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| Section | Weight | Objectives |
|---|---|---|
| OpenAI Responses API and Agents SDK | 15% | - OpenAI agent stack
|
| Agentic AI for Oracle AI Database | 25% | - Oracle AI Database agentic AI capabilities
|
| OCI Enterprise AI Agents | 25% | - OCI Enterprise AI platform and agent services
|
| Introduction to AI Agents | 15% | - AI agent fundamentals
|
| LangChain for AI Agents | 5% | - LangChain fundamentals and agent construction
|
| Model Context Protocol (MCP) Fundamentals | 15% | - MCP architecture and integration
|
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NEW QUESTION # 15
Which OCI capability is required for serving fine-tuned or imported custom models?
Answer: B
Explanation:
OCI Generative AI uses Dedicated AI Clusters to provide the isolated compute infrastructure required for fine- tuning and hosting custom model workloads. Oracle defines Dedicated AI Clusters as compute resources dedicated to a customer's models rather than shared with other tenancies. They can be created specifically for fine-tuning or for hosting model endpoints.
Oracle's current model onboarding workflow confirms the requirement. For imported models, the process includes importing the model, creating a hosting Dedicated AI Cluster , creating an endpoint, and then invoking the model. Fine-tuned models similarly require dedicated clusters for fine-tuning and subsequent hosting.
Shared On-Demand inference is appropriate for supported Oracle-hosted pretrained models, but it does not provide the dedicated isolated serving environment required by these custom model workflows. Object Storage can be an input location for model artifacts or training data, but it is storage rather than model-serving infrastructure. General-purpose Free Tier compute is likewise not the managed Generative AI capability Oracle specifies for custom-model serving.
Thus, B is correct and agrees with the uploaded course material.
Study Guide reference/topic: OCI Enterprise AI Agents - Dedicated AI Clusters, imported models, fine- tuned custom models, hosting clusters, and endpoints.
NEW QUESTION # 16
Which authentication approach should be used for production-grade access to OCI Enterprise AI services?
Answer: B
Explanation:
For production OCI Enterprise AI workloads, Oracle recommends OCI IAM-based authentication rather than long-lived development credentials. The uploaded source identifies OCI IAM authentication with signed requests and IAM policies as the correct production architecture.
Oracle's OCI Responses API authentication documentation distinguishes service API keys used for testing and early development from IAM authentication intended for production and OCI-managed environments.
IAM-based authentication uses OCI identity principals and request-signing mechanisms and allows authorization to be controlled through centralized IAM policies. Oracle specifically recommends IAM when applications execute in services such as OCI Functions or Oracle Kubernetes Engine, when long-lived API keys should be avoided, or when fine-grained centralized access control is required.
IAM policies also implement least privilege by defining exactly which users, groups, resource principals, or workloads can access individual Generative AI resource types.
Browser cookies, anonymous tenancy access, and credentials committed into source repositories violate standard enterprise security practices and significantly increase credential-exposure risk.
Therefore, A is the only production-grade authentication approach among the choices.
Study Guide reference/topic: OCI Enterprise AI Agents - OCI IAM, signed requests, policies, principals, least privilege, and production authentication.
NEW QUESTION # 17
What occurs during the MCP initialization phase?
Answer: C
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 # 18
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 # 19
Which set lists built-in tool categories supported by OCI Enterprise AI Agents?
Answer: A
Explanation:
OCI Enterprise AI Agents supports a defined set of OpenAI-compatible agent tools through the OCI Responses API. Oracle's current documentation identifies File Search, Code Interpreter, Function Calling, and MCP Calling as supported tool categories.
File Search allows an agent to retrieve relevant information from indexed content and vector stores. Code Interpreter provides a controlled environment for computational or programmatic analysis. Function Calling lets the model request execution of application-defined functions with structured parameters. MCP Calling enables the agent to discover and invoke capabilities made available by remote Model Context Protocol servers. Together, these mechanisms allow an LLM to move beyond text generation and perform retrieval, computation, application actions, and standardized external-system integration.
Oracle additionally provides supporting agent resources such as Files, Vector Stores, Containers, Conversations, Projects, and memory capabilities, while SQL Search/NL2SQL is available as an OCI-native agent capability.
SSH, FTP, RDP, VCN routing, load balancing, SMS, and fax are not the four built-in tool categories identified in the OCI Enterprise AI Agents curriculum. Consequently, A is correct and agrees with the uploaded question set.
Study Guide reference/topic: OCI Enterprise AI Agents - File Search, Code Interpreter, Function Calling, MCP Calling, Vector Stores, and agent tools.
NEW QUESTION # 20
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