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| Section | Weight | Objectives |
|---|---|---|
| MySQL HeatWave and NoSQL Services | ~11% | - MySQL HeatWave architecture and analytics - Oracle NoSQL Database features and use cases |
| Converged Database — Multi-Model and AI Capabilities | ~15% | - JSON, Graph, Spatial, and key-value data support - Oracle AI Vector Search concepts - Select AI and natural language querying |
| Oracle Database Services — Exadata, DBCS, and Engineered Systems | ~11% | - Exadata architecture and features - Database Cloud Service (DBCS) characteristics |
| Oracle Machine Learning and AI Integration | ~15% | - AI agents and LLM integration - In-database machine learning algorithms - Oracle Data Studio and visualization |
| Autonomous AI Database and Tools | ~16% | - Core features of Autonomous AI Database - Shared vs dedicated infrastructure - Built-in management and query tools |
| 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 |
| 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 |
Oracleの1z0-1195-26認定試験に合格するのは簡単なことではありませんか。GoShikenのOracleの1z0-1195-26試験トレーニング資料を選ぶなら、一回で認定試験に合格するの可能性は高いです。GoShikenのOracleの1z0-1195-26試験トレーニング資料は豊富な経験を持っているIT業種の専門家が長年の研究を通じて、作成したものです。その権威性が高いと言えます。GoShikenを選ぶなら、絶対に後悔させません。
質問 # 50
When comparing a query vector with stored vectors, what does a smaller vector distance indicate?
正解：C
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 # 51
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：A
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 # 52
Which set of characteristics is commonly used to describe Autonomous AI Database?
正解：C
解説：
The uploaded question set identifies self-driving, self-securing, and self-repairing as the defining Autonomous AI Database characteristics. Oracle documentation uses the same terminology when describing Autonomous Database services.
Self-driving refers to automated database-management activities that traditionally require significant DBA intervention, including provisioning, tuning, optimization, backups, patching, and scaling. Self-securing encompasses automated security practices designed to protect database infrastructure and data, including encryption and security maintenance. Self-repairing describes automated availability and fault-management capabilities intended to reduce downtime and recover from infrastructure or database failures with minimal manual involvement.
These attributes are central to Oracle's Autonomous Database strategy because the service shifts routine infrastructure and database operations from customer-managed procedures toward automated cloud-service capabilities. This allows development and data teams to focus on application logic and business workloads rather than routine database administration.
The alternative combinations are not Oracle's established characterization. Autonomous AI Database does not promise autonomous application coding, licensing, documentation creation, or architectural design. Those terms incorrectly broaden the scope of database automation beyond the capabilities Oracle associates with the Autonomous platform.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - Autonomous characteristics and automated database operations.
質問 # 53
A developer wants one place to work with SQL, REST endpoints, JSON features, APEX, and machine learning tools. Which entry point should the developer use?
正解：D
解説：
Database Actions is the correct centralized entry point because it is the browser-based development and administration environment bundled with Autonomous AI Database. Oracle documents Database Actions as providing development, data, administration, monitoring, and download capabilities. Its Development area includes SQL, Data Modeler, REST, JSON, Oracle Machine Learning, Graph Studio, and Oracle APEX, which directly matches the developer's requirement for a single integrated workspace. The SQL worksheet supports SQL and PL/SQL development, while REST and JSON tools expose database data through modern application interfaces. APEX provides low-code application development, and Oracle Machine Learning integrates analytical and machine-learning workflows with database-resident data.
The other choices are individual configuration concepts rather than comprehensive developer entry points.
Customer-managed key rotation relates to encryption-key lifecycle management; vector target accuracy controls approximate vector-search behavior; and a Property Graph View is a graph modeling construct.
Therefore, none provides the breadth of tooling required. The uploaded question set likewise identifies Database Actions as the correct response.
Study Guide reference: Using Oracle Database Actions and Data Studio Tools - Database Actions development tools and integrated workspaces.
質問 # 54
What is an embedding in the context of AI Vector Search?
正解：A
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 # 55
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1z0-1195-26認証資格: https://www.goshiken.com/Oracle/1z0-1195-26-mondaishu.html