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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Autonomous AI Database and Tools | ~16% | - Shared vs dedicated infrastructure - Core features of Autonomous AI Database - Built-in management and query tools |
| Topic 2: Data Management and Oracle Data Platform Overview | ~11% | - Data management concepts and data types - Oracle Data Strategy and multi-cloud deployment models - Modern data platform value and architecture |
| Topic 3: Oracle Database Services — Exadata, DBCS, and Engineered Systems | ~11% | - Database Cloud Service (DBCS) characteristics - Exadata architecture and features |
| Topic 4: Oracle Machine Learning and AI Integration | ~15% | - Oracle Data Studio and visualization - In-database machine learning algorithms - AI agents and LLM integration |
| Topic 5: Security, Resilience, and Cloud Integration | ~21% | - Cloud-native database services and deployment strategies - Database security architectures and data protection - High availability, backup, and disaster recovery |
| Topic 6: MySQL HeatWave and NoSQL Services | ~11% | - Oracle NoSQL Database features and use cases - MySQL HeatWave architecture and analytics |
| Topic 7: Converged Database — Multi-Model and AI Capabilities | ~15% | - Select AI and natural language querying - Oracle AI Vector Search concepts - JSON, Graph, Spatial, and key-value data support |
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問題 #37
A company wants a GenAI assistant that answers policy questions by grounding responses in its internal documents.
Which use of AI Vector Search best supports this design?
答案：C
解題說明：
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
Retrieval-Augmented Generation grounds an LLM by retrieving relevant enterprise content before generation.
Oracle Select AI with RAG uses AI Vector Search and semantic similarity to locate the top matching document chunks from a vector store, then supplies those retrieved texts together with the user's question to the LLM. This gives the model current, organization-specific context and reduces hallucination risk. Keyword- only retrieval can miss semantically related passages that use different wording, while graph edge identifiers are not a substitute for document content. Returning unfiltered database patches also does not provide targeted grounding. Therefore, the correct design is to retrieve semantically similar chunks and use them as context for generation. This matches Oracle's documented Select AI RAG workflow and the AI/vector foundations objectives. Oracle Docs
問題 #38
In a RAG-style application, where does similarity search apply?
答案：A
解題說明：
Similarity search performs the retrieval component of Retrieval-Augmented Generation. Source documents or other content are converted into vector embeddings and stored in a vector-capable database. The user's question is similarly transformed into an embedding, and a vector-distance or similarity calculation identifies stored embeddings whose semantic meaning is closest to the query. Oracle describes semantic similarity search as identifying and retrieving data points that closely match a query by comparing feature vectors in the vector store.
The resulting documents or chunks provide relevant contextual information to the generative model. Select AI with RAG, for example, retrieves content from a configured vector store through semantic similarity search and places that content into an augmented prompt sent to the LLM. The LLM then generates a response grounded in retrieved enterprise information. Similarity search therefore does not create the original documents, perform authentication, or eliminate response generation. Returning raw vectors alone would also fail to achieve RAG's purpose because the retrieved source content must ultimately inform the generated response. The uploaded question set confirms the retrieval of relevant stored content as the correct function.
Study Guide reference: Working with AI and Vector Foundations - semantic similarity search, vector retrieval, embeddings, and RAG architecture.
問題 #39
A company must control the lifecycle of its database encryption keys to satisfy regulatory requirements.
Which key management option should it use for the Oracle Autonomous AI Database instance?
答案：D
解題說明：
Customer-managed encryption keys integrated with OCI Vault are appropriate when an organization requires direct control over encryption-key lifecycle operations for security, governance, or regulatory compliance.
Autonomous AI Database uses Transparent Data Encryption to protect database data and supports both Oracle-managed and customer-managed master encryption keys. With the default Oracle-managed approach, Oracle performs key-management operations. With customer-managed keys, the organization creates and manages a master key in a supported external key-management system such as OCI Vault.
OCI Vault centralizes secure key storage and enables the customer to control operations such as key creation, rotation, lifecycle governance, access policy, and auditing. Autonomous AI Database then uses the customer- managed master encryption key as part of the TDE key hierarchy. This directly addresses the stated requirement for organizational control of encryption keys. Public certificates are intended for network identity and TLS-related functions rather than TDE key lifecycle management. APEX workspace configuration is unrelated to database master encryption keys. Oracle-managed keys provide strong encryption but do not satisfy a requirement specifically calling for customer-controlled lifecycle management.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - Autonomous AI Database security, TDE, OCI Vault, and customer-managed encryption keys.
問題 #40
Which set of tools is specifically highlighted as available from Database Actions?
答案：C
解題說明：
The Database Actions environment specifically includes SQL, Data Modeler, REST, JSON, Oracle Machine Learning, and Oracle APEX , making option C correct. The uploaded assessment identifies the same tool set. Oracle's current Autonomous AI Database documentation describes Database Actions as a web- based interface for development, data tooling, administration, and monitoring. Its Development area includes SQL, Data Modeler, REST, JSON, Charts, Scheduling, Oracle Machine Learning, Spatial Studio, Graph Studio, and Oracle APEX.
SQL provides the browser-based worksheet for SQL and PL/SQL execution. Data Modeler supports database modeling and diagramming. REST provides tools for database REST services and APIs. JSON provides facilities for working with JSON collections and documents. Oracle Machine Learning exposes integrated ML development capabilities, while APEX launches Oracle's low-code application-development environment.
Oracle's Machine Learning documentation likewise identifies Database Actions as the entry point for Oracle Machine Learning.
The remaining choices largely describe OCI infrastructure or security-management services rather than Database Actions development tools. Compute, block storage, load balancing, IAM, billing, private endpoints, and Vault are managed elsewhere in OCI.
Study Guide reference: Using Oracle Database Actions and Data Studio Tools - Database Actions Launchpad and Development tools.
問題 #41
A business wants one data platform where semantic similarity, relational consistency, and SQL-based filtering all work together.
Which statement aligns with this design goal?
答案：C
解題說明：
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
Oracle AI Database's converged architecture is designed to keep vectors and conventional business data in the same database so semantic ranking can be combined directly with SQL predicates. Oracle's VECTOR data type enables vector similarity search inside the database, and Oracle explicitly documents combining business- data searches with AI vector similarity search using SQL and the broader converged engine. This preserves transactional consistency and avoids exporting data to a separate vector platform merely to perform semantic retrieval. Options A and C incorrectly separate relational filtering from vector retrieval, while option B incorrectly requires graph modeling. The intended architecture is therefore one database that combines relational filtering, consistency, and vector similarity. This is a core design principle under "Implementing Select AI and AI Vector Search in Autonomous AI Database." Oracle Docs
問題 #42
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