100% PASS ORACLE - HIGH PASS-RATE LATEST 1Z0-184-25 TEST NOTES

100% Pass Oracle - High Pass-Rate Latest 1Z0-184-25 Test Notes

100% Pass Oracle - High Pass-Rate Latest 1Z0-184-25 Test Notes

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Oracle AI Vector Search Professional Sample Questions (Q55-Q60):

NEW QUESTION # 55
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. 4 MB
  • B. 1 GB
  • C. 1 MB
  • D. 256 KB

Answer: A

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 # 56
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. Encodes the question and database chunks into vectors, finds the most similar using cosine similarity, and includes them in the LLM prompt
  • B. Converts the question to keywords, searches for matches, and inserts the text into the response
  • C. Clusters similar text chunks and randomly selects one from the most relevant cluster
  • D. Trains a separate LLM on the database and uses it to answer, ignoring the general LLM

Answer: A

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 # 57
You are asked to fetch the top five vectors nearest to a query vector, but only for a specific category of documents. Which query structure should you use?

  • A. Use VECTOR_INDEX_HINT and NO WHERE clause
  • B. Use UNION ALL with vector operations
  • C. Perform the similarity search without a WHERE clause
  • D. Apply relational filters and a similarity search in the query

Answer: D

Explanation:
To fetch the top five nearest vectors for a specific category, combine relational filtering (e.g., WHERE category = 'X') with similarity search (C) (e.g., VECTOR_DISTANCE with ORDER BY and FETCH FIRST 5 ROWS). UNION ALL (A) is for combining result sets, not filtering. Omitting WHERE (B) ignores the category constraint. VECTOR_INDEX_HINT (D) influences index usage, not filtering, and skipping WHERE misses the requirement. Oracle's vector search examples use WHERE clauses with similarity functions for such tasks.


NEW QUESTION # 58
A machine learning team is using IVF indexes in Oracle Database 23ai to find similar images in a large dataset. During testing, they observe that the search results are often incomplete, missing relevant images. They suspect the issue lies in the number of partitions probed. How should they improve the search accuracy?

  • A. Re-create the index with a higher EFCONSTRUCTION value
  • B. Increase the VECTOR_MEMORY_SIZE initialization parameter
  • C. Change the index type to HNSW for better accuracy
  • D. Add the TARGET_ACCURACY clause to the query with a higher value for the accuracy

Answer: D

Explanation:
IVF (Inverted File) indexes in Oracle 23ai partition vectors into clusters, probing a subset during queries for efficiency. Incomplete results suggest insufficient partitions are probed, reducing recall. The TARGET_ACCURACY clause (A) allows users to specify a desired accuracy percentage (e.g., 90%), dynamically increasing the number of probed partitions to meet this target, thus improving accuracy at the cost of latency. Switching to HNSW (B) offers higher accuracy but requires re-indexing and may not be necessary if IVF tuning suffices. Increasing VECTOR_MEMORY_SIZE (C) allocates more memory for vector operations but doesn't directly affect probe count. EFCONSTRUCTION (D) is an HNSW parameter, irrelevant to IVF. Oracle's IVF documentation highlights TARGET_ACCURACY as the recommended tuning mechanism.


NEW QUESTION # 59
What is the function of the COSINE parameter in the SQL query used to retrieve similar vectors?
topk = 3
sql = f"""select payload, vector_distance(vector, :vector, COSINE) as score from {table_name} order by score fetch approximate {topk} rows only"""

  • A. It filters out vectors with a cosine similarity below a certain threshold
  • B. It indicates that the cosine distance metric should be used to measure similarity between vectors
  • C. It specifies the type of vector encoding used in the database
  • D. It converts the vectors to a format compatible with the SQL database

Answer: B

Explanation:
In Oracle Database 23ai, the VECTOR_DISTANCE function calculates the distance between two vectors using a specified metric. The COSINE parameter in the query (vector_distance(vector, :vector, COSINE)) instructs the database to use the cosine distance metric (C) to measure similarity. Cosine distance, defined as 1 - cosine similarity, is ideal for high-dimensional vectors (e.g., text embeddings) as it focuses on angular separation rather than magnitude. It doesn't filter vectors (A); filtering requires additional conditions (e.g., WHERE clause). It doesn't convert vector formats (B); vectors are already in the VECTOR type. It also doesn't specify encoding (D), which is defined during vector creation (e.g., FLOAT32). Oracle's documentation confirms COSINE as one of the supported metrics for similarity search.


NEW QUESTION # 60
......

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