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1Z0-184-25ๆฅๆฌ่ชpdfๅ้กใ1Z0-184-25ใใฆใณใญใผใ
1Z0-184-25่ฉฆ้จๅ้กใฎๆดๆฐใ1ๅนดไปฅๅ ใซใฏใฉใคใขใณใใซ็กๆใงๆไพใใ1ๅนดๅพใซใฏใฉใคใขใณใใฏ50๏ผ ใฎๅฒๅผใๅใใใใจใใงใใพใใใฏใฉใคใขใณใใๅคใใฏใฉใคใขใณใใฎๅ ดๅใไธๅฎใฎๅฒๅผใไบซๅใงใใพใใๅฝ็คพOracleใฎๅฐ้ๅฎถใฏใๆฏๆฅ1Z0-184-25ใฌใคใใใฌใณใใๆดๆฐใใ1Z0-184-25ในใฟใใฃใฌใคใใฎๆๆฐใฎๆดๆฐใใฏใฉใคใขใณใใซๆไพใใพใใ็งใใกใฏใฏใฉใคใขใณใใซๅฒๅผใๆไพใใๅฝผใใใใๅฐใชใใ้ใไฝฟใใใใซใใพใใใใชใใๅคใใฏใฉใคใขใณใใงใใๅ ดๅใใใชใใฏ็นๅฅๅฒๅผใไบซๅใใใใจใใงใใพใใฎใงใใ้ใ็ฏ็ดใใใใจใใงใใพใใใใใใฃใฆใ1Z0-184-25ใในใใใฌใณใใ่ณผๅ ฅใใใใจใฏ้ๅธธใซไพกๅคใใใใพใใ
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Tech4ExamใฏOracleใฎ1Z0-184-25่ชๅฎ่ฉฆ้จใซๅใใใใๅๅ้จ็ใซๆ็ขบใใค้ก่ใชใฝใชใฅใผใทใงใณใๆไพใใพใใใๅฝ็คพใฏOracleใฎ1Z0-184-25่ชๅฎ่ฉฆ้จใฎ่ฉณใใๅ้กใจ่งฃ็ญใๆไพใใพใใๅฝ็คพใฎITๅฐ้ๅฎถใๆใ็ต้จใจ่ณๆ ผใใใใใญใชไบบใ ใงใๆใ ใๆไพใใใในใใฎๅ้กใจ่งฃ็ญใฏๅฎ้ใฎ่ชๅฎ่ฉฆ้จใจๆฎใฉๅใใงใใใใใฏๆฌๅฝใซ็ด ๆดใใใใใจใงใใใใใซใใฃใจๅคงๅใชใฎใฏใTech4Examใฎใตใคใใฏไธ็็ใง1Z0-184-25่ฉฆ้จใใฌใผใใณใฐใซใใฃใฆใฎ่ฉฆ้จๅๆ ผ็ใไธ็ช้ซใใงใใ
Oracle 1Z0-184-25 ่ชๅฎ่ฉฆ้จใฎๅบ้ก็ฏๅฒ๏ผ
ใใใใฏ
ๅบ้ก็ฏๅฒ
ใใใใฏ 1
- RAGใขใใชใฑใผใทใงใณใฎๆง็ฏ๏ผใใฎใปใฏใทใงใณใงใฏใAIใฝใชใฅใผใทใงใณใขใผใญใใฏใใRAG๏ผๆค็ดขๆกๅผต็ๆ๏ผใขใใชใฑใผใทใงใณใๅฎ่ฃ ใใไธใงใฎ็ฅ่ญใ่ฉไพกใใพใใๅ้จ่ ใฏใPL
- SQLใจPythonใไฝฟ็จใใฆRAGใขใใชใฑใผใทใงใณใๆง็ฏใใAIใขใใซใจๆค็ดขๆ่กใ็ตฑๅใใใใจใงใAIไธปๅฐใฎๆๆๆฑบๅฎใๅผทๅใใๆนๆณใๅญฆ็ฟใใพใใ
ใใใใฏ 2
- ใใฏใฟใผใคใณใใใฏในใฎไฝฟ็จ๏ผใใฎใปใฏใทใงใณใงใฏใใคใณใใใฏในๆ่กใ็จใใใใฏใฟใผๆค็ดขใฎๆ้ฉๅใซใใใAIใใผใฟใใผในในใใทใฃใชในใใฎๅฐ้็ฅ่ญใ่ฉไพกใใพใใๆค็ดข้ๅบฆใๅไธใใใใใฏใฟใผใคใณใใใฏในใฎไฝๆใ็นใซAI้งๅๅใขใใชใฑใผใทใงใณใงๅน็็ใชๆค็ดขใฏใจใชใๅฎ่กใใใใใฎHNSWใใใณIVFใใฏใฟใผใคใณใใใฏในใฎไฝฟ็จใซใคใใฆ่งฃ่ชฌใใพใใ
ใใใใฏ 3
- ้ข้ฃใใAIๆฉ่ฝใฎๆดป็จ๏ผใใฎใปใฏใทใงใณใงใฏใOracleใฎAIๅผทๅๆฉ่ฝใๆดป็จใใใฏใฉใฆใAIใจใณใธใใขใฎในใญใซใ่ฉไพกใใพใใExadata AI Storageใ็จใใใใฏใใซๆค็ดขใฎ้ซ้ๅใSelect AI with Autonomousใ็จใใ่ช็ถ่จ่ชใซใใใใผใฟใฏใจใชใSQL LoaderใจOracle Data Pumpใ็จใใใใผใฟใญใผใๆ่กใซใใAI้งๅๅใฏใผใฏใใญใผใฎๅน็ๅใชใฉใซใคใใฆ่งฃ่ชฌใใพใใ
ใใใใฏ 4
- ใใฏใใซๅใ่พผใฟใฎไฝฟ็จ๏ผใใฎใปใฏใทใงใณใงใฏใAI้็บ่ ใAIใขใใชใฑใผใทใงใณๅใใใฏใใซๅใ่พผใฟใ็ๆใปไฟๅญใใ่ฝๅใ่ฉไพกใใพใใOracleใใผใฟใใผในใฎๅ ๅคไธกๆนใงๅใ่พผใฟใ็ๆใใใใผใฟใใผในๅ ใซๅนๆ็ใซไฟๅญใใฆๅน็็ใชๆค็ดขใปๅฆ็ใๅฎ็พใใๆนๆณใซใคใใฆ่งฃ่ชฌใใพใใ
ใใใใฏ 5
- ้กไผผๆค็ดขใฎๅฎ่ก๏ผใใฎใปใฏใทใงใณใงใฏใๆฉๆขฐๅญฆ็ฟใจใณใธใใขใ้กไผผๆค็ดขใๅฎๆฝใใฆ้ข้ฃใใผใฟใใคใณใใ่ฆใคใใในใญใซใใในใใใพใใใใฏใใซใคใณใใใฏในใ็จใใๆญฃ็ขบใช้กไผผๆค็ดขใจ่ฟไผผ้กไผผๆค็ดขใฎๅฎ่กใๅซใพใใพใใใพใใ่คๆฐใฎใใญใฅใกใณใใซใพใใใๆค็ดขใๅฆ็ใใใใซใใใฏใใซ้กไผผๆค็ดขใๆฑใใๆค็ดข็ฒพๅบฆใๅไธใใใพใใ
ย
Oracle AI Vector Search Professional ่ชๅฎ 1Z0-184-25 ่ฉฆ้จๅ้ก (Q34-Q39):
่ณชๅ # 34
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. Increase the VECTOR_MEMORY_SIZE initialization parameter
