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[Hardware] Reliable 1Z0-184-25 Exam Questions | 1Z0-184-25 Exams

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【Hardware】 Reliable 1Z0-184-25 Exam Questions | 1Z0-184-25 Exams

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Oracle 1Z0-184-25 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Using Vector Embeddings: This section measures the abilities of AI Developers in generating and storing vector embeddings for AI applications. It covers generating embeddings both inside and outside the Oracle database and effectively storing them within the database for efficient retrieval and processing.
Topic 2
  • Understand Vector Fundamentals: This section of the exam measures the skills of Data Engineers in working with vector data types for storing embeddings and enabling semantic queries. It covers vector distance functions and metrics used in AI vector search. Candidates must demonstrate proficiency in performing DML and DDL operations on vectors to manage data efficiently.
Topic 3
  • Leveraging Related AI Capabilities: This section evaluates the skills of Cloud AI Engineers in utilizing Oracle’s AI-enhanced capabilities. It covers the use of Exadata AI Storage for faster vector search, Select AI with Autonomous for querying data using natural language, and data loading techniques using SQL Loader and Oracle Data Pump to streamline AI-driven workflows.
Topic 4
  • Performing Similarity Search: This section tests the skills of Machine Learning Engineers in conducting similarity searches to find relevant data points. It includes performing exact and approximate similarity searches using vector indexes. Candidates will also work with multi-vector similarity search to handle searches across multiple documents for improved retrieval accuracy.
Topic 5
  • Building a RAG Application: This section assesses the knowledge of AI Solutions Architects in implementing retrieval-augmented generation (RAG) applications. Candidates will learn to build RAG applications using PL
  • SQL and Python to integrate AI models with retrieval techniques for enhanced AI-driven decision-making.

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Oracle AI Vector Search Professional Sample Questions (Q32-Q37):NEW QUESTION # 32
What is the default distance metric used by the VECTOR_DISTANCE function if none is specified?
  • A. Euclidean
  • B. Cosine
  • C. Hamming
  • D. Manhattan
Answer: B
Explanation:
The VECTOR_DISTANCE function in Oracle 23ai computes vector distances, and if no metric is specified (e.g., VECTOR_DISTANCE(v1, v2)), it defaults to Cosine (C). Cosine distance (1 - cosine similarity) is widely used for text embeddings due to its focus on angular separation, ignoring magnitude-fitting for normalized vectors from models like BERT. Euclidean (A) measures straight-line distance, not default. Hamming (B) is for binary vectors, rare in 23ai's FLOAT32 context. Manhattan (D) sums absolute differences, less common for embeddings. Oracle's choice of Cosine reflects its AI focus, as documentation confirms, aligning with industry norms for semantic similarity-vital for users assuming defaults in queries.

NEW QUESTION # 33
What is the correct order of steps for building a RAG application using PL/SQL in Oracle Database 23ai?
  • A. Vectorize Question, Load ONNX Model, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output
  • B. Load Document, Load ONNX Model, Split Text into Chunks, Create Embeddings, VectorizeQuestion, Perform Vector Search, Generate Output
  • C. Load Document, Split Text into Chunks, Load ONNX Model, Create Embeddings, Vectorize Question, Perform Vector Search, Generate Output
  • D. Load ONNX Model, Vectorize Question, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output
Answer: C
Explanation:
Building a RAG application in Oracle 23ai using PL/SQL follows a logical sequence: (1) Load Document (e.g., via SQL*Loader) into the database; (2) Split Text into Chunks (e.g., DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS) to manage token limits; (3) Load ONNX Model (e.g., via DBMS_VECTOR) for embedding generation; (4) Create Embeddings (e.g., UTL_TO_EMBEDDINGS) for the chunks; (5) Vectorize Question (using the same model) when a query is received; (6) Perform Vector Search (e.g., VECTOR_DISTANCE) to find relevant chunks; (7) Generate Output (e.g., via DBMS_AI with an LLM). Option B matches this flow. A starts with the model prematurely. C prioritizes the question incorrectly. D is close but loads the model too early. Oracle's RAG workflow documentation outlines this document-first approach.

NEW QUESTION # 34
What is the primary difference between the HNSW and IVF vector indexes in Oracle Database 23ai?
  • A. HNSW is partition-based, whereas IVF uses neighbor graphs for indexing
  • B. HNSW guarantees accuracy, whereas IVF sacrifices performance for accuracy
  • C. HNSW uses an in-memory neighbor graph for faster approximate searches, whereas IVF uses the buffer cache with partitions
  • D. Both operate identically but differ in memory usage
Answer: C

NEW QUESTION # 35
What is the purpose of the Vector Pool in Oracle Database 23ai?
  • A. To store HNSW vector indexes and IVF index metadata
  • B. To enable longer SQL execution
  • C. To store non-vector data types
  • D. To manage database partitioning
Answer: A
Explanation:
The Vector Pool in Oracle 23ai is a dedicated SGA memory region (controlled by VECTOR_MEMORY_SIZE) for vector operations, specifically storing HNSW indexes (graph structures) and IVF index metadata (e.g., centroids) (B). This optimizes memory usage for vector search, keeping critical index data accessible for fast queries. Partitioning (A) is unrelated; that's a tablespace feature. Longer SQL execution (C) might benefit indirectly from memory efficiency, but it's not the purpose. Non-vector data (D) resides elsewhere (e.g., PGA, buffer cache). Oracle allocates the Vector Pool to enhance AI workloads, ensuring indexes don't compete with other memory, a design choice reflecting vector search's growing importance.

NEW QUESTION # 36
You want to quickly retrieve the top-10 matches for a query vector from a dataset of billions of vectors, prioritizing speed over exact accuracy. What is the best approach?
  • A. Exact similarity search with a high target accuracy setting
  • B. Relational filtering combined with an exact search
  • C. Approximate similarity search with a low target accuracy setting
  • D. Exact similarity search using flat search
Answer: C
Explanation:
For speed over accuracy with billions of vectors, approximate similarity search (ANN) with a low target accuracy setting (B) (e.g., 70%) uses indexes like HNSW or IVF, probing fewer vectors to return top-10 matches quickly. Exact flat search (A) scans all vectors, too slow for billions. Relational filtering with exact search (C) adds overhead without speed gains. Exact search with high accuracy (D) maximizes precision but sacrifices speed. Oracle's documentation recommends ANN for large-scale, speed-focused queries.

NEW QUESTION # 37
......
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