1Z0-184-25試験問題集 & 1Z0-184-25トレーニング資料

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試験の準備方法-効果的な1Z0-184-25試験問題集試験-検証する1Z0-184-25トレーニング資料

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Oracle 1Z0-184-25 認定試験の出題範囲：

トピック	出題範囲
トピック 1	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.
トピック 2	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.
トピック 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 認定 1Z0-184-25 試験問題 (Q44-Q49):

質問 # 44
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?

A. ALTER DATABASE SET VECTOR_MEMORY_SIZE=1G SCOPE=VECTOR
B. ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=SGA
C. ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=BOTH
D. ALTER SYSTEM RESET VECTOR_MEMORY_SIZE

正解：C

解説：
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.

質問 # 45
What happens when querying with an IVF index if you increase the value of the NEIGHBOR_PARTITIONS probes parameter?

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

正解：B

解説：
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.

質問 # 46
What is the default distance metric used by the VECTOR_DISTANCE function if none is specified?

A. Euclidean
B. Manhattan
C. Cosine
D. Hamming

正解：C

解説：
The VECTOR_DISTANCE function in Oracle 23ai computes vector distances, and if no metric is specified (e.g., VECTOR_DISTANCE(v1, v2)), it defaults to Cosine (C). Cosine distance (1 - cosine similarity) is widely used for text embeddings due to its focus on angular separation, ignoring magnitude-fitting for normalized vectors from models like BERT. Euclidean (A) measures straight-line distance, not default. Hamming (B) is for binary vectors, rare in 23ai's FLOAT32 context. Manhattan (D) sums absolute differences, less common for embeddings. Oracle's choice of Cosine reflects its AI focus, as documentation confirms, aligning with industry norms for semantic similarity-vital for users assuming defaults in queries.

質問 # 47
You need to generate a vector from the string '[1.2, 3.4]' in FLOAT32 format with 2 dimensions. Which function will you use?

A. FROM_VECTOR
B. TO_VECTOR
C. VECTOR_SERIALIZE
D. VECTOR_DISTANCE

正解：B

解説：
In Oracle Database 23ai, the TO_VECTOR function (A) converts a string representation of a vector (e.g., '[1.2, 3.4]') into a VECTOR data type with specified format (e.g., FLOAT32) and dimensions (here, 2). It's designed for creating vectors from text input, matching the requirement. VECTOR_DISTANCE (B) calculates distances between vectors, not generates them.FROM_VECTOR (C) isn't a documented function; it might be confused with serialization or extraction, but it's not standard. VECTOR_SERIALIZE (D) converts a vector to a string, the opposite of what's needed. Oracle's SQL reference confirms TO_VECTOR for this purpose, parsing the string into a 2D FLOAT32 vector.

質問 # 48
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 converts the vectors to a format compatible with the SQL database
B. It specifies the type of vector encoding used in the database
C. It indicates that the cosine distance metric should be used to measure similarity between vectors
D. It filters out vectors with a cosine similarity below a certain threshold

正解：C

解説：
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.

質問 # 49
......

Oracle　1Z0-184-25学習教材を選んだら、1Z0-184-25試験に落ちた人は少ないです。何故というと、1Z0-184-25学習教材の合格率が高いからです。1Z0-184-25学習教材は多くの人から好評をもらいました。そのほかに、1Z0-184-25学習教材は三種類があります。自分の好みによって選択できます。とても便利で、使い安いです。

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1Z0-184-25試験問題集 & 1Z0-184-25トレーニング資料

試験の準備方法-効果的な1Z0-184-25試験問題集試験-検証する1Z0-184-25トレーニング資料

Oracle 1Z0-184-25 認定試験の出題範囲：

Oracle AI Vector Search Professional 認定 1Z0-184-25 試験問題 (Q44-Q49):

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