当社Xhs1991は、製品の品質が非常に重要であることを深く知っているため、1z0-1195-26テストトレントの高品質の開発に注力しています。当社の製品を購入したすべてのお客様は、1z0-1195-26ガイド急流に深い印象を残しています。もちろん、顧客は製品の高品質だけでなく、製品の効率性にも深い印象を残しています。 1z0-1195-26試験の質問は多くの時間を節約するのに役立ちます。1z0-1195-26試験準備を使用する場合、学習に20〜30時間を費やすだけで、1z0-1195-26試験に合格できます。
| Section | Objectives |
|---|---|
| Topic 1: Working with JSON and Graph in Oracle AI Database | - Distinguish when graph capabilities and Property Graph Views fit a business use case - Describe core graph concepts and graph analytic capabilities - Explain JSON and Oracle AI Database JSON capabilities |
| Topic 2: Working with AI and Vector Foundations | - Describe AI, AGI, and machine learning foundations - Apply vector distance and indexing concepts to similarity search needs - Explain vectors, embeddings, and the Oracle VECTOR data type |
| Topic 3: 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 4: 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 |
| Topic 5: 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 6: Implementing Select AI and AI Vector Search in Autonomous AI Database | - Describe Select AI in Autonomous AI Database - Apply AI Vector Search to combined semantic and business-data search scenarios - Determine how AI Vector Search supports GenAI pipelines and RAG |
当社Xhs1991の製品は、実践と記憶に値する専門知識の蓄積です。一緒に参加して、お客様のニーズに合わせて1z0-1195-26ガイドクイズの成功に貢献する多くの専門家がいます。仕事に取り掛かって顧客とやり取りする前に厳密に訓練された責任ある忍耐強いスタッフ。 1z0-1195-26試験の準備の質を実践し、経験すると、それらの保守性と有用性を思い出すでしょう。 1z0-1195-26練習教材が試験受験者の98％以上が夢の証明書を取得するのに役立った理由を説明しています。あなたもそれを手に入れることができると信じてください。
質問 # 25
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 # 26
A vector index will not fit entirely in memory. Which index organization option should be considered for use?
正解：B
解説：
NEIGHBOR PARTITIONS is the correct index organization when an entirely memory-resident vector graph is unsuitable. The uploaded assessment identifies NEIGHBOR PARTITIONS as the intended answer.
Oracle AI Vector Search distinguishes two primary approximate vector-index organizations: INMEMORY NEIGHBOR GRAPH , based on HNSW, and NEIGHBOR PARTITIONS , based on IVF.
HNSW is specifically an in-memory graph structure. Oracle documentation describes HNSW indexes as specialized memory-only structures and provides vector-memory-pool facilities for holding them. By contrast, the IVF-based Neighbor Partition index organizes vectors into centroid-based partitions and narrows each approximate search to relevant partitions rather than maintaining the complete graph as an in-memory HNSW structure.
EXACT SEARCH ONLY is not an index organization and would typically require evaluating a broader candidate set, sacrificing the scalability benefits of approximate indexing. TARGET ACCURACY is a parameter governing the accuracy/performance trade-off of approximate searches, not an index organization.
INMEMORY NEIGHBOR GRAPH directly conflicts with the stated memory constraint.
Study Guide reference: Working with AI and Vector Foundations - vector index organizations, IVF
/Neighbor Partitions, HNSW/In-Memory Neighbor Graph, and approximate similarity search.
質問 # 27
A data engineer is creating a vector similarity query and wants to choose the distance metric correctly.
Which guidance should be applied?
正解：D
解説：
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
Oracle recommends using the distance metric associated with the embedding model that generated the vectors. Different metrics-such as cosine, Euclidean, dot product, Manhattan, or Hamming-measure similarity in different ways, and an embedding model is normally trained or intended to be evaluated with a particular metric. Oracle's AI Vector Search documentation states that it is generally best to match the query distance metric to the metric used to train the embedding model. Oracle also notes that a vector index should be created and searched with the appropriate distance function; using a different function can prevent index use and trigger exact search behavior. Table row count, maintenance schedules, and the presence of JSON attributes do not determine semantic vector geometry. Therefore, option C is the correct guidance. Oracle Docs
質問 # 28
What is an embedding in the context of AI Vector Search?
正解：B
解説：
An embedding is a numerical vector representation of the meaning or characteristics of some source content.
Oracle describes vector embeddings as mathematical vector representations that capture semantic meaning for content such as words, documents, images, audio, or other data objects. The embedding model transforms the source input into an ordered sequence of numerical values, placing semantically related items close together within a multidimensional vector space.
This numerical representation enables AI Vector Search. Instead of comparing documents solely by literal keyword equality, Oracle can calculate mathematical distances between embeddings. Smaller distances generally identify objects that are semantically more closely related according to the embedding model. This supports capabilities such as semantic document retrieval, recommendation systems, image similarity, RAG, and natural-language search.
An embedding is therefore not a backup mechanism, database maintenance configuration, or graph label.
Those concepts belong to unrelated database subsystems. The crucial certification distinction is that the original content and its embedding are different representations: the content may be text, image, audio, or another object, while its embedding is a numeric vector representing learned semantic characteristics. The uploaded assessment identifies "a sequence of numbers that represents semantic content" as the correct description.
Study Guide reference: Working with AI and Vector Foundations - embeddings, semantic representations, vector spaces, and similarity search.
質問 # 29
What is OSON in Oracle AI Database JSON support?
正解：C
解説：
OSON is Oracle's optimized binary representation for JSON data. Oracle AI Database uses OSON as the native storage representation of the SQL JSON data type. Unlike textual JSON stored in VARCHAR2, CLOB, or BLOB values, native JSON data does not need to be repeatedly parsed from character representation for common processing operations. Oracle states that OSON is optimized for fast query and update operations in both the Oracle AI Database server and supported database clients.
The practical advantage is that applications retain JSON's flexible document model while gaining database- native processing efficiency, SQL integration, indexing capabilities, and transactional control. OSON therefore concerns the physical/optimized representation of JSON data, not the operational scheduling of JSON collections or the visualization of graph structures. It is also unrelated to the SQL Worksheet, which is a Database Actions development interface for executing SQL and PL/SQL. The uploaded question explicitly identifies "An optimized binary format for JSON storage" as the correct answer.
Study Guide reference: Working with JSON and Graph in Oracle AI Database - native JSON data type, OSON binary JSON representation, JSON query and update processing.
質問 # 30
......
我々Xhs1991サイトはすべてのOracle 1z0-1195-26試験に準備する受験生の最も信頼できる強いバッキングです。Oracle 1z0-1195-26試験のための一切な需要を満足して努力します。購入した後、我々はあなたが1z0-1195-26試験にうまく合格するまで細心のヘルプをずっと与えます。一年間の無料更新と試験に合格しなくて全額返金も我々の誠のアフタサーブすでございます。
1z0-1195-26日本語練習問題: https://www.xhs1991.com/1z0-1195-26.html