[2026年01月] 更新されたのはOracle 1Z0-184-25問題集PDFオンラインエンジン
1Z0-184-25.PDFで問題解答！サンプル問題は信頼され続けます
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 |
|
| トピック 2 |
|
| トピック 3 |
|
| トピック 4 |
|
質問 # 11
In Oracle Database 23ai, which data type is used to store vector embeddings for similarity search?
- A. VECTOR
- B. BLOB
- C. VARCHAR2
- D. VECTOR2
正解：A
解説：
Oracle Database 23ai introduces the VECTOR data type (C) specifically for storing vector embeddings used in similarity search, supporting dimensions and formats (e.g., FLOAT32, INT8). VECTOR2 (A) doesn't exist. BLOB (B) can store binary data, including vectors, but lacks the semantic structure and indexing support of VECTOR. VARCHAR2 (D) is for text, not numerical arrays. VECTOR is optimized for AI vector search with native indexing (e.g., HNSW, IVF), as per Oracle's documentation.
質問 # 12
You want to quickly retrieve the top-10 matches for a query vector from a dataset of billions of vectors, prioritizing speed over exact accuracy. What is the best approach?
- A. Relational filtering combined with an exact search
- B. Exact similarity search using flat search
- C. Approximate similarity search with a low target accuracy setting
- D. Exact similarity search with a high target accuracy setting
正解：C
解説：
For speed over accuracy with billions of vectors, approximate similarity search (ANN) with a low target accuracy setting (B) (e.g., 70%) uses indexes like HNSW or IVF, probing fewer vectors to return top-10 matches quickly. Exact flat search (A) scans all vectors, too slow for billions. Relational filtering with exact search (C) adds overhead without speed gains. Exact search with high accuracy (D) maximizes precision but sacrifices speed. Oracle's documentation recommends ANN for large-scale, speed-focused queries.
質問 # 13
Which Oracle Cloud Infrastructure (OCI) service is directly integrated with Select AI?
- A. OCI Language
- B. OCI Data Science
- C. OCI Generative AI
- D. OCI Vision
正解：C
解説：
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.
質問 # 14
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. Providing a vector with duplicate values for its components
- C. Using a vector with a data type that is not supported by the function
- D. Calling the function on a vector that has been created with TO_VECTOR()
正解：C
解説：
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.
質問 # 15
Which is a characteristic of an approximate similarity search in Oracle Database 23ai?
- A. It is slower than exact similarity search
- B. It compares every vector in the dataset
- C. It always guarantees 100% accuracy
- D. It trades off accuracy for faster performance
正解：D
解説：
Approximate similarity search (ANN) in Oracle 23ai (B) uses indexes (e.g., HNSW, IVF) to trade accuracy for speed, returning near-matches faster by not comparing all vectors. Exact search compares every vector (A), not ANN. It doesn't guarantee 100% accuracy (C); that's exact search. It's faster, not slower (D), than exact search due to indexing. Oracle's documentation defines ANN's speed-accuracy trade-off as its hallmark.
質問 # 16
What is the advantage of using Euclidean Squared Distance rather than Euclidean Distance in similarity search queries?
- A. It supports hierarchical partitioning of vectors
- B. It guarantees higher accuracy than Euclidean Distance
- C. It is the default distance metric for Oracle AI Vector Search
- D. It is simpler and faster because it avoids square-root calculations
正解：D
解説：
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.
質問 # 17
What is the first step in setting up the practice environment for Select AI?
- A. Create a new user account with elevated privileges
- B. Create a policy to enable access to OCI Generative AI
- C. Drop any compartment that does not use OCI Generative AI
- D. Optionally create an OCI compartment
正解：D
解説：
Select AI in Oracle Database 23ai enables natural language queries by integrating with OCI Generative AI services. The first step in setting up the practice environment is to optionally create an OCI compartment (A), which organizes and isolates resources in Oracle Cloud Infrastructure (OCI). This is foundational because subsequent steps-like defining policies or configuring the Autonomous Database-depend on a compartment structure, though an existing compartment can be reused, making it optional. Creating a policy (B) is a subsequent step to grant access to OCIGenerative AI, requiring a compartment first. Dropping compartments (C) is irrelevant and disruptive. Creating a user account (D) is not specified as the initial step in Select AI setup. Oracle's Select AI documentation lists compartment setup as the starting point in OCI configuration.
質問 # 18
Which PL/SQL package is primarily used for interacting with Generative AI services in Oracle Database 23ai?
