ANALYZE YOUR PROGRESS WITH DESKTOP 1Z0-184-25 PRACTICE EXAM SOFTWARE

Analyze Your Progress With Desktop 1Z0-184-25 Practice Exam Software

Analyze Your Progress With Desktop 1Z0-184-25 Practice Exam Software

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Tags: 1Z0-184-25 Reliable Braindumps Pdf, 1Z0-184-25 Valid Test Dumps, 1Z0-184-25 Exam Guide Materials, 1Z0-184-25 Exam Engine, 1Z0-184-25 Exam Torrent

Along with Oracle AI Vector Search Professional (1Z0-184-25) self-evaluation exams, 1Z0-184-25 dumps PDF is also available at PrepAwayPDF. These 1Z0-184-25 questions can be used for quick Oracle AI Vector Search Professional (1Z0-184-25) preparation. Our 1Z0-184-25 dumps PDF format works on a range of Smart devices, such as laptops, tablets, and smartphones. Since 1Z0-184-25 Questions Pdf are easily accessible, you can easily prepare for the test without time and place constraints. You can also print this format of PrepAwayPDF's Oracle AI Vector Search Professional (1Z0-184-25) exam dumps to prepare off-screen and on the go.

Oracle 1Z0-184-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 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.

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Oracle AI Vector Search Professional Sample Questions (Q24-Q29):

NEW QUESTION # 24
Which operation is NOT permitted on tables containing VECTOR columns?

  • A. UPDATE
  • B. SELECT
  • C. DELETE
  • D. JOIN ON VECTOR columns

Answer: D

Explanation:
In Oracle 23ai, tables with VECTOR columns support standard DML operations: SELECT (A) retrieves data, UPDATE (B) modifies rows, and DELETE (C) removes rows. However, JOIN ON VECTOR columns (D) is not permitted because VECTOR isn't a relational type for equality comparison; it's for similarity search (e.g., via VECTOR_DISTANCE). Joins must use non-VECTOR columns. Oracle's SQL reference restricts VECTOR to specific operations, excluding direct joins.


NEW QUESTION # 25
Which is a characteristic of an approximate similarity search in Oracle Database 23ai?

  • A. It trades off accuracy for faster performance
  • B. It always guarantees 100% accuracy
  • C. It compares every vector in the dataset
  • D. It is slower than exact similarity search

Answer: A

Explanation:
Approximate similarity search (ANN) in Oracle 23ai (B) uses indexes (e.g., HNSW, IVF) to trade accuracy for speed, returning near-matches faster by not comparing all vectors. Exact search compares every vector (A), not ANN. It doesn't guarantee 100% accuracy (C); that's exact search. It's faster, not slower (D), than exact search due to indexing. Oracle's documentation defines ANN's speed-accuracy trade-off as its hallmark.


NEW QUESTION # 26
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 # 27
How is the security interaction between Autonomous Database and OCI Generative AI managed in the context of Select AI?

  • A. By encrypting all communication between the Autonomous Database and OCI Generative AI using TLS/SSL protocols
  • B. By utilizing Resource Principals, which grant the Autonomous Database instance access to OCI Generative AI without exposing sensitive credentials
  • C. By establishing a secure VPN tunnel between the Autonomous Database and OCI Generative AI service
  • D. By requiring users to manually enter their OCI API keys each time they execute a natural language query

Answer: B

Explanation:
In Oracle Database 23ai's Select AI, security between the Autonomous Database and OCI Generative AI is managed using Resource Principals (B). This mechanism allows the database instance to authenticate itself to OCI services without hardcoding credentials, enhancing security by avoiding exposure of sensitive keys. TLS/SSL encryption (A) is used for data-in-transit security, but it's a complementary layer, not the primary management method. A VPN tunnel (C) is unnecessary within OCI's secure infrastructure and not specified for Select AI. Manual API key entry (D) is impractical and insecure for automated database interactions. Oracle's documentation on Select AI highlights Resource Principals as the secure, scalable authentication method.


NEW QUESTION # 28
What is the purpose of the Vector Pool in Oracle Database 23ai?

  • A. To enable longer SQL execution
  • B. To store non-vector data types
  • C. To manage database partitioning
  • D. To store HNSW vector indexes and IVF index metadata

Answer: D

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 # 29
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