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Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic	Details
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	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 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.

Oracle AI Vector Search Professional Sample Questions (Q58-Q63):

NEW QUESTION # 58
How does an application use vector similarity search to retrieve relevant information from a database, and how is this information then integrated into the generation process?

A. Clusters similar text chunks and randomly selects one from the most relevant cluster
B. Converts the question to keywords, searches for matches, and inserts the text into the response
C. Trains a separate LLM on the database and uses it to answer, ignoring the general LLM
D. Encodes the question and database chunks into vectors, finds the most similar using cosine similarity, and includes them in the LLM prompt

Answer: D

Explanation:
In Oracle 23ai's RAG framework, vector similarity search (A) encodes a user question and database chunks into vectors (e.g., via VECTOR_EMBEDDING), computes similarity (e.g., cosine via VECTOR_DISTANCE), and retrieves the most relevant chunks. These are then included in the LLM prompt, augmenting its response with context. Training a separate LLM (B) is not RAG; RAG uses existing models. Keyword search (C) is traditional, not vector-based, and less semantic. Clustering and random selection (D) lacks precision and isn't RAG's approach. Oracle's documentation describes this encode-search-augment process as RAG's core mechanism.

NEW QUESTION # 59
What is the primary purpose of a similarity search in Oracle Database 23ai?

A. To find exact matches in BLOB data
B. To retrieve the most semantically similar entries using distance metrics between different vectors
C. To group vectors by their exact scores
D. Optimize relational database operations to compute distances between all data points in a database

Answer: B

Explanation:
Similarity search in Oracle 23ai (C) uses vector embeddings in VECTOR columns to retrieve entries semantically similar to a query vector, based on distance metrics (e.g., cosine, Euclidean) via functions like VECTOR_DISTANCE. This is key for AI applications like RAG, finding "close" rather than exact matches. Optimizing relational operations (A) is unrelated; similarity search is vector-specific. Exact matches in BLOBs (B) don't leverage vector semantics. Grouping by scores (D) is a post-processing step, not the primary purpose. Oracle's documentation defines similarity search as retrieving semantically proximate vectors.

NEW QUESTION # 60
What happens when querying with an IVF index if you increase the value of the NEIGHBOR_PARTITIONS probes parameter?

A. More partitions are probed, improving accuracy, but also increasing query latency
B. The number of centroids decreases
C. Accuracy decreases
D. Index creation time is reduced

Answer: A

Explanation:
The NEIGHBOR_PARTITIONS parameter in Oracle 23ai's IVF index controls how many partitions are probed during a query. Increasing this value examines more clusters, raising theprobability of finding relevant vectors, thus improving accuracy (recall). However, this increases computational effort, leading to higher query latency-a classic ANN trade-off. The number of centroids (A) is fixed during index creation and unaffected by query parameters. Accuracy does not decrease (B); it improves. Index creation time (C) is unrelated to query-time settings. Oracle's documentation on IVF confirms that NEIGHBOR_PARTITIONS directly governs this accuracy-latency balance.

NEW QUESTION # 61
Which Oracle Cloud Infrastructure (OCI) service is directly integrated with Select AI?

A. OCI Language
B. OCI Vision
C. OCI Data Science
D. OCI Generative AI

Answer: D

Explanation:
Select AI in Oracle Database 23ai integrates with OCI Generative AI (B) to process natural language queries and generate context-aware responses using large language models (LLMs). OCI Language (A) focuses on text analysis (e.g., sentiment, entity recognition), not generative tasks. OCI Vision (C) handles image processing, unrelated to Select AI's text-based functionality. OCI Data Science (D) supports model development, not direct integration with Select AI. Oracle's documentation explicitly names OCI Generative AI as the integrated service for Select AI's LLM capabilities.

NEW QUESTION # 62
What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?

A. To calculate vector distances
B. To calculate vector dimensions
C. To serialize vectors into a string
D. To generate a single vector embedding for data

Answer: D

NEW QUESTION # 63
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Online 1Z0-184-25 Training Materials & 1Z0-184-25 Test Simulator Online

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Oracle 1Z0-184-25 Exam Syllabus Topics:

Oracle AI Vector Search Professional Sample Questions (Q58-Q63):

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