Oracle 1Z0-184-25 시험환경에 적응하고 싶은 분은 pdf버전 구매시 온라인버전 또는 테스트엔진 버전을 추가구매하시면 됩니다. 문제는 pdf버전의 문제와 같지만 pdf버전의 문제를 마스터한후 실력테스 가능한 프로그램이기에Oracle 1Z0-184-25시험환경에 익숙해져 시험을 보다 릴렉스한 상태에서 볼수 있습니다.
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최근 Oracle인증 1Z0-184-25시험이 IT업계에서 제일 높은 인지도를 가지고 있습니다.바라만 보지 마시고Oracle인증 1Z0-184-25시험에 도전해보세요. Pass4Test 의 Oracle인증 1Z0-184-25덤프로 시험준비공부를 하시면 한방에 시험패스 가능합니다. Oracle인증 1Z0-184-25덤프로 자격증취득에 가까워지고 나아가서는 IT업계에서 인정을 받는 열쇠를 소유한것과 같다고 할수 있습니다.
질문 # 54
A database administrator wants to change the VECTOR_MEMORY_SIZE parameter for a pluggable database (PDB) in Oracle Database 23ai. Which SQL command is correct?
정답:C
설명:
VECTOR_MEMORY_SIZE in Oracle 23ai controls memory allocation for vector operations (e.g., indexing, search) in the SGA. For a PDB, ALTER SYSTEM adjusts parameters, andSCOPE=BOTH (A) applies the change immediately and persists it across restarts (modifying the SPFILE). Syntax: ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=BOTH sets it to 1 GB. Option B (ALTER DATABASE) is invalid for this parameter, and SCOPE=VECTOR isn't a valid scope. Option C (SCOPE=SGA) isn't a scope value; valid scopes are MEMORY, SPFILE, or BOTH. Option D (RESET) reverts to default, not sets a value. In a PDB, this must be executed in the PDB context, not CDB, and BOTH ensures durability-key for production environments where vector workloads demand consistent memory.
질문 # 55
What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?
정답:A
질문 # 56
Which operation is NOT permitted on tables containing VECTOR columns?
정답:C
설명:
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.
질문 # 57
What is the advantage of using Euclidean Squared Distance rather than Euclidean Distance in similarity search queries?
정답:D
설명:
Euclidean Squared Distance (L2-squared) skips the square-root step of Euclidean Distance (L2), i.e., ∑(xi - yi)² vs. √∑(xi - yi)². Since the square root is monotonic, ranking order remains identical, but avoiding it (C) reduces computational cost, making queries faster-crucial for large-scale vector search. It's not the default metric (A); cosine is often default in Oracle 23ai. It doesn't relate to partitioning (B), an indexing feature. Accuracy (D) is equivalent, as rankings are preserved. Oracle's documentation notes L2-squared as an optimization for performance.
질문 # 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?
정답:B
설명:
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.
질문 # 59
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