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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
|
| Introduction to AI Agents | - Agent development concepts
|
| Introduction to MCP | - Model Context Protocol fundamentals
|
| OCI Enterprise AI Agents | - OCI Enterprise AI platform
|
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NEW QUESTION # 34
What is the high-level workflow for Oracle AI Vector Search?
Answer: C
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 # 35
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: C
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 # 36
Assume an agent has access to the tools multiply(a, b) and divide(a, b). A user asks: "What is 15 multiplied by
8, then divided by 3?" In the OpenAI Agents SDK, how does the agent loop handle this multi-step task?
Answer: B
Explanation:
The OpenAI Agents SDK implements an iterative agent loop in which the model determines which available capability should be invoked, receives the resulting observation, and can then make another tool call based on that updated context. Consequently, the model first requests multiply(15, 8) . The function executes and returns 120 ; that tool output is supplied back to the model. The model then determines that the remaining operation requires divide(120, 3) and requests the second tool.
OpenAI describes Agents as LLMs equipped with tools and explains that the SDK runtime manages repeated model/tool interactions until the workflow produces final output. Function tools expose schemas and executable implementations to this orchestration process.
The Runner is responsible for coordinating the loop; it does not independently substitute its own arithmetic logic for the model's tool decisions. Similarly, the SDK does not synthesize a new combined function when two distinct tools are required, nor does it invoke every available tool without reason.
The uploaded question source explicitly marks the sequential multiply-then-divide behavior as correct.
Study Guide reference/topic: OpenAI Responses API and Agents SDK - agent loop, sequential function calling, tool observations, and Runner orchestration.
NEW QUESTION # 37
In JSON-RPC 2.0, what is the difference between a request and a notification?
Answer: A
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 # 38
What is the purpose of OCI Enterprise AI Governance?
Answer: A
Explanation:
OCI Enterprise AI Governance provides the control framework required to operate generative and agentic AI workloads securely in enterprise environments. Oracle defines governance as a combination of infrastructure protection, access control, network security, and runtime safety mechanisms. Key capabilities include OCI IAM policies , which determine who can access and manage Generative AI resources; Private Endpoints , which prevent model traffic from requiring public network exposure; Zero Trust Packet Routing , which introduces identity-aware network enforcement; and Guardrails , which apply safety and compliance controls to model inputs and outputs.
Oracle Guardrails specifically support mechanisms including content moderation, prompt-injection detection, and personally identifiable information detection. These controls address AI-specific operational and security risks rather than model lifecycle rollback or performance optimization.
Therefore, option D accurately expresses the purpose of Enterprise AI Governance. Model version management, runtime implementation, and latency monitoring may be operational concerns in an AI platform, but they are not the principal governance function described by OCI. The uploaded examination source also identifies D as the correct answer.
Study Guide reference/topic: OCI Enterprise AI Agents - Enterprise AI Governance, IAM, Private Endpoints, Zero Trust Packet Routing, and Guardrails.
NEW QUESTION # 39
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