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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Introduction to AI Agents | 15% | - AI agent fundamentals
|
| Topic 2: Model Context Protocol (MCP) Fundamentals | 15% | - MCP architecture and integration
|
| Topic 3: Agentic AI for Oracle AI Database | 25% | - Oracle AI Database agentic AI capabilities
|
| Topic 4: OCI Enterprise AI Agents | 25% | - OCI Enterprise AI platform and agent services
|
| Topic 5: LangChain for AI Agents | 5% | - LangChain fundamentals and agent construction
|
| Topic 6: OpenAI Responses API and Agents SDK | 15% | - OpenAI agent stack
|
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NEW QUESTION # 42
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?
Answer: B
Explanation:
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.
NEW QUESTION # 43
What is the high-level workflow for Oracle AI Vector Search?
Answer: B
Explanation:
Official Oracle documentation supports C. Oracle describes the typical AI Vector Search workflow in five stages: generate vector embeddings from unstructured content; store those embeddings with the associated data; create vector indexes; perform semantic/vector searches using SQL; and then use the retrieved content in an LLM prompt for RAG inference.
Therefore, the technically complete sequence is:
Generate embeddings # Store vectors # Create indexes # Search and query # Feed into LLM.
This ordering reflects the operational dependency between the stages. Embeddings must exist before they can be persisted. Vector indexes are created over stored vector columns to accelerate similarity retrieval. Search then retrieves semantically relevant content, which can subsequently be incorporated into an LLM prompt for retrieval-augmented generation.
There is an important discrepancy in the uploaded question file: it marks option A as the correct answer even though A omits the documented Create indexes stage. Because the request requires verification against official Agentic AI/Oracle material, the verified answer is C , not the supplied key's A.
Study Guide reference/topic: Agentic AI for Oracle AI Database - AI Vector Search workflow, embeddings, VECTOR storage, vector indexes, similarity search, and RAG.
NEW QUESTION # 44
Which four behaviors does every Select AI Agent perform?
Answer: B
Explanation:
Oracle Select AI Agent is architected around four foundational behaviors: Planning, Tool Use, Reflection, and Memory Management . Oracle documentation describes these as the framework's principal layers. Planning interprets the user's objective, decomposes it into ordered actions, and identifies appropriate capabilities. Tool Use invokes mechanisms such as NL2SQL, RAG, PL/SQL procedures, or external REST services. Reflection evaluates observations returned by those tools and determines whether the current plan should continue, be revised, or use another capability. Memory preserves context and useful information, supporting coherent multi-turn interactions and longer-term continuity.
Oracle explicitly states that Select AI Agent combines planning, tool use, reflection, and memory and implements a ReAct-style agentic pattern in which the agent reasons, acts through tools, evaluates observations, and continues toward the goal.
The alternative answer sets describe generic information-retrieval or operational lifecycle stages but do not correspond to Oracle's defined Select AI Agent architecture. Consequently, B reproduces the four documented agent behaviors and is the correct answer in the supplied question set.
Study Guide reference/topic: Agentic AI for Oracle AI Database - Select AI Agent architecture, Planning, Tool Use, Reflection, Memory, and ReAct.
NEW QUESTION # 45
In the OpenAI Agents SDK, how does a Handoff differ from the Manager pattern?
Answer: B
Explanation:
The distinction concerns ownership of the conversation and orchestration flow , not synchronous versus asynchronous execution. In the OpenAI Agents SDK's Manager pattern-also called agents-as-tools-a central manager remains the active user-facing agent. It invokes specialist agents as tools, receives their outputs, synthesizes them, and retains responsibility for the final response. The specialist supports the manager without taking ownership of the conversation.
A Handoff works differently. When the current agent hands the task to another agent, the selected specialist becomes the active agent and takes over the conversation for the remainder of that portion of the run.
OpenAI's official Agents SDK documentation explicitly describes the Manager pattern as retaining control and Handoffs as transferring control to a specialized agent.
This distinction allows architects to select centralized orchestration when a single agent must aggregate results or apply common controls, and decentralized handoffs when specialists should directly own particular interactions. Therefore, D states the relationship correctly and matches the uploaded question's answer key.
Study Guide reference/topic: OpenAI Responses API and Agents SDK - multi-agent orchestration, Agents as Tools/Manager pattern, and Handoffs.
NEW QUESTION # 46
In JSON-RPC 2.0, what is the difference between a request and a notification?
Answer: C
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 # 47
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