2025年最新の100%試験高合格率1Z0-184-25問題集PDF
合格させる試験完全版1Z0-184-25問題集62解答
質問 # 37
What is created to facilitate the use of OCI Generative AI with Autonomous Database?
- A. A dedicated OCI compartment
- B. A new user account with elevated privileges
- C. An AI profile for OCI Generative AI
- D. A secure VPN tunnel
正解：C
解説：
To integrate OCI Generative AI with Autonomous Database in Oracle 23ai (e.g., for Select AI), an AI profile (A) is created within the database using DBMS_AI. This profile configures the connection to OCI Generative AI, specifying the LLM and authentication (e.g., Resource Principals). A compartment (B) organizes OCI resources but isn't "created" specifically for this integration; it's a prerequisite. A new user account (C) or VPN tunnel (D) isn't required; security leverages existing mechanisms. Oracle's Select AI setup documentation highlights the AI profile as the key facilitator.
質問 # 38
Which statement best describes the core functionality and benefit of Retrieval Augmented Generation (RAG) in Oracle Database 23ai?
- A. It empowers LLMs to interact with private enterprise data stored within the database, leading to more context-aware and precise responses to user queries
- B. It enables Large Language Models (LLMs) to access and process real-time data streams from diverse sources to generate the most up-to-date insights
- C. It allows users to train their own specialized LLMs directly within the Oracle Database environment using their internal data, thereby reducing reliance on external AI providers
- D. It primarily aims to optimize the performance and efficiency of LLMs by using advanced data retrieval techniques, thus minimizing response times and reducing computational overhead
正解：A
解説：
RAG in Oracle Database 23ai combines vector search with LLMs to enhance responses by retrieving relevant private data from the database (e.g., via VECTOR columns) and augmenting LLM prompts. This (A) improves context-awareness and precision, leveraging enterprise-specific data without retraining LLMs. Optimizing LLM performance (B) is a secondary benefit, not the core focus. Training specialized LLMs (C) is not RAG's purpose; it uses existing models. Real-time streaming (D) is possible but not the primary benefit, as RAG focuses on stored data retrieval. Oracle's RAG documentation emphasizes private data integration for better LLM outputs.
質問 # 39
What is the default distance metric used by the VECTOR_DISTANCE function if none is specified?
- A. Cosine
- B. Hamming
- C. Manhattan
- D. Euclidean
正解：A
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 # 40
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. Exact similarity search using flat search
- B. Approximate similarity search with a low target accuracy setting
- C. Relational filtering combined with an exact search
- D. Exact similarity search with a high target accuracy setting
正解：B
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 # 41
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. Exact similarity search with Euclidean distance
- B. A combination of relational filters and similarity search
- C. Multivector similarity search with approximate fetching and target accuracy
- 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 # 42
What security enhancement is introduced in Exadata System Software 24ai?
- A. Integration with third-party security tools
- B. Enhanced encryption algorithm for data at rest
- C. SNMP security (Security Network Management Protocol)
正解：B
解説：
Exadata System Software 24ai (noted in context beyond 23ai) introduces an enhanced encryption algorithm for data at rest (B), strengthening security for stored data, including vectors. Third-party integration (A) isn't highlighted as a 24ai feature. SNMP security (C) relates to network monitoring, not a primary Exadata enhancement. Oracle's Exadata documentation for 24ai emphasizes advanced encryption as a key security upgrade.
質問 # 43
If a query vector uses a different distance metric than the one used to create the index, whathappens?
- A. An exact match search is triggered
- B. A warning is logged, but the query executes
- C. The index automatically updates
- D. The query fails
正解：D
解説：
In Oracle Database 23ai, vector indexes (e.g., HNSW, IVF) are built with a specific distance metric (e.g., cosine, Euclidean) that defines how similarity is computed. If a query specifies a different metric (e.g., querying with Euclidean on a cosine-based index), the index cannot be used effectively, and the query fails (A) with an error, as the mismatch invalidates the index's structure. An exact match search (B) doesn't occur automatically; Oracle requires explicit control. The index doesn't update itself (C), and warnings (D) are not the default behavior-errors are raised instead. Oracle's documentation mandates metric consistency for index usage.
質問 # 44
You need to prioritize accuracy over speed in a similarity search for a dataset of images. Which should you use?
