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[General] 2026 1Z0-184-25–100% Free Exam Quizzes | Efficient Valid Oracle AI Vector Search

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【General】 2026 1Z0-184-25–100% Free Exam Quizzes | Efficient Valid Oracle AI Vector Search

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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.

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Oracle AI Vector Search Professional Sample Questions (Q30-Q35):NEW QUESTION # 30
Which is NOT a feature or capability related to AI and Vector Search in Exadata?
  • A. Loading Vector Data using SQL*Loader
  • B. Vector Replication with GoldenGate
  • C. Native Support for Vector Search Only within the Database Server
  • D. AI Smart Scan
Answer: C
Explanation:
Exadata in Oracle Database 23ai enhances AI and vector search capabilities. Vector Replication with GoldenGate (B) supports real-time vector data distribution. SQL*Loader (C) loads vector data into VECTOR columns. AI Smart Scan (D) accelerates AI workloads using Exadata's storage optimizations. However, "Native Support for Vector Search Only within the Database Server" (A) is not a feature; vector search is natively supported across Exadata's architecture, leveraging both database and storage layers (e.g., via Smart Scan), not restricted to the server alone. This option misrepresents Exadata's distributed capabilities, making it the correct "NOT" answer.

NEW QUESTION # 31
Which of the following actions will result in an error when using VECTOR_DIMENSION_COUNT() in Oracle Database 23ai?
  • A. Providing a vector with a dimensionality that exceeds the specified dimension count
  • B. Using a vector with a data type that is not supported by the function
  • C. Providing a vector with duplicate values for its components
  • D. Calling the function on a vector that has been created with TO_VECTOR()
Answer: B
Explanation:
The VECTOR_DIMENSION_COUNT() function in Oracle 23ai returns the number of dimensions in a VECTOR-type value (e.g., 512 for VECTOR(512, FLOAT32)). It's a metadata utility, not a validator of content or structure beyond type compatibility. Option B-using a vector with an unsupported data type-causes an error because the function expects a VECTOR argument; passing, say, a VARCHAR2 or NUMBER instead (e.g., '1,2,3' or 42) triggers an ORA-error (e.g., ORA-00932: inconsistent datatypes). Oracle enforces strict typing for vector functions.
Option A (exceeding specified dimensions) is a red herring; the function reports the actual dimension count of the vector, not the column's defined limit-e.g., VECTOR_DIMENSION_COUNT(TO_VECTOR('[1,2,3]')) returns 3, even if the column is VECTOR(2), as the error occurs at insertion, not here. Option C (duplicate values, like [1,1,2]) is valid; the function counts dimensions (3), ignoring content. Option D (using TO_VECTOR()) is explicitly supported; VECTOR_DIMENSION_COUNT(TO_VECTOR('[1.2, 3.4]')) returns 2 without issue. Misinterpreting this could lead developers to over-constrain data prematurely-B's type mismatch is the clear error case, rooted in Oracle's vector type system.

NEW QUESTION # 32
An application needs to fetch the top-3 matching sentences from a dataset of books while ensuring a balance between speed and accuracy. Which query structure should you use?
  • A. A combination of relational filters and similarity search
  • B. Exact similarity search with Euclidean distance
  • C. Approximate similarity search with the VECTOR_DISTANCE function
  • D. Multivector similarity search with approximate fetching and target accuracy
Answer: C
Explanation:
Fetching the top-3 matching sentences requires a similarity search, and balancing speed and accuracy points to approximate nearest neighbor (ANN) techniques. Option A-approximate similarity search with VECTOR_DISTANCE-uses an index (e.g., HNSW, IVF) to quickly find near-matches, ordered by distance (e.g., SELECT sentence, VECTOR_DISTANCE(vector, :query_vector, COSINE) AS score FROM books ORDER BY score FETCH APPROXIMATE 3 ROWS ONLY). The APPROXIMATE clause leverages indexing for speed, with tunable accuracy (e.g., TARGET_ACCURACY), ideal for large datasets where exactness is traded for performance.
Option B (exact search with Euclidean) scans all vectors without indexing, ensuring 100% accuracy but sacrificing speed-impractical for big datasets. Option C ("multivector" search) isn't a standard Oracle 23ai construct; it might imply multiple vectors per row, but lacks clarity and isn't optimal here. Option D (relational filters plus similarity) adds WHERE clauses (e.g., WHERE genre = 'fiction'), useful for scoping but not specified as needed, and doesn't inherently balance speed-accuracy without ANN. Oracle's ANN support in 23ai, via HNSW or IVF withVECTOR_DISTANCE, makes A the practical choice, aligning with real-world RAG use cases where response time matters as much as relevance.

NEW QUESTION # 33
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. Encodes the question and database chunks into vectors, finds the most similar using cosine similarity, and includes them in the LLM prompt
  • C. Trains a separate LLM on the database and uses it to answer, ignoring the general LLM
  • D. Converts the question to keywords, searches for matches, and inserts the text into the response
Answer: B
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 # 34
You need to prioritize accuracy over speed in a similarity search for a dataset of images. Which should you use?
  • A. Approximate similarity search with HNSW indexing and target accuracy of 70%
  • B. Exact similarity search using a full table scan
  • C. Multivector similarity search with partitioning
  • 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.

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