- B. Re-create the index with a higher EFCONSTRUCTION value
- C. Add the TARGET_ACCURACY clause to the query with a higher value for the accuracy
- D. Change the index type to HNSW for better accuracy
ๆญฃ่งฃ๏ผC
่งฃ่ชฌ๏ผ
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.
ย
่ณชๅ # 35
Which SQL statement correctly adds a VECTOR column named "v" with 4 dimensions and FLOAT32 format to an existing table named "my_table"?
- A. UPDATE my_table SET v = VECTOR(4, FLOAT32)
- B. ALTER TABLE my_table ADD v VECTOR(4, FLOAT32)
- C. ALTER TABLE my_table ADD (v VECTOR(4, FLOAT32))
- D. ALTER TABLE my_table MODIFY (v VECTOR(4, FLOAT32))
ๆญฃ่งฃ๏ผC
่งฃ่ชฌ๏ผ
To add a new column to an existing table, Oracle uses the ALTER TABLE statement with the ADD clause. Option B, ALTER TABLE my_table ADD (v VECTOR(4, FLOAT32)), correctly specifies the column name "v", the VECTOR type, and its attributes (4 dimensions, FLOAT32 precision) within parentheses, aligning with Oracle's DDL syntax for VECTOR columns. Option A uses MODIFY, which alters existing columns, not adds new ones, making it incorrect here. Option C uses UPDATE, a DML statement for updating data, not a DDL operation for schema changes. Option D omits parentheses around the VECTOR specification, which is syntactically invalid as Oracle requires dimensions and format to be enclosed. The SQL Language Reference confirms this syntax for adding VECTOR columns.
ย
่ณชๅ # 36
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. Perform the similarity search without a WHERE clause
- B. Use UNION ALL with vector operations
- C. Apply relational filters and a similarity search in the query
- D. Use VECTOR_INDEX_HINT and NO WHERE clause
ๆญฃ่งฃ๏ผC
่งฃ่ชฌ๏ผ
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.
ย
่ณชๅ # 37
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
ๆญฃ่งฃ๏ผD
่งฃ่ชฌ๏ผ
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.
ย
่ณชๅ # 38
Which Oracle Cloud Infrastructure (OCI) service is directly integrated with Select AI?
- A. OCI Data Science
- B. OCI Vision
- C. OCI Generative AI
- D. OCI Language
ๆญฃ่งฃ๏ผC
่งฃ่ชฌ๏ผ
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.
ย
่ณชๅ # 39
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1Z0-184-25่ชๅฎใฏใใใชใใฎ่ฝๅใฎๆ้ซใฎ่จผๆใงใใใใ ใใใใฎใใใช1Z0-184-25่ฉฆ้จใๆบๅใใ่ช็ฑๆ้ใๅฐใชใไฝๆฅญๆ ๅฝ่ ใซใจใฃใฆใฏๅฎนๆใงใฏใชใใไบบใ ใฏๅธธใซๆช็ฅใฎใใฎใซๅฏพใใๆๆใๆใใ็ช็ถใฎๅคๅใซๅฏพๅฆใใใใจใฏใงใใพใใใใใ ใใ1Z0-184-25่ฉฆ้จๅ้กใฏใใชใใฎใใฐใซ็ซใคใใจใใงใใพใใใใใฆใๅชใใ1Z0-184-25ๅญฆ็ฟๆๆใๆไพใใใใจใซๅฐๅฟตใใๆฑบๆใงใใ 1Z0-184-25่ฉฆ้จๅ้กใฎ็กๆใใขใใ่ฉฆใใใ ใใใ่ฉณ็ดฐใ็่งฃใใฆ้ธๆใใใใจใใงใใพใใ
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