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| Section | Objectives |
|---|---|
| Using Oracle Database Actions and Data Studio Tools | - Apply Data Studio capabilities to data discovery, integration, analysis, and sharing tasks - Describe Database Actions and core development tools |
| Working with AI and Vector Foundations | - Apply vector distance and indexing concepts to similarity search needs - Explain vectors, embeddings, and the Oracle VECTOR data type - Describe AI, AGI, and machine learning foundations |
| Working with JSON and Graph in Oracle AI Database | - Distinguish when graph capabilities and Property Graph Views fit a business use case - Explain JSON and Oracle AI Database JSON capabilities - Describe core graph concepts and graph analytic capabilities |
| Implementing Select AI and AI Vector Search in Autonomous AI Database | - Determine how AI Vector Search supports GenAI pipelines and RAG - Apply AI Vector Search to combined semantic and business-data search scenarios - Describe Select AI in Autonomous AI Database |
| Building Low-Code Applications and Agentic AI | - Choose the appropriate Agent Factory capability for a no-code AI agent use case - Describe Oracle APEX as Oracle's low-code platform |
| Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy - Create an Autonomous AI Database Serverless instance for a basic workload - Describe Autonomous AI Database characteristics, offerings, and deployment choices |
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問題 #35
A support portal must search product manuals semantically while also filtering results by product line and support level stored in relational columns. How does Oracle AI Vector Search support this requirement?
答案：B
解題說明：
Oracle AI Vector Search supports semantic similarity search together with conventional relational predicates in SQL , making option C correct. The uploaded source explicitly identifies this integrated approach. Oracle AI Database 26ai provides the native VECTOR data type so embeddings can reside directly alongside relational business attributes. Oracle states that AI-powered vector similarity searches can be combined with business-data searches using SQL and the full capabilities of the converged database.
For the support portal, each manual or document chunk can have an embedding while relational columns identify its product line, entitlement level, version, or support tier. A SQL statement can restrict rows using predicates such as product line and support level while ordering eligible records by vector distance from the user's query embedding.
This approach is superior to retrieving a broad semantic result set and filtering it later in application code.
External post-filtering can waste processing and may remove highly ranked records without correctly replacing them with the next eligible matches.
Document APIs and property graphs are also not prerequisites. Oracle's converged architecture permits relational and vector criteria to operate together directly.
Study Guide reference: Implementing Select AI and AI Vector Search in Autonomous AI Database - hybrid business filtering, vector similarity ranking, and integrated SQL.
問題 #36
A team needs faster similarity search at scale and accepts approximate top-K results. Which feature should they use?
答案：D
解題說明：
A vector index with approximate similarity search is designed specifically for high-performance top-K retrieval over large vector collections. Exact vector search calculates distances against all candidate vectors that satisfy the query predicates, which can become computationally expensive at scale. Approximate nearest- neighbor search uses vector indexing structures to reduce the number of candidate vectors evaluated, significantly improving search latency while accepting a controlled trade-off between performance and recall or accuracy. Oracle AI Database supports vector indexes with organizations such as INMEMORY NEIGHBOR GRAPH and NEIGHBOR PARTITIONS and allows administrators to configure target accuracy.
This requirement explicitly states that approximate top-K results are acceptable, making an approximate vector index the intended architecture. A conventional B-tree index is appropriate for scalar equality, ordering, or range-access patterns, not high-dimensional semantic similarity. JSON Duality Views provide document-relational mapping rather than nearest-neighbor acceleration. Property graph views model entities and relationships and likewise do not serve as vector similarity indexes. The uploaded assessment identifies "a vector index with approximate search" as the correct option.
Study Guide reference: Working with AI and Vector Foundations - vector indexes, approximate nearest- neighbor search, top-K retrieval, and target accuracy.
問題 #37
What is the main difference between Autonomous AI Database Serverless and Dedicated deployment choices?
答案：B
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 #38
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
問題 #39
How can developers access JSON Duality Views?
答案：D
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 #40
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