1z0-1195-26練習教材を購入したすべての学生は、Oracleの1z0-1195-26学習教材が提供する内容に従い、毎日学習する限り、プロの1z0-1195-26資格試験に合格できると信じています。 模擬試験を通じて定期的に自己検査を実施します。 もちろん、Jpexam購入する前に、1z0-1195-26学習教材は無料の試用サービスを提供します。当社のWebサイトにログオンしている限り、無料で試用版のOracle AI Database Foundations Associate質問バンクをダウンロードできます。 1z0-1195-26テストエンジンを試した後、あなたはそれらを愛することになると信じています。
| Section | Objectives |
|---|---|
| Topic 1: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | - Create an Autonomous AI Database Serverless instance for a basic workload - Describe Autonomous AI Database characteristics, offerings, and deployment choices - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy |
| Topic 2: 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 3: 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 |
| Topic 4: 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 5: Using Oracle Database Actions and Data Studio Tools | - Describe Database Actions and core development tools - Apply Data Studio capabilities to data discovery, integration, analysis, and sharing tasks |
| Topic 6: 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 |
1z0-1195-26学習教材は、当初の目標を達成し、仕事のキャリアをよりスムーズにし、家族の生活の質を向上させるのに役立ちます。 1z0-1195-26試験トレントを20〜30時間学習するだけで、Oracleの1z0-1195-26試験に自信を持って参加できると言っても過言ではありません。 そして、10年以上にわたってこのキャリアでプロフェッショナルであったため、あなたの成功を確実にすることができます。 そして、数千人の候補者が、優れた1z0-1195-26トレーニング資料の助けを借りて、Oracle AI Database Foundations Associate夢と野望を達成しました。
質問 # 41
How is Oracle APEX described?
正解：A
解説：
Oracle APEX is Oracle's low-code application development platform , making option C correct. The uploaded source explicitly identifies this characterization. Oracle's current documentation defines APEX as a complete low-code platform for rapidly building secure, scalable enterprise applications. Developers work primarily through the browser-based App Builder and configure declarative components, pages, features, and data sources rather than manually writing the entire application stack.
APEX is built as part of Oracle Database and provides direct access to Oracle Database data. Its model-driven architecture allows developers to use declarative metadata for common application functionality while retaining SQL, PL/SQL, JavaScript, REST, and other extension mechanisms when custom logic is necessary.
The incorrect options describe entirely different Oracle technologies. Graph analytics and property graphs are Oracle Database graph capabilities, not the definition of APEX. Encryption-key lifecycle management belongs to database security and services such as OCI Vault. Oracle's optimized binary representation for JSON storage is OSON.
The exam-level distinction is therefore direct: APEX addresses rapid application creation, especially data- centric enterprise applications, through a browser-based low-code development model tightly integrated with Oracle Database.
Study Guide reference: Building Low-Code Applications and Agentic AI - Oracle APEX, low-code development, App Builder, and database-driven applications.
質問 # 42
A bank wants to identify indirect money movement patterns across several accounts to support fraud investigation. Which approach fits this requirement?
正解：D
解説：
Modeling accounts as vertices and transfers as edges is the appropriate graph design because the investigation depends on discovering relationships and multi-hop transaction paths. The uploaded source explicitly designates this model as correct. Oracle Property Graph documentation confirms that graphs represent linked information using vertices, edges, and associated properties and are suitable for pattern matching, path finding, community detection, and other relationship-centric analytics.
In this banking scenario, each account can become a vertex containing account attributes, while each transfer becomes a directed edge containing properties such as amount, timestamp, or transaction type. Graph traversal can then follow transfers across multiple intermediary accounts and reveal patterns that are difficult to identify from isolated transaction rows.
This is particularly valuable for fraud investigation because suspicious activity may involve chains, rings, circular movements, intermediaries, or highly connected accounts rather than one anomalous transaction.
Keyword searching transaction descriptions does not analyze relationships. Summary balances discard essential connectivity information, while one-transaction-at-a-time reporting prevents efficient multi-hop analysis.
The technical requirement is therefore fundamentally graph-oriented: determine how entities are connected and analyze paths among those entities.
Study Guide reference: Working with JSON and Graph in Oracle AI Database - property graphs, vertices, edges, path analysis, and fraud-oriented relationship analysis.
質問 # 43
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?
正解：B
解説：
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.
質問 # 44
Which statement describes Artificial Intelligence?
正解：D
解説：
Artificial Intelligence describes computing systems designed to perform functions associated with human intelligence, including learning, reasoning, language understanding, recognition, prediction, and decision support. Oracle characterizes AI as computing systems trained to simulate human intelligence and notes that AI systems can learn from data, solve problems, process diverse inputs, and pursue defined objectives. Oracle also describes AI technologies as systems or machines that mimic human intelligence when performing tasks.
Data and algorithms are central to modern AI implementations. Machine-learning models learn patterns from training data, while generative models can synthesize new outputs based on learned representations and prompts. However, AI does not imply that training or grounding data is unnecessary. Nor is current enterprise AI equivalent to Artificial General Intelligence (AGI); most deployed systems remain specialized around particular tasks and workloads.
AI also does not replace database technology. Oracle AI Database instead integrates AI capabilities with relational, JSON, graph, spatial, vector, and other database models so that intelligent applications can operate directly on governed enterprise data. Oracle AI Database 26ai specifically emphasizes AI capabilities while preserving converged data-management functionality. The uploaded question source marks option A as correct.
Study Guide reference: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics - AI fundamentals and Oracle's AI-enabled database strategy.
質問 # 45
Which example is a common AI use case?
正解：D
解説：
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
Fraud detection is a standard AI use case because machine-learning and anomaly-detection models can identify unusual behavioral patterns across transaction attributes such as amount, account, merchant, location, and historical activity. Oracle documents AI-driven fraud-prevention architectures that analyze transaction behavior and flag suspicious or anomalous activity for investigation. The other options are administrative database or network tasks rather than AI inference problems. Alphabetically listing tables is ordinary metadata browsing; rotating an encryption key is deterministic security administration; and assigning a subnet CIDR is infrastructure configuration. None requires a model to learn patterns from data. Detecting fraudulent transactions from behavior patterns therefore best represents an AI workload and aligns with the Oracle AI Database foundations coverage of practical AI domains and use cases. Oracle Docs
質問 # 46
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1z0-1195-26模擬問題: https://www.jpexam.com/1z0-1195-26_exam.html