1Z0-184-25 PRINTABLE PDF & 1Z0-184-25 VALID BRAINDUMPS EBOOK

1Z0-184-25 Printable PDF & 1Z0-184-25 Valid Braindumps Ebook

1Z0-184-25 Printable PDF & 1Z0-184-25 Valid Braindumps Ebook

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Tags: 1Z0-184-25 Printable PDF, 1Z0-184-25 Valid Braindumps Ebook, Valid 1Z0-184-25 Test Notes, 1Z0-184-25 Test Sample Online, Relevant 1Z0-184-25 Questions

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Oracle 1Z0-184-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Using Vector Indexes: This section evaluates the expertise of AI Database Specialists in optimizing vector searches using indexing techniques. It covers the creation of vector indexes to enhance search speed, including the use of HNSW and IVF vector indexes for performing efficient search queries in AI-driven applications.
Topic 2
  • 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.
Topic 3
  • 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 4
  • 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.

Oracle AI Vector Search Professional Sample Questions (Q56-Q61):

NEW QUESTION # 56
What security enhancement is introduced in Exadata System Software 24ai?

  • A. SNMP security (Security Network Management Protocol)
  • B. Integration with third-party security tools
  • C. Enhanced encryption algorithm for data at rest

Answer: C

Explanation:
Exadata System Software 24ai (noted in context beyond 23ai) introduces an enhanced encryption algorithm for data at rest (B), strengthening security for stored data, including vectors. Third-party integration (A) isn't highlighted as a 24ai feature. SNMP security (C) relates to network monitoring, not a primary Exadata enhancement. Oracle's Exadata documentation for 24ai emphasizes advanced encryption as a key security upgrade.


NEW QUESTION # 57
Which vector index available in Oracle Database 23ai is known for its speed and accuracy, making it a preferred choice for vector search?

  • A. Inverted File System (IFS) index
  • B. Binary Tree (BT) index
  • C. Inverted File (IVF) index
  • D. Hierarchical Navigable Small World (HNSW) index

Answer: D

Explanation:
Oracle 23ai supports two main vector indexes: IVF and HNSW. HNSW (D) is renowned for its speed and accuracy, using a hierarchical graph to connect vectors, enabling fast ANN searches with high recall-ideal for latency-sensitive applications like real-time RAG. IVF (C) partitions vectors for scalability but often requires tuning (e.g., NEIGHBOR_PARTITIONS) to match HNSW's accuracy, trading off recall for memory efficiency. BT (A) isn't a 23ai vector index; it's a generic term unrelated here. IFS (B) seems a typo for IVF; no such index exists. HNSW's graph structure outperforms IVF in small-to-medium datasets or where precision matters, as Oracle's documentation and benchmarks highlight, making it a go-to for balanced performance.


NEW QUESTION # 58
What is the significance of using local ONNX models for embedding within the database?

  • A. Improved accuracy compared to external models
  • B. Enhanced security because data remains within the database
  • C. Support for legacy SQL*Plus clients
  • D. Reduced embedding dimensions for faster processing

Answer: B

Explanation:
Using local ONNX (Open Neural Network Exchange) models for embedding within Oracle Database 23ai means loading pre-trained models (e.g., via DBMS_VECTOR) into the database to generate vectors internally, rather than relying on external APIs or services. The primary significance is enhanced security (D): sensitive data (e.g., proprietary documents) never leaves the database, avoiding exposure to external networks or third-party providers. This aligns with enterprise needs for data privacy and compliance (e.g., GDPR), as the embedding process-say, converting "confidential report" to a vector-occurs within Oracle's secure environment, leveraging its encryption and access controls.
Option A (SQLPlus support) is irrelevant; ONNX integration is about AI functionality, not legacy client compatibility-SQLPlus can query vectors regardless. Option B (improved accuracy) is misleading; accuracy depends on the model's training, not its location-local vs. external models could be identical (e.g., same BERT variant). Option C (reduced dimensions) is a misconception; dimensionality is model-defined (e.g., 768 for BERT), not altered by locality-processing speed might improve due to reduced latency, but that's secondary. Security is the standout benefit, as Oracle's documentation emphasizes in-database processing to minimize data egress risks, a critical consideration for RAG or Select AI workflows where private data fuels LLMs. Without this, external calls could leak context, undermining trust in AI applications.


NEW QUESTION # 59
You need to prioritize accuracy over speed in a similarity search for a dataset of images. Which should you use?

  • A. Multivector similarity search with partitioning
  • B. Exact similarity search using a full table scan
  • C. Approximate similarity search with HNSW indexing and target accuracy of 70%
  • D. Approximate similarity search with IVF indexing and target accuracy of 70%

Answer: B

Explanation:
To prioritize accuracy over speed, exact similarity search with a full table scan (C) computes distances between the query vector and all stored vectors, guaranteeing 100% recall without approximation trade-offs. HNSW with 70% target accuracy (A) and IVF with 70% (D) are approximate methods, sacrificing accuracy for speed via indexing (e.g., probing fewer neighbors). Multivector search (B) isn't a standard Oracle 23ai term; partitioning aids scale, not accuracy. Exact search, though slower, ensures maximum accuracy, as per Oracle's vector search options.


NEW QUESTION # 60
A database administrator wants to change the VECTOR_MEMORY_SIZE parameter for a pluggable database (PDB) in Oracle Database 23ai. Which SQL command is correct?

  • A. ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=SGA
  • B. ALTER SYSTEM RESET VECTOR_MEMORY_SIZE
  • C. ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=BOTH
  • D. ALTER DATABASE SET VECTOR_MEMORY_SIZE=1G SCOPE=VECTOR

Answer: C

Explanation:
VECTOR_MEMORY_SIZE in Oracle 23ai controls memory allocation for vector operations (e.g., indexing, search) in the SGA. For a PDB, ALTER SYSTEM adjusts parameters, andSCOPE=BOTH (A) applies the change immediately and persists it across restarts (modifying the SPFILE). Syntax: ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=BOTH sets it to 1 GB. Option B (ALTER DATABASE) is invalid for this parameter, and SCOPE=VECTOR isn't a valid scope. Option C (SCOPE=SGA) isn't a scope value; valid scopes are MEMORY, SPFILE, or BOTH. Option D (RESET) reverts to default, not sets a value. In a PDB, this must be executed in the PDB context, not CDB, and BOTH ensures durability-key for production environments where vector workloads demand consistent memory.


NEW QUESTION # 61
......

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