- A. Multivector similarity search with partitioning
- B. Exact similarity search using a full table scan
- C. Approximate similarity search with HNSW indexing and target accuracy of 70%
- D. Approximate similarity search with IVF indexing and target accuracy of 70%
正解：B
解説：
To prioritize accuracy over speed, exact similarity search with a full table scan (C) computes distances between the query vector and all stored vectors, guaranteeing 100% recall without approximation trade-offs. HNSW with 70% target accuracy (A) and IVF with 70% (D) are approximate methods, sacrificing accuracy for speed via indexing (e.g., probing fewer neighbors). Multivector search (B) isn't a standard Oracle 23ai term; partitioning aids scale, not accuracy. Exact search, though slower, ensures maximum accuracy, as per Oracle's vector search options.
質問 # 45
Which SQL function is used to create a vector embedding for a given text string in Oracle Database 23ai?
- A. GENERATE_EMBEDDING
- B. VECTOR_EMBEDDING
- C. CREATE_VECTOR_EMBEDDING
- D. EMBED_TEXT
正解：B
解説：
The VECTOR_EMBEDDING function in Oracle Database 23ai generates a vector embedding from input data (e.g., a text string) using a specified model, such as an ONNX model loaded into the database. It's designed for in-database embedding creation, supporting vector search and AI applications. Options A, B, and C (GENERATE_EMBEDDING, CREATE_VECTOR_EMBEDDING, EMBED_TEXT) are not valid SQL functions in 23ai. VECTOR_EMBEDDING integrates seamlessly with the VECTOR data type and is documented as the standard method for embedding generation in SQL queries.
質問 # 46
Which of the following actions will result in an error when using VECTOR_DIMENSION_COUNT() in Oracle Database 23ai?
- A. Using a vector with a data type that is not supported by the function
- B. Providing a vector with duplicate values for its components
- C. Calling the function on a vector that has been created with TO_VECTOR()
- D. Providing a vector with a dimensionality that exceeds the specified dimension count
正解：A
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 # 47
What is the correct order of steps for building a RAG application using PL/SQL in Oracle Database 23ai?
- A. Load Document, Split Text into Chunks, Load ONNX Model, Create Embeddings, Vectorize Question, Perform Vector Search, Generate Output
- B. Load ONNX Model, Vectorize Question, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output
- C. Load Document, Load ONNX Model, Split Text into Chunks, Create Embeddings, VectorizeQuestion, Perform Vector Search, Generate Output
- D. Vectorize Question, Load ONNX Model, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output
正解：A
解説：
Building a RAG application in Oracle 23ai using PL/SQL follows a logical sequence: (1) Load Document (e.g., via SQL*Loader) into the database; (2) Split Text into Chunks (e.g., DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS) to manage token limits; (3) Load ONNX Model (e.g., via DBMS_VECTOR) for embedding generation; (4) Create Embeddings (e.g., UTL_TO_EMBEDDINGS) for the chunks; (5) Vectorize Question (using the same model) when a query is received; (6) Perform Vector Search (e.g., VECTOR_DISTANCE) to find relevant chunks; (7) Generate Output (e.g., via DBMS_AI with an LLM). Option B matches this flow. A starts with the model prematurely. C prioritizes the question incorrectly. D is close but loads the model too early. Oracle's RAG workflow documentation outlines this document-first approach.
質問 # 48
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. json
- B. sentence_transformers
- C. oci
- D. oracledb
正解：B
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 # 49
Which is NOT a feature or capability related to AI and Vector Search in Exadata?
- A. AI Smart Scan
- B. Loading Vector Data using SQL*Loader
- C. Native Support for Vector Search Only within the Database Server
- D. Vector Replication with GoldenGate
正解：C
解説：
Exadata in Oracle Database 23ai enhances AI and vector search capabilities. Vector Replication with GoldenGate (B) supports real-time vector data distribution. SQL*Loader (C) loads vector data into VECTOR columns. AI Smart Scan (D) accelerates AI workloads using Exadata's storage optimizations. However, "Native Support for Vector Search Only within the Database Server" (A) is not a feature; vector search is natively supported across Exadata's architecture, leveraging both database and storage layers (e.g., via Smart Scan), not restricted to the server alone. This option misrepresents Exadata's distributed capabilities, making it the correct "NOT" answer.
