最速の配送速度を保証できる最新のオペレーションシステムを当社にインストールしました。具体的には、購入後5〜10分以内に1z0-1195-26トレーニング資料をすぐに入手できます。同時に、支払いボタンを押すとすぐに、オペレーティングシステムによって個人情報が自動的に暗号化されます。つまり、を購入することを選択した場合、個人情報を心配する必要はありません。1z0-1195-26当社の試験対策。 1z0-1195-26ガイド資料：Oracle AI Database Foundations Associateの学習に完全に専念できるように、お客様に不安を残さないことを目指しています。時間は誰も待っていないので、アイロンが熱いうちに打つことをお勧めします。
| Section | Objectives |
|---|---|
| Topic 1: Working with AI and Vector Foundations | - Explain vectors, embeddings, and the Oracle VECTOR data type - Describe AI, AGI, and machine learning foundations - Apply vector distance and indexing concepts to similarity search needs |
| Topic 2: 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 |
| Topic 3: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | - Describe Autonomous AI Database characteristics, offerings, and deployment choices - Create an Autonomous AI Database Serverless instance for a basic workload - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy |
| Topic 4: Working with JSON and Graph in Oracle AI Database | - Explain JSON and Oracle AI Database JSON capabilities - Describe core graph concepts and graph analytic capabilities - Distinguish when graph capabilities and Property Graph Views fit a business use case |
| Topic 5: Building Low-Code Applications and Agentic AI | - Describe Oracle APEX as Oracle's low-code platform - Choose the appropriate Agent Factory capability for a no-code AI agent use case |
| Topic 6: Implementing Select AI and AI Vector Search in Autonomous AI Database | - Describe Select AI 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 |
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質問 # 16
Which JSON feature helps represent repeating child data inside one document?
正解：C
解説：
JSON arrays and nested objects provide the hierarchical structure required to represent repeating or composite child information within a single JSON document. The uploaded question set explicitly identifies this answer. Oracle AI Database supports standard JSON value types including objects and arrays. An object contains named property/value members, while an array contains an ordered sequence of JSON values.
Because array elements can themselves be objects or additional arrays, applications can represent complex parent-child structures naturally within one document.
For example, a customer document can contain an addresses array with multiple address objects, or an order can contain an items array where every element contains product, quantity, and price properties. This avoids artificially flattening inherently hierarchical information.
A scalar property is appropriate for a single value and therefore cannot naturally represent repeated child records. Merely storing an external identifier does not embed the child information in the document. Creating a separate standalone JSON document for every child would also fail the requirement to represent the repeating data inside one document .
Study Guide reference: Working with JSON and Graph in Oracle AI Database - JSON objects, arrays, hierarchical documents, and nested JSON structures.
質問 # 17
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?
正解：B
解説：
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.
質問 # 18
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?
正解：B
解説：
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
質問 # 19
When comparing a query vector with stored vectors, what does a smaller vector distance indicate?
正解：B
解説：
Vector distance quantifies how far apart two vector representations are according to a specified mathematical distance metric. In semantic search, embeddings place semantically related content near one another in a multidimensional vector space. Consequently, when a distance-oriented metric such as cosine distance or Euclidean distance produces a smaller value, the vectors are considered closer and therefore generally more similar according to the embedding model. Oracle's cosine-distance documentation explains the inverse relationship between cosine similarity and cosine distance: increasingly similar vector directions produce smaller cosine-distance values.
For example, cosine distance is calculated as 1 - cosine similarity. Identical or highly aligned semantic representations therefore approach a distance of zero, while increasingly different vectors produce larger distances. This principle is why Oracle SQL similarity searches commonly order rows by VECTOR_DISTANCE(...) in ascending order and fetch the first N rows: the rows with the smallest distances are the nearest semantic matches. Distance has no relationship to database maintenance windows, and a smaller value does not prove that different embedding models were used. Vector search also remains available through SQL and does not require GraphQL.
Study Guide reference: Working with AI and Vector Foundations - vector embeddings, distance metrics, semantic similarity, and top-K ranking.
質問 # 20
You just provisioned a Serverless database and need to confirm when application teams can begin connecting to it. When is the database ready for connections?
正解：A
解説：
The database should be treated as ready after provisioning completes and its lifecycle state becomes Available
. The uploaded question source explicitly identifies this state as the correct readiness point. Oracle Autonomous AI Database documentation uses Available as the operational lifecycle state for an instance and requires that state for numerous database-management operations. Oracle's connection documentation then describes applications and client tools connecting to an existing Autonomous AI Database through supported Oracle Net Services and built-in tools.
Selecting Create only initiates the provisioning workflow. OCI still has to allocate and configure the underlying service resources before the database becomes operational. Loading a JSON collection is an application-data activity performed after database availability and is unrelated to whether the database service itself is ready. Likewise, Data Studio is a built-in data-management environment; creation or use of a Data Studio workflow is not a prerequisite for normal database connectivity.
For certification purposes, the decisive distinction is between provisioning initiated and provisioning completed . The Available lifecycle status indicates that the service has completed the provisioning process sufficiently for normal database use.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - Serverless provisioning, lifecycle states, and database connectivity.
質問 # 21
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