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| Section | Objectives |
|---|---|
| Agentic AI for Oracle AI Database | - Oracle AI Database agentic AI capabilities
|
| LangChain for AI Agents | - LangChain fundamentals
|
| OpenAI Responses API and Agents SDK | - OpenAI agent development
|
| OCI Enterprise AI Agents | - OCI Enterprise AI platform
|
| Introduction to MCP | - Model Context Protocol fundamentals
|
| Introduction to AI Agents | - AI agent fundamentals and architecture
|
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NEW QUESTION # 22
Which native column type does Oracle AI Database use for storing vector embeddings?
Answer: B
Explanation:
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.
NEW QUESTION # 23
What does the @function_tool decorator do in the OpenAI Agents SDK?
Answer: D
Explanation:
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
NEW QUESTION # 24
Which MCP primitive is model-controlled and used to perform actions?
Answer: C
Explanation:
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.
NEW QUESTION # 25
What occurs during the MCP initialization phase?
Answer: A
Explanation:
MCP initialization establishes compatibility between the MCP client and server before normal protocol operations begin. During initialization, the parties establish a mutually supported protocol version and exchange their supported capabilities and implementation information. The client initiates the process with an initialize request containing its protocol version, capabilities, and client information. The server responds with its supported protocol version, capabilities, and server information, after which the client signals that initialization has completed. Authentication is handled at the transport or deployment security layer rather than being the defining initialization exchange. MCP initialization also does not fine-tune the LLM or execute every registered tool. This capability-negotiation process enables OCI agent applications using MCP Calling to understand which remote capabilities can safely be used. Model Context Protocol
NEW QUESTION # 26
In JSON-RPC 2.0, what is the difference between a request and a notification?
Answer: B
Explanation:
The defining distinction is the presence of a request identifier and the expectation of a corresponding response. In JSON-RPC 2.0, a normal request contains an id value so that the sender can correlate the response with the request. A notification deliberately omits the ID because no response is expected. The supplied course material identifies exactly this distinction.
MCP uses JSON-RPC 2.0 as its underlying messaging protocol. Its architecture documentation explicitly states that clients and servers exchange requests and responses, while notifications are used where no response is required. MCP's notification examples contain no id field, and the documentation explains that this follows JSON-RPC notification semantics.
The difference has nothing to do with whether data is structured, whether encryption is enabled, or which transport is used. Both requests and notifications can carry structured JSON parameters. Security belongs to the transport/authentication layer, while MCP can transmit JSON-RPC messages over supported transports such as STDIO or Streamable HTTP.
Therefore, B is the precise protocol-level distinction.
Study Guide reference/topic: Model Context Protocol (MCP) Fundamentals - JSON-RPC 2.0 requests, responses, IDs, and notifications.
NEW QUESTION # 27
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