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NEW QUESTION # 32
Which function should you use to determine the storage format of a vector?
- A. VECTOR_NORM
- B. VECTOR_EMBEDDING
- C. VECTOR_CHUNKS
- D. VECTOR_DIMENSION_FORMAT
Answer: D
NEW QUESTION # 33
You are tasked with creating a table to store vector embeddings with the following characteristics: Each vector must have exactly 512 dimensions, and the dimensions should be stored as 32-bitfloating point numbers. Which SQL statement should you use?
- A. CREATE TABLE vectors (id NUMBER, embedding VECTOR)
- B. CREATE TABLE vectors (id NUMBER, embedding VECTOR(*, INT8))
- C. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512))
- D. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512, FLOAT32))
Answer: D
Explanation:
In Oracle 23ai, the VECTOR data type can specify dimensions and precision. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512, FLOAT32)) (D) defines a column with exactly 512 dimensions and FLOAT32 (32-bit float) format, meeting both requirements. Option A omits the format (defaults vary), risking mismatch. Option B is unspecified, allowing variable dimensions-not "exactly 512." Option C uses INT8, not FLOAT32, and '*' denotes undefined dimensions. Oracle's SQL reference confirms this syntax for precise VECTOR definitions.
NEW QUESTION # 34
Which Oracle Cloud Infrastructure (OCI) service is directly integrated with Select AI?
- A. OCI Vision
- B. OCI Generative AI
- C. OCI Data Science
- D. OCI Language
Answer: B
Explanation:
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.
NEW QUESTION # 35
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 requiring users to manually enter their OCI API keys each time they execute a natural language query
- C. By establishing a secure VPN tunnel between the Autonomous Database and OCI Generative AI service
- D. By utilizing Resource Principals, which grant the Autonomous Database instance access to OCI Generative AI without exposing sensitive credentials
Answer: D
Explanation:
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.
NEW QUESTION # 36
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. Encodes the question and database chunks into vectors, finds the most similar using cosine similarity, and includes them in the LLM prompt
- B. Clusters similar text chunks and randomly selects one from the most relevant cluster
- C. Converts the question to keywords, searches for matches, and inserts the text into the response
- D. Trains a separate LLM on the database and uses it to answer, ignoring the general LLM
Answer: A
Explanation:
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.
NEW QUESTION # 37
Which statement best describes the capability of Oracle Data Pump for handling vector data in thecontext of vector search applications?
- A. 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
- B. 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
- C. Data Pump only exports and imports vector data if the vector embeddings are stored as BLOB (Binary Large Object) data types in the database
- D. Data Pump provides native support for exporting and importing tables containing vector data types, facilitating the transfer of vector data for vector search applications
Answer: D
Explanation:
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.
NEW QUESTION # 38
In Oracle Database 23ai, which data type is used to store vector embeddings for similarity search?
- A. VARCHAR2
- B. BLOB
- C. VECTOR2
- D. VECTOR
Answer: D
Explanation:
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.
NEW QUESTION # 39
Which SQL function is used to create a vector embedding for a given text string in Oracle Database 23ai?
- A. VECTOR_EMBEDDING
- B. EMBED_TEXT
- C. GENERATE_EMBEDDING
- D. CREATE_VECTOR_EMBEDDING
Answer: A
Explanation:
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.
NEW QUESTION # 40
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. Add the TARGET_ACCURACY clause to the query with a higher value for the accuracy
- B. Re-create the index with a higher EFCONSTRUCTION value
- C. Change the index type to HNSW for better accuracy
- D. Increase the VECTOR_MEMORY_SIZE initialization parameter
Answer: A
Explanation:
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.
NEW QUESTION # 41
If a query vector uses a different distance metric than the one used to create the index, whathappens?
- A. A warning is logged, but the query executes
- B. The index automatically updates
- C. The query fails
- D. An exact match search is triggered
Answer: C
Explanation:
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.
NEW QUESTION # 42
In Oracle Database 23ai, which SQL function calculates the distance between two vectors using the Euclidean metric?
- A. L2_DISTANCE
- B. L1_DISTANCE
- C. COSINE_DISTANCE
- D. HAMMING_DISTANCE
Answer: A
Explanation:
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.
NEW QUESTION # 43
Which of the following actions will result in an error when using VECTOR_DIMENSION_COUNT() in Oracle Database 23ai?
- A. Providing a vector with duplicate values for its components
- B. Calling the function on a vector that has been created with TO_VECTOR()
- C. Using a vector with a data type that is not supported by the function
- D. Providing a vector with a dimensionality that exceeds the specified dimension count
Answer: C
Explanation:
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.
NEW QUESTION # 44
What is the first step in setting up the practice environment for Select AI?
- A. Create a new user account with elevated privileges
- B. Optionally create an OCI compartment
- C. Drop any compartment that does not use OCI Generative AI
- D. Create a policy to enable access to OCI Generative AI
Answer: B
Explanation:
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.
NEW QUESTION # 45
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
Answer: C
Explanation:
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.
NEW QUESTION # 46
What is the default distance metric used by the VECTOR_DISTANCE function if none is specified?
- A. Euclidean
- B. Cosine
- C. Manhattan
- D. Hamming
Answer: B
Explanation:
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.
NEW QUESTION # 47
When using SQL*Loader to load vector data for search applications, what is a critical consideration regarding the formatting of the vector data within the input CSV file?
