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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: Model Context Protocol (MCP) Fundamentals | 15% | - MCP architecture and integration
|
| Topic 4: OpenAI Responses API and Agents SDK | 15% | - OpenAI agent stack
|
| Topic 5: OCI Enterprise AI Agents | 25% | - OCI Enterprise AI platform and agent services
|
| Topic 6: Introduction to AI Agents | 15% | - AI agent fundamentals
|
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NEW QUESTION # 36
What is the strategic theme behind agentic AI capabilities in Oracle AI Database?
Answer: D
Explanation:
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.
NEW QUESTION # 37
Which prompt addition is used for zero-shot Chain-of-Thought prompting?
Answer: A
NEW QUESTION # 38
What is Oracle AI Database Private Agent Factory?
Answer: D
Explanation:
Oracle AI Database Private Agent Factory is a no-code platform for building, testing, and deploying intelligent AI agents . The uploaded question set marks D as correct, and current Oracle documentation independently confirms that definition.
Oracle describes Private Agent Factory as a platform intended for both business users and engineers. It provides an Agent Builder with visual and drag-and-drop capabilities, enabling users to construct intelligent assistants and workflows without writing conventional application code. The platform can combine pre-built agents, custom agents, reusable templates, enterprise data, LLMs, APIs, databases, and external tools.
The strategic purpose is to lower the engineering barrier for enterprise agent creation while retaining governance and integration with Oracle AI Database capabilities. Current releases include pre-built agents and workflow automation functionality for rapidly creating business-oriented agentic solutions.
It is not an embedding backup product, dedicated Kubernetes deployment manager, or physical training appliance. Those alternatives describe unrelated infrastructure or administration capabilities.
Therefore, D is directly supported by Oracle documentation.
Study Guide reference/topic: Agentic AI for Oracle AI Database - Private Agent Factory, no-code Agent Builder, pre-built agents, custom agents, workflows, and enterprise integration.
NEW QUESTION # 39
Which description defines memory poisoning in AI-agent systems?
Answer: C
Explanation:
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.
NEW QUESTION # 40
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: D
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 # 41
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