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| Section | Weight | Objectives |
|---|---|---|
| Security, Resilience, and Cloud Integration | ~21% | - High availability, backup, and disaster recovery - Cloud-native database services and deployment strategies - Database security architectures and data protection |
| MySQL HeatWave and NoSQL Services | ~11% | - MySQL HeatWave architecture and analytics - Oracle NoSQL Database features and use cases |
| 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 |
| Autonomous AI Database and Tools | ~16% | - Built-in management and query tools - Shared vs dedicated infrastructure - Core features of Autonomous AI Database |
| Oracle Machine Learning and AI Integration | ~15% | - Oracle Data Studio and visualization - In-database machine learning algorithms - AI agents and LLM integration |
| Converged Database — Multi-Model and AI Capabilities | ~15% | - Oracle AI Vector Search concepts - Select AI and natural language querying - JSON, Graph, Spatial, and key-value data support |
| Oracle Database Services — Exadata, DBCS, and Engineered Systems | ~11% | - Database Cloud Service (DBCS) characteristics - Exadata architecture and features |
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問題 #20
What is the main difference between Autonomous AI Database Serverless and Dedicated deployment choices?
答案：D
解題說明：
The fundamental distinction is that Serverless emphasizes simplicity and elasticity , whereas Dedicated provides isolated infrastructure and greater operational customization . This is the answer explicitly identified in the uploaded question set. Oracle documentation describes the Serverless model as ultra-simple and elastic: customers manage the Autonomous AI Database while Oracle manages the underlying Exadata infrastructure. Dedicated, by contrast, provides exclusive compute, storage, network, and database resources.
Oracle also characterizes Dedicated as a private-cloud-in-public-cloud deployment model with high levels of security isolation and governance. Dedicated environments can support customizable operational policies involving workload placement, update scheduling, availability, capacity usage, and other infrastructure-level concerns. Serverless removes much of that infrastructure planning and is therefore well suited to organizations prioritizing rapid provisioning and elastic consumption.
Neither deployment is restricted exclusively to JSON or relational workloads, and the distinction is not primarily about available developer SQL tools. Option A reverses the infrastructure characteristics: it is Dedicated-not Serverless-that supplies the isolated dedicated resource model.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - Serverless versus Dedicated deployment architecture.
問題 #21
What does Select AI enable in Autonomous AI Database?
答案：B
解題說明：
Select AI enables users to interact with Autonomous AI Database by expressing requests in natural language rather than manually constructing SQL. Oracle documents that Select AI uses generative AI and large language models to convert natural-language input into Oracle SQL. Depending on the requested action, the generated SQL can be displayed, explained, executed, or its results can be transformed into a natural-language response. This makes database information accessible to users who understand the business question but may not know SQL syntax or the underlying schema.
Internally, Select AI can augment a prompt with schema metadata, interact with the configured LLM, generate SQL, run the query, and optionally narrate the resulting data. It also extends beyond NL2SQL into Retrieval-Augmented Generation, conversations, and other generative-AI capabilities. It does not replace relational SQL with graph pattern matching, manage encryption-key rotation, or provision Autonomous AI Database infrastructure. Those are unrelated database administration or graph functions. The supplied question bank explicitly marks the natural-language data-query capability as correct.
Study Guide reference: Implementing Select AI and AI Vector Search in Autonomous AI Database - Select AI, natural-language interaction, NL2SQL, and LLM integration.
問題 #22
A retailer wants to find products semantically similar to a shopper's description, but only from items that are in stock and sold in the shopper's region. Which design meets this requirement?
答案：B
解題說明：
Oracle AI Vector Search is designed to combine semantic similarity with conventional business predicates inside the same SQL statement. Oracle's native VECTOR data type and vector-distance operators allow embeddings to coexist with relational attributes such as inventory status, region, category, price, or security classification. The application can therefore restrict rows using ordinary SQL predicates-for example, in_stock = 'Y' and region = :region-while ranking qualifying products using vector similarity or distance.
Oracle explicitly positions the converged database architecture as enabling vector similarity searches together with relational, JSON, graph, text, and spatial criteria in a single database query.
JSON Duality Views are not a prerequisite for semantic search, and a property graph does not replace the vector engine. Performing vector retrieval first and filtering unavailable inventory in application code is also inferior because it wastes retrieval capacity and can distort the top-K result set. Applying business filters and semantic ranking together keeps processing close to the data and produces the appropriate eligible top matches. The uploaded source identifies this integrated SQL design as the correct choice.
Study Guide reference: Implementing Select AI and AI Vector Search in Autonomous AI Database - vector similarity search combined with relational filtering.
問題 #23
A team needs faster similarity search at scale and accepts approximate top-K results. Which feature should they use?
答案：C
問題 #24
How can developers access JSON Duality Views?
答案：A
解題說明：
Developers can access JSON-Relational Duality Views using document-oriented interfaces, including Oracle AI Database API for MongoDB , as well as SQL-based database interfaces. The uploaded source explicitly identifies "MongoDB-compatible APIs or SQL" as correct. Oracle documentation confirms that duality views expose relational table data as JSON documents and that applications can interact with the same underlying data either document-centrically or relationally.
With the MongoDB-compatible API, the duality view can be treated as a document collection by applications using familiar MongoDB drivers and development patterns. At the same time, because the authoritative data remains in Oracle relational tables, SQL and other relational capabilities can operate directly on the same information. This is a primary architectural advantage of JSON-Relational Duality.
No export into a separate document store is necessary; doing so would reintroduce data duplication and synchronization problems that duality views are designed to avoid. Graph visualization is unrelated to document access, and although APEX applications can consume database data, APEX-specific PL/SQL packages are not the principal interface defining duality-view access.
Study Guide reference: Working with JSON and Graph in Oracle AI Database - JSON Duality Views, Oracle AI Database API for MongoDB, SQL access, and unified relational/document development.
問題 #25
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