpenAI agent stack
|
| Topic 4: Model Context Protocol (MCP) Fundamentals | 15% | - MCP architecture and integration
|
| Topic 5: OCI Enterprise AI Agents | 25% | - OCI Enterprise AI platform and agent services
|
| Topic 6: Introduction to AI Agents | 15% | - AI agent fundamentals
|
>> 1z0-1157-26 Quizfragen Und Antworten <<
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10. Frage
Agent has multiply(a,b) and divide(a,b) . User: "What is 15 multiplied by 8, then divided by 3?" How does the OpenAI Agents SDK handle this?
Antwort: D
Begründung:
The OpenAI Agents SDK uses an iterative agent loop for multi-step tool execution. In this scenario, the model first determines that it needs the multiply tool and generates a call with the arguments 15 and 8 . The application executes that tool and returns 120 as a tool result. The model receives the updated conversation state, recognizes that another operation remains, and subsequently requests divide(120, 3) . The resulting value is then available for the final response. The uploaded course source explicitly specifies this sequence.
OpenAI's Agents SDK documentation confirms that the Runner repeatedly calls the LLM, executes requested tools, appends their results, and runs the model again until final output is produced.
The Runner does not independently decide to calculate the arithmetic itself. Nor does the SDK automatically merge unrelated function calls into one synthetic operation. Likewise, an agent does not invoke every registered tool indiscriminately; the model selects the tools required by the current task.
Therefore, D accurately describes the sequential model/tool interaction.
Study Guide reference/topic: OpenAI Responses API and Agents SDK - agent loop, sequential tool calls, tool results, Runner orchestration, and multi-step execution.
11. Frage
What is an embedding in a semantic search workflow?
Antwort: B
Begründung:
An embedding is a numerical vector representation of data created by an embedding model, normally implemented using a neural network. Its purpose is to encode semantic characteristics so that items with related meanings are positioned near each other in a multidimensional vector space. Instead of matching only literal keywords, a semantic-search system converts documents and queries into vectors and compares their relative distances or similarities.
Oracle AI Vector Search documentation explains that vector embeddings are mathematical representations describing semantic meaning for content such as text, documents, images, or audio. Oracle further states that modern embeddings are created through neural networks, commonly transformer-based models, although other neural architectures can also be used. This allows Oracle AI Database to store those embeddings using its VECTOR data type and perform similarity searches against them.
A trigger is procedural database logic, a SQL JOIN combines relational data, and a compressed video format is unrelated to semantic representation. Consequently, B is the only technically valid definition. The uploaded question set confirms the same answer.
Study Guide reference/topic: Agentic AI for Oracle AI Database - Oracle AI Vector Search, vector embeddings, semantic similarity, and neural embedding models.
12. Frage
Which set lists built-in tool categories supported by OCI Enterprise AI Agents?
Antwort: C
Begründung:
OCI Enterprise AI Agents supports a defined set of OpenAI-compatible agent tools through the OCI Responses API. Oracle's current documentation identifies File Search, Code Interpreter, Function Calling, and MCP Calling as supported tool categories.
File Search allows an agent to retrieve relevant information from indexed content and vector stores. Code Interpreter provides a controlled environment for computational or programmatic analysis. Function Calling lets the model request execution of application-defined functions with structured parameters. MCP Calling enables the agent to discover and invoke capabilities made available by remote Model Context Protocol servers. Together, these mechanisms allow an LLM to move beyond text generation and perform retrieval, computation, application actions, and standardized external-system integration.
Oracle additionally provides supporting agent resources such as Files, Vector Stores, Containers, Conversations, Projects, and memory capabilities, while SQL Search/NL2SQL is available as an OCI-native agent capability.
SSH, FTP, RDP, VCN routing, load balancing, SMS, and fax are not the four built-in tool categories identified in the OCI Enterprise AI Agents curriculum. Consequently, A is correct and agrees with the uploaded question set.
Study Guide reference/topic: OCI Enterprise AI Agents - File Search, Code Interpreter, Function Calling, MCP Calling, Vector Stores, and agent tools.
13. Frage
What is the strategic theme behind agentic AI capabilities in Oracle AI Database?
Antwort: B
Begründung:
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.
14. Frage
Which MCP primitive is model-controlled and used to perform actions?
Antwort: B
Begründung:
In MCP, Tools are the primitive explicitly designed to be model-controlled. They represent executable functions that an MCP server exposes so that a language model can take actions, retrieve information, query databases, invoke APIs, modify files, or perform computations. The uploaded question set identifies Tools as the correct answer.
The official MCP specification defines three principal server primitives with different control models:
Prompts are user-controlled , Resources are application-controlled , and Tools are model-controlled . Tools can be discovered by the model-facing application and invoked automatically according to the model's contextual interpretation of the user's request.
Resources differ because they primarily provide contextual data such as file contents or database schemas.
Prompts provide reusable templates or instructions normally selected through user interaction. "Schemas" are not one of the three MCP primitives in this control hierarchy; schemas describe structures such as tool parameters rather than constituting a standalone primitive.
Therefore, A is correct.
Study Guide reference/topic: Model Context Protocol (MCP) Fundamentals - MCP primitives, Tools, Resources, Prompts, control hierarchy, and tool invocation.
15. Frage
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