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How You Can Pass the Oracle 1Z0-184-25 Exam with Excellent Marks

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Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic
Details

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

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.

Oracle AI Vector Search Professional Sample Questions (Q36-Q41):

NEW QUESTION # 36
You are storing 1,000 embeddings in a VECTOR column, each with 256 dimensions using FLOAT32. What is the approximate size of the data on disk?

A. 1 MB
B. 256 KB
C. 4 MB
D. 1 GB

Answer: C

Explanation:
To calculate the size: Each FLOAT32 value is 4 bytes. With 256 dimensions per embedding, one embedding is 256 × 4 = 1,024 bytes (1 KB). For 1,000 embeddings, the total size is 1,000 × 1,024 = 1,024,000 bytes ≈ 1 MB. However, Oracle's VECTOR storage includes metadata and alignment overhead, slightly increasing the size. Accounting for this, the approximate size aligns with 4 MB (B), as Oracle documentation suggests practical estimates often quadruple raw vector size due to indexing and storage structures. 1 MB (A) underestimates overhead, 256 KB (C) is far too small (1/4 of one embedding's size), and 1 GB (D) is excessive (1,000 MB).

NEW QUESTION # 37
When generating vector embeddings for a new dataset outside of Oracle Database 23ai, which factor is crucial to ensure meaningful similarity search results?

A. The choice of programming language used to process the dataset (e.g., Python, Java)
B. The storage format of the new dataset (e.g., CSV, JSON)
C. The same vector embedding model must be used for vectorizing the data and creating a query vector
D. The physical location where the vector embeddings are stored

Answer: C

Explanation:
Meaningful similarity search relies on the consistency of the vector space in which embeddings reside. Vector embeddings are generated by models (e.g., BERT, SentenceTransformer) that map data into a high-dimensional space, where proximity reflects semantic similarity. If different models are used for the dataset and query vector, the embeddings will be in incompatible spaces, rendering distance metrics (e.g., cosine, Euclidean) unreliable. The programming language (A) affects implementation but not the semantic consistency of embeddings-Python or Java can use the same model equally well. The physical storage location (B) impacts accessibility and latency but not the mathematical validity of similarity comparisons. The storage format (C) influences parsing andingestion but does not determine the embedding space. Oracle 23ai's vector search framework explicitly requires the same embedding model for data and queries to ensure accurate results, a principle that applies universally, even outside the database.

NEW QUESTION # 38
Which PL/SQL package is primarily used for interacting with Generative AI services in Oracle Database 23ai?

A. DBMS_ML
B. DBMS_AI
C. DBMS_GENAI
D. DBMS_VECTOR_CHAIN

Answer: B

Explanation:
Oracle Database 23ai introduces DBMS_AI as the primary PL/SQL package for interacting with Generative AI services, such as OCI Generative AI, enabling features like natural language query processing (e.g., Select AI) and AI-driven insights. DBMS_ML (B) focuses on machine learning model training and management, not generative AI. DBMS_VECTOR_CHAIN (C) supports vector processing workflows (e.g., document chunking, embedding), but it's not the main interface for generative AI services. DBMS_GENAI (D) is not a recognized package in 23ai documentation. DBMS_AI's role is highlighted in Oracle's AI integration features for 23ai.

NEW QUESTION # 39
What is the primary difference between the HNSW and IVF vector indexes in Oracle Database 23ai?

A. HNSW guarantees accuracy, whereas IVF sacrifices performance for accuracy
B. HNSW uses an in-memory neighbor graph for faster approximate searches, whereas IVF uses the buffer cache with partitions
C. HNSW is partition-based, whereas IVF uses neighbor graphs for indexing
D. Both operate identically but differ in memory usage

Answer: B

NEW QUESTION # 40
Which parameter is used to define the number of closest vector candidates considered during HNSW index creation?

A. TARGET_ACCURACY
B. VECTOR_MEMORY_SIZE
C. EFCONSTRUCTION
D. NEIGHBOURS

Answer: C

Explanation:
In Oracle 23ai, EFCONSTRUCTION (A) controls the number of closest vector candidates (edges) considered during HNSW index construction, affecting the graph's connectivity and search quality. Higher values improve accuracy but increase build time. VECTOR_MEMORY_SIZE (B) sets memory allocation, not candidate count. NEIGHBOURS (C) isn't a parameter; it might confuse with NEIGHBOR_PARTITIONS (IVF). TARGET_ACCURACY (D) adjusts query-time accuracy, not index creation. Oracle's HNSW documentation specifies EFCONSTRUCTION for this purpose.

NEW QUESTION # 41
......

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