- A. As FVEC is a binary format and the vector dimensions have a known width, fixed offsets can be used to make parsing the vectors fast and efficient
- B. Enclose vector components in curly braces ({})
- C. Use sparse format for vector data
- D. Rely on SQL*Loader's automatic normalization of vector data
Answer: B
Explanation:
SQLLoader in Oracle 23ai supports loading VECTOR data from CSV files, requiring vectors to be formatted as text. A critical consideration is enclosing components in curly braces (A), e.g., {1.2, 3.4, 5.6}, to match the VECTOR type's expected syntax (parsed into FLOAT32, etc.). FVEC (B) is a binary format, not compatible with CSV text input; SQLLoader expects readable text, not fixed offsets. Sparse format (C) isn't supported for VECTOR columns, which require dense arrays. SQLLoader doesn't normalize vectors automatically (D); formatting must be explicit. Oracle's documentation specifies curly braces for CSV-loaded vectors.
NEW QUESTION # 48
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. A combination of relational filters and similarity search
- B. Approximate similarity search with the VECTOR_DISTANCE function
- C. Exact similarity search with Euclidean distance
- D. Multivector similarity search with approximate fetching and target accuracy
Answer: B
Explanation:
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.
NEW QUESTION # 49
What is a key advantage of using GoldenGate 23ai for managing and distributing vector data for AI applications?
- A. Automatic translation of vector embeddings between formats
- B. Built-in version control for vector data
- C. Real-time vector data updates across locations
- D. Specialized vector embedding compression
Answer: C
Explanation:
Oracle GoldenGate 23ai is a real-time data replication and integration tool, extended in 23ai to handle the VECTOR data type for AI applications. Its key advantage (A) is enabling real-time updates of vector data across distributed locations-e.g., replicating VECTOR columns from a primary database in New York to a secondary in London with sub-second latency. This ensures AI models (e.g., for similarity search or RAG) access the latest embeddings as source data (e.g., documents) changes, critical for dynamic environments like customer support systems where new queries demand current context. Imagine a VECTOR column storing embeddings of support tickets; GoldenGate keeps these synchronized across regions, minimizing staleness that could degrade AI responses.
Option B (automatic translation) is fictional; GoldenGate doesn't convert vector formats (e.g., FLOAT32 to INT8)-that's a model or application task. Option C (compression) isn't a GoldenGate feature; compression might occur at the storage layer, but GoldenGate focuses on replication fidelity, not size reduction. Option D (version control) misaligns with GoldenGate's purpose; it ensures data consistency, not historical versioning like Git. Real-time replication (A) stands out, as Oracle's documentation emphasizes GoldenGate's role in keeping vector-driven AI applications globally consistent, a game-changer for distributed AI deployments where latency or inconsistency could disrupt user trust. Without this, static exports (e.g., Data Pump) would lag, undermining real-time AI use cases.
NEW QUESTION # 50
What security enhancement is introduced in Exadata System Software 24ai?
- A. SNMP security (Security Network Management Protocol)
- B. Enhanced encryption algorithm for data at rest
- C. Integration with third-party security tools
Answer: B
Explanation:
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.
NEW QUESTION # 51
What is a key characteristic of HNSW vector indexes?
- A. They are disk-based structures
- B. They require exact match for searches
- C. They use hash-based clustering
- D. They are hierarchical with multilayered connections
Answer: D
Explanation:
HNSW (Hierarchical Navigable Small World) indexes in Oracle 23ai (A) are characterized by a hierarchical structure with multilayered connections, enabling efficient approximate nearest neighbor (ANN) searches. This graph-based approach connects vectors across levels, balancing speed and accuracy. They don't require exact matches (B); they're designed for approximate searches. They're memory-optimized, not solely disk-based (C), though persisted to disk. Hash-based clustering (D) relates to other methods (e.g., LSH), not HNSW. Oracle's documentation highlights HNSW's hierarchical nature as key to its performance.
NEW QUESTION # 52
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
Answer: B
Explanation:
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.
NEW QUESTION # 53
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. 1 MB
- B. 4 MB
- C. 1 GB
- D. 256 KB
Answer: B
Explanation:
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).
NEW QUESTION # 54
What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?
- A. To calculate vector dimensions
- B. To serialize vectors into a string
- C. To generate a single vector embedding for data
- D. To calculate vector distances
Answer: C
Explanation:
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.
NEW QUESTION # 55
Which function is used to generate vector embeddings within an Oracle database?
- A. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS
- B. DBMS_VECTOR_CHAIN.UTL_TO_TEXT
- C. DBMS_VECTOR_CHAIN.UTL_TO_GENERATE_TEXT
- D. DBMS_VECTOR_CHAIN.UTL_TO_EMBEDDINGS
Answer: D
Explanation:
In Oracle 23ai, the DBMS_VECTOR_CHAIN package provides utilities for vector workflows. UTL_TO_EMBEDDINGS (C) generates vector embeddings from text within the database, typically using an ONNX model, supporting RAG and search applications. UTL_TO_CHUNKS (A) splits text, not generates embeddings. UTL_TO_TEXT (B) converts documents to text, a preprocessing step. UTL_TO_GENERATE_TEXT (D) doesn't exist; text generation is handled by LLMs, not this package. Oracle's documentation identifies UTL_TO_EMBEDDINGS as the embedding creation function in PL/SQL workflows.
NEW QUESTION # 56
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