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| Section | Weight | Objectives |
|---|---|---|
| Agentic AI for Oracle AI Database | 25% | - Oracle AI Database agentic AI capabilities
|
| LangChain for AI Agents | 5% | - LangChain fundamentals and agent construction
|
| Model Context Protocol (MCP) Fundamentals | 15% | - MCP architecture and integration
|
| Introduction to AI Agents | 15% | - AI agent fundamentals
|
| OpenAI Responses API and Agents SDK | 15% | - OpenAI agent stack
|
| OCI Enterprise AI Agents | 25% | - OCI Enterprise AI platform and agent services
|
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質問 # 22
Which description defines memory poisoning in AI-agent systems?
正解：B
解説：
Memory poisoning is an agent-security attack in which malicious, misleading, or attacker-controlled information is introduced into memory that the agent may reuse in future reasoning or actions. The uploaded course source defines it as malicious content inserted into persistent memory stores and identifies C as correct.
Oracle's current AI Agent Memory security guidance explains why persistent memory must be treated as a security-sensitive surface. Model-derived memories, summaries, context cards, metadata, and retrieved records can become persistent state and later be inserted into prompts. Oracle therefore advises treating memory-derived content as untrusted and emphasizes that write-capable memory paths can influence future prompts and retrieval results.
The broader agent-security definition is also explicit in OWASP's Agentic AI guidance: memory poisoning involves malicious data being persisted in agent memory so that it can influence future sessions or behaviors.
This differs from temporary context-window pressure, SQL injection, or physical RAM corruption. The essential security property is persistence : compromised memory can affect later reasoning long after the original malicious interaction.
Therefore, C is correct.
Study Guide reference/topic: Introduction to AI Agents - agent memory, persistent state, memory poisoning, prompt injection persistence, and agent security.
質問 # 23
What is the purpose of the Vector Stores API?
正解：C
解説：
The Vector Stores API provides infrastructure for ingesting content and retrieving the portions that are semantically relevant to a query. Files associated with a vector store can be chunked and prepared for vector- based retrieval, allowing applications and agents to locate content based on semantic similarity rather than only exact lexical matches .
OpenAI's official Vector Stores API supports searching a vector store with a natural-language query and returns relevant content chunks together with similarity scores. The API also supports attaching files to vector stores and configuring the chunking strategy used during ingestion. This architecture is foundational to retrieval-augmented generation and File Search workflows: source material is indexed, a user query retrieves semantically related chunks, and those chunks can then provide grounded context to a model.
Language translation is a generative-model task. Object Storage encryption is a cloud-storage security function, while video streaming is unrelated to the purpose of a vector store.
Accordingly, "Indexing and retrieving data by meaning" is the technically correct description and is also the answer specified by the uploaded question source.
Study Guide reference/topic: OpenAI Responses API and Agents SDK - Vector Stores, semantic retrieval, chunking, File Search, similarity ranking, and RAG.
質問 # 24
Why is chunking necessary before generating embeddings for large documents?
正解：B
解説：
Embedding models accept inputs only up to their supported input-size or token limits. Large documents can exceed those limits and therefore must be divided into smaller segments before embedding generation. The course source explicitly identifies overcoming token limits as the reason for chunking.
Oracle AI Vector Search includes native chunking functionality that can split text according to characters, words, or model vocabulary tokens. Oracle's Vector Search guidance specifically states that input length must remain within the token limits of the embedding model and provides configurable maximum chunk sizes.
Chunking also provides an important retrieval benefit. Instead of representing an entire long document with one coarse embedding, the system creates embeddings for semantically meaningful sections. A similarity query can then retrieve only the chunks most relevant to the user's question, reducing irrelevant context and improving retrieval-augmented generation precision.
Chunking does not intentionally remove semantic meaning; well-designed chunking attempts to preserve it. It does not automatically provide encryption, and SQL storage format is not its underlying purpose.
Therefore, A is correct.
Study Guide reference/topic: Agentic AI for Oracle AI Database - document chunking, embedding token limits, vector generation, semantic retrieval, and RAG.
質問 # 25
What is the architectural advantage of Autonomous AI Database MCP Server over a separately deployed third- party MCP server?
正解：A
質問 # 26
Why are docstrings especially important when defining LangChain tools with the @tool decorator?
正解：D
解説：
When LangChain's @tool decorator is applied to a Python function, the function's documentation becomes part of the metadata shown to the language model. LangChain explicitly states that, by default, the function's docstring becomes the tool description , helping the model understand when the capability should be selected.
This metadata is operationally significant because tool selection is model-driven. A clear description communicates the tool's purpose, appropriate usage conditions, expected arguments, and semantic boundaries.
LangChain's context-engineering guidance emphasizes that tool names, descriptions, argument names, and argument descriptions guide the model's reasoning about when and how a particular tool should be invoked.
Docstrings therefore affect agent reliability, not Python runtime performance. They do not determine which underlying model provider is used, and they have no cryptographic role in protecting function parameters.
Poor or ambiguous descriptions can cause an LLM to choose an inappropriate tool or supply unsuitable arguments even when the Python implementation itself is technically correct.
Consequently, D precisely captures why docstrings are particularly important for @tool -defined LangChain functions. The uploaded source confirms the same answer.
Study Guide reference/topic: LangChain for AI Agents - @tool decorator, tool descriptions, docstrings, schemas, tool selection, and context engineering.
質問 # 27
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