質問 # 50
What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?
- A. To calculate vector distances
- B. To calculate vector dimensions
- C. To serialize vectors into a string
- D. To generate a single vector embedding for data
正解：D
解説：
The VECTOR_EMBEDDING function in Oracle 23ai (D) generates a vector embedding from input data (e.g., text) using a specified model (e.g., ONNX), producing a single VECTOR-type output for similarity search or AI tasks. It doesn't calculate dimensions (A); VECTOR_DIMENSION_COUNT does that. It doesn't compute distances (B); VECTOR_DISTANCE is for that. It doesn't serialize vectors (C); VECTOR_SERIALIZE handles serialization. Oracle's documentation positions VECTOR_EMBEDDING as the core function for in-database embedding creation, central to vector search workflows.
質問 # 51
What is the primary difference between the HNSW and IVF vector indexes in Oracle Database 23ai?
- A. Both operate identically but differ in memory usage
- B. HNSW is partition-based, whereas IVF uses neighbor graphs for indexing
- C. HNSW uses an in-memory neighbor graph for faster approximate searches, whereas IVF uses the buffer cache with partitions
- D. HNSW guarantees accuracy, whereas IVF sacrifices performance for accuracy
正解：C
質問 # 52
Which statement best describes the capability of Oracle Data Pump for handling vector data in thecontext of vector search applications?
- A. Data Pump provides native support for exporting and importing tables containing vector data types, facilitating the transfer of vector data for vector search applications
- B. Data Pump only exports and imports vector data if the vector embeddings are stored as BLOB (Binary Large Object) data types in the database
- C. Because of the complexity of vector data, Data Pump requires a specialized plug-in to handle the export and import operations involving vector data types
- D. Data Pump treats vector embeddings as regular text strings, which can lead to data corruption or loss of precision when transferring vector data for vector search
正解：A
解説：
Oracle Data Pump in 23ai natively supports the VECTOR data type (C), allowing export and import of tables with vector columns without conversion or plug-ins. This facilitates vector search application migrations, preserving dimensional and format integrity (e.g., FLOAT32). BLOB storage (A) isn't required; VECTOR is a distinct type. Data Pump doesn't treat vectors as text (B), avoiding corruption; it handles them as structured arrays. No specialized plug-in (D) is needed; native support is built-in. Oracle's Data Pump documentation confirms seamless handling of VECTOR data.
質問 # 53
Which SQL statement correctly adds a VECTOR column named "v" with 4 dimensions and FLOAT32 format to an existing table named "my_table"?
- A. ALTER TABLE my_table ADD v VECTOR(4, FLOAT32)
- B. UPDATE my_table SET v = VECTOR(4, FLOAT32)
- C. ALTER TABLE my_table ADD (v VECTOR(4, FLOAT32))
- D. ALTER TABLE my_table MODIFY (v VECTOR(4, FLOAT32))
正解：C
解説：
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.
質問 # 54
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. GROUP BY with vector operations
- B. Exact similarity search with a single query vector
- C. A nested query with ORDER BY
- D. FETCH PARTITIONS BY clause
正解：C
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 # 55
How is the security interaction between Autonomous Database and OCI Generative AI managed in the context of Select AI?
- A. By encrypting all communication between the Autonomous Database and OCI Generative AI using TLS/SSL protocols
- B. By establishing a secure VPN tunnel between the Autonomous Database and OCI Generative AI service
- C. By utilizing Resource Principals, which grant the Autonomous Database instance access to OCI Generative AI without exposing sensitive credentials
- D. By requiring users to manually enter their OCI API keys each time they execute a natural language query
正解：C
解説：
In Oracle Database 23ai's Select AI, security between the Autonomous Database and OCI Generative AI is managed using Resource Principals (B). This mechanism allows the database instance to authenticate itself to OCI services without hardcoding credentials, enhancing security by avoiding exposure of sensitive keys. TLS/SSL encryption (A) is used for data-in-transit security, but it's a complementary layer, not the primary management method. A VPN tunnel (C) is unnecessary within OCI's secure infrastructure and not specified for Select AI. Manual API key entry (D) is impractical and insecure for automated database interactions. Oracle's documentation on Select AI highlights Resource Principals as the secure, scalable authentication method.
質問 # 56
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 # 57
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