- A. DBMS_ML
- B. DBMS_AI
- C. DBMS_VECTOR_CHAIN
- D. DBMS_GENAI
正解：B
解説：
Oracle Database 23ai introduces DBMS_AI as the primary PL/SQL package for interacting with Generative AI services, such as OCI Generative AI, enabling features like natural language query processing (e.g., Select AI) and AI-driven insights. DBMS_ML (B) focuses on machine learning model training and management, not generative AI. DBMS_VECTOR_CHAIN (C) supports vector processing workflows (e.g., document chunking, embedding), but it's not the main interface for generative AI services. DBMS_GENAI (D) is not a recognized package in 23ai documentation. DBMS_AI's role is highlighted in Oracle's AI integration features for 23ai.
質問 # 19
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. Multivector similarity search with approximate fetching and target accuracy
- B. Exact similarity search with Euclidean distance
- C. A combination of relational filters and similarity search
- D. Approximate similarity search with the VECTOR_DISTANCE function
正解：D
解説：
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.
質問 # 20
What happens when you attempt to insert a vector with an incorrect number of dimensions into a VECTOR column with a defined number of dimensions?
- A. The insert operation fails, and an error message is thrown
- B. The database ignores the defined dimensions and inserts the vector as is
- C. The database pads the vector with zeros to match the defined dimensions
- D. The database truncates the vector to fit the defined dimensions
正解：A
解説：
In Oracle Database 23ai, a VECTOR column with a defined dimension count (e.g., VECTOR(4, FLOAT32)) enforces strict dimensional integrity to ensure consistency for similarity search and indexing. Attempting to insert a vector with a mismatched number of dimensions-say, TO_VECTOR('[1.2, 3.4, 5.6]') (3D) into a VECTOR(4)-results in the insert operation failing with an error (D), such as ORA-13199: "vector dimension mismatch." This rigidity protects downstream AI operations; a 3D vector in a 4D column would misalign with indexed data (e.g., HNSW graphs), breaking similarity calculations like cosine distance, which require uniform dimensionality.
Option A (truncation) is tempting but incorrect; Oracle doesn't silently truncate [1.2, 3.4, 5.6] to [1.2, 3.4]-this would discard data arbitrarily, risking semantic loss (e.g., a truncated sentence embedding losing meaning). Option B (padding with zeros) seems plausible-e.g., [1.2, 3.4, 5.6] becoming [1.2, 3.4, 5.6, 0]-but Oracle avoids implicit padding to prevent unintended semantic shifts (zero-padding could alter distances). Option C (ignoring dimensions) only applies to undefined VECTOR columns (e.g., VECTOR without size), not fixed ones; here, the constraint is enforced. The failure (D) forces developers to align data explicitly (e.g., regenerate embeddings), ensuring reliability-a strict but necessary design choice in Oracle's AI framework. In practice, this error prompts debugging upstream data pipelines, avoiding silent failures that could plague production AI systems.
質問 # 21
What is the significance of splitting text into chunks in the process of loading data into Oracle AI Vector Search?
- A. To reduce the computational burden on the embedding model
- B. To facilitate parallel processing of the data during vectorization
- C. To minimize token truncation as each vector embedding model has its own maximum token limit
正解：C
解説：
Splitting text into chunks (C) in Oracle AI Vector Search (e.g., via DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS) ensures that each segment fits within the token limit of embedding models (e.g., 512 tokens for BERT), preventing truncation that loses semantic content. This improves vector quality for similarity search. Reducing computational burden (A) is a secondary effect, not the primary goal. Parallel processing (B) may occur but isn't the main purpose; chunking is about model compatibility. Oracle's documentation emphasizes chunking to align with embedding model constraints.
質問 # 22
Which Python library is used to vectorize text chunks and the user's question in the following example?
import oracledb
connection = oracledb.connect(user=un, password=pw, dsn=ds)
table_name = "Page"
with connection.cursor() as cursor:
create_table_sql = f"""
CREATE TABLE IF NOT EXISTS {table_name} (
id NUMBER PRIMARY KEY,
payload CLOB CHECK (payload IS JSON),
vector VECTOR
)"""
try:
cursor.execute(create_table_sql)
except oracledb.DatabaseError as e:
raise
connection.autocommit = True
from sentence_transformers import SentenceTransformer
encoder = SentenceTransformer('all-MiniLM-L12-v2')
- A. sentence_transformers
- B. json
- C. oracledb
- D. oci
正解：A
解説：
In the provided Python code, the sentence_transformers library (A) is imported and used to instantiate a SentenceTransformer object with the 'all-MiniLM-L12-v2' model. This library is designed to vectorize text (e.g., chunks and questions) into embeddings, a common step in RAG applications. The oracledb library (C) handles database connectivity, not vectorization. oci (B) is for OCI service interaction, not text embedding. json (D) processes JSON data, not vectors. The code explicitly uses sentence_transformers for vectorization, consistent with Oracle's examples for external embedding integration.
