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- Exam Code: 1z0-1195-26
- Exam Name: Oracle AI Database Foundations Associate
- Q & A: 54 Questions and Answers
- PDF Price: $59.99
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- Exam Code: 1z0-1195-26
- Exam Name: Oracle AI Database Foundations Associate
- Q & A: 54 Questions and Answers
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Oracle 1z0-1195-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Building Low-Code Applications and Agentic AI | 10% | - Choose the appropriate Agent Factory capability for a no-code AI agent use case - Describe Oracle APEX as Oracle's low-code platform |
| Using Oracle Database Actions and Data Studio Tools | 15% | - 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 | 15% | - 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 |
| Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | 20% | - 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 |
| Working with JSON and Graph in Oracle AI Database | 20% | - Describe core graph concepts and graph analytic capabilities - Distinguish when graph capabilities and Property Graph Views fit a business use case - Explain JSON and Oracle AI Database JSON capabilities |
| Implementing Select AI and AI Vector Search in Autonomous AI Database | 20% | - 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 |
Oracle AI Database Foundations Associate Sample Questions:
Question #1
Which pair correctly matches an AI domain to an example?
- A. Speech - product demand prediction
- B. Forecasting - entity extraction from text
- C. Vision - image classification
- D. Language - object detection in photos
Correct Answer: C 🗳️
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Question #2
A team needs faster similarity search at scale and accepts approximate top-K results. Which feature should they use?
- A. A JSON duality view with update restrictions
- B. A standard B-tree index optimized for exact match and range queries
- C. A graph view with ONE ROW PER STEP
- D. A vector index with approximate search
Correct Answer: D 🗳️
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Question #3
Which task is a common use of graph analytics?
- A. Listing unrelated records without evaluating relationships
- B. Storing each relationship as an isolated text value with no links
- C. Determining communities or connected clusters in a network
- D. Sorting records only by a single numeric column value
Correct Answer: C 🗳️
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Question #4
A data analyst wants to discover data, load it into the platform, and prepare it for later use without leaving the built-in tools. Which workspace fits this task?
- A. VECTOR_DISTANCE function only
- B. Property Graph View definition
- C. Data Studio
- D. OCI Vault
Correct Answer: C 🗳️
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Question #5
A bank wants to identify indirect money movement patterns across several accounts to support fraud investigation. Which approach fits this requirement?
- A. Model accounts as vertices and transfers as edges in a property graph
- B. Store transaction descriptions as flat text and search by keywords only
- C. Store only summary totals for each account without relationship data
- D. Use a relational report that shows one transaction at a time without link analysis
Correct Answer: A 🗳️
Explanation: Only visible for ValidBraindumps members. You can sign-up / login (it's free).
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