質問 # 23
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. 256 KB
- B. 1 MB
- C. 4 MB
- D. 1 GB
正解：C
解説：
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).
質問 # 24
What is the primary purpose of a similarity search in Oracle Database 23ai?
- A. To group vectors by their exact scores
- B. To find exact matches in BLOB data
- C. Optimize relational database operations to compute distances between all data points in a database
- D. To retrieve the most semantically similar entries using distance metrics between different vectors
正解：D
解説：
Similarity search in Oracle 23ai (C) uses vector embeddings in VECTOR columns to retrieve entries semantically similar to a query vector, based on distance metrics (e.g., cosine, Euclidean) via functions like VECTOR_DISTANCE. This is key for AI applications like RAG, finding "close" rather than exact matches. Optimizing relational operations (A) is unrelated; similarity search is vector-specific. Exact matches in BLOBs (B) don't leverage vector semantics. Grouping by scores (D) is a post-processing step, not the primary purpose. Oracle's documentation defines similarity search as retrieving semantically proximate vectors.
質問 # 25
You are tasked with finding the closest matching sentences across books, where each book has multiple paragraphs and sentences. Which SQL structure should you use?
- A. A nested query with ORDER BY
- B. FETCH PARTITIONS BY clause
- C. GROUP BY with vector operations
- D. Exact similarity search with a single query vector
正解：A
解説：
Finding the closest matching sentences across books involves comparing a query vector to sentence vectors stored in a table (e.g., columns: book_id, sentence, vector). A nested query with ORDER BY (A) is the optimal SQL structure: an inner query computes distances (e.g., SELECT sentence, VECTOR_DISTANCE(vector, :query_vector, COSINE) AS score FROM sentences), and the outer query sorts and limits results (e.g., SELECT * FROM (inner_query) ORDER BY score FETCH FIRST 5 ROWS ONLY). This ranks sentences by similarity, leveraging Oracle's vector capabilities efficiently, especially with an index.
Option B (exact search) describes a technique, not a structure, and a full scan is slow without indexing-lacking specificity here. Option C (GROUP BY) aggregates (e.g., by book), not ranks individual sentences, missing the "closest" goal. Option D (FETCH PARTITIONS BY) isn't a valid clause; it might confuse with IVF partitioning, but that's index-related, not query syntax. The nested structure allows flexibility (e.g., adding WHERE clauses) and aligns with Oracle's vector search examples, ensuring both correctness and scalability-crucial when books yield thousands of sentences.
質問 # 26
In Oracle Database 23ai, which SQL function calculates the distance between two vectors using the Euclidean metric?
- A. HAMMING_DISTANCE
- B. L1_DISTANCE
- C. L2_DISTANCE
- D. COSINE_DISTANCE
正解：C
解説：
In Oracle Database 23ai, vector distance calculations are primarily handled by the VECTOR_DISTANCE function, which supports multiple metrics (e.g., COSINE, EUCLIDEAN) specified as parameters (e.g., VECTOR_DISTANCE(v1, v2, EUCLIDEAN)). However, the question implies distinct functions, a common convention in some databases or libraries, and Oracle's documentation aligns L2_DISTANCE (B) with the Euclidean metric. L2 (Euclidean) distance is the straight-line distance between two points in vector space, computed as √∑(xi - yi)², where xi and yi are vector components. For example, for vectors [1, 2] and [4, 6], L2 distance is √((1-4)² + (2-6)²) = √(9 + 16) = 5.
Option A, L1_DISTANCE, represents Manhattan distance (∑|xi - yi|), summing absolute differences-not Euclidean. Option C, HAMMING_DISTANCE, counts differing bits, suited for binary vectors (e.g., INT8), not continuous Euclidean spaces typically used with FLOAT32 embeddings. Option D, COSINE_DISTANCE (1 - cosine similarity), measures angular separation, distinct from Euclidean's magnitude-inclusive approach. While VECTOR_DISTANCE is the general function in 23ai, L2_DISTANCE may be an alias or a contextual shorthand in some Oracle AI examples, reflecting Euclidean's prominence in geometric similarity tasks. Misinterpreting this could lead to choosing COSINE for spatial tasks where magnitude matters, skewing results. Oracle's vector search framework supports Euclidean via VECTOR_DISTANCE, but B aligns with the question's phrasing.
質問 # 27
......
Oracle 1Z0-184-25問題集PDFのベストを目指すなら問題集を使おう！高得点目指すならここ：https://www.passtest.jp/Oracle/1Z0-184-25-shiken.html
Oracle Database 23ai 1Z0-184-25試験と認定テストエンジン：https://drive.google.com/open?id=18ov5Lsijl8fjlhRZPUyANjYxsK0h0O4n