1Z0-184-25 Oracle AI Vector Search Professional

When generating vector embeddings outside the database, what is the most suitable option for storing the embeddings for later use?. in a CSV file. in a binary FVEC file with the relational data in a CSV file. In the database as BLOB (Binary Large Object) data. In a dedicated vector database.

When generating vector embeddings for a new dataset outside of Oracle Database 23ai, which factor is crucial to ensure meaningful similarity search results?. The choice of programming language used to process the dataset (for example, Python, Java). The physical location where the vector embeddings are stored. The storage format of the new dataset (for example, CSV, JSON). The same vector embedding model must be used for vectorizing the data and creating a query vector.

You are working with vector search in Oracle Database 23ai and need to ensure the integrity of your vector data during storage and retrieval. Which factor is crucial for maintaining the accuracy and reliability of your vector search results?. Using the same embedding model for both vector creation and similarity search. Regularly updating vector embeddings to reflect changes in the source data. The specific distance algorithm employed for vector comparisons. The physical storage location of the vector data.

Which DDL operation is NOT permitted on a table containing a VECTOR column in Oracle Database 23ai?. Creating a new table using CTAS CREATE TABLE AS SELECT that includes the VECTOR column from the original table. Dropping an existing VECTOR column from the table. Modifying the data type of an existing VECTOR column to a non-VECTOR type. Adding a new VECTOR column to the table.

Which SQL statement correctly adds a VECTOR column named v with 4 dimensions and FLOAT 32 format to an existing table named my_table?. ALTER TABLE my_table MODIFY (V VECTOR (4, FLOAT32)). ALTER TABLE my_table ADD (V VECTOR (4, FLOAT32)). UPDATE my_table SET v - VECTOR (4, FLOAT32). ALTER TABLE my_table ADD v VECTOR (4, FLOAT32).

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?. Add the TARGET ACCURACY clause to the query with a higher value for the accuracy. Change the index type to HNSW for better accuracy. Increase the VECTOR MEMORY SIZE initialization parameter. Re-create the index with a higher EFCONSTRUCTION value.

What happens when querying with an IVF index if you increase the value of the NEIGHBOR PARTITION probes parameter?. The number of centroids decreases. Accuracy decreases. Index creation time is reduced. More partitions are probed, improving accuracy, but also increasing query latency.

Which PL/SQL package is primarily used for interacting with Generative AI services in Oracle Database 23ai?. DBMS AI. DBMS ML. DBMS VECTOR CHAIN. DBMS GENAI.

Which SQL function is used to create a vector embedding for a given text string in Oracle Database 23ai?. GENERATE EMBEDDING. CREATE VECTOR_EMBEDDING. EMBED TEXT. VECTOR EMBEDDING.

Which PL/SQL function converts documents such as PDF, DOC, JSON, XML, or HTML to plain text?. DBMS VECTOR/TEXT_TO_PLAIN. DBMS VECTOR_CHAIN.UTIL_TO_TEXT. DBMS VECTOR CHAIN.UTIL_TO_CHUNKS. DBMS VECTOR.CONVERT_TO_TEXT.

What is the primary purpose of the DBMS_VECTOR_CHAIN_UTIL_TO_CHUNKS package in a RAG application?. To generate vector embeddings from a text document. To load a document into the database. To split a large document into smaller chunks to improve vector quality by minimizing token truncation. To convert a document into a single, large text string.

What is the first step in setting up the practice environment for Select AI?. Optionally create an OCI compartment. Create a policy to enable access to OCI Generative AI. Drop any compartment that does not use OCI Generative AI.

How is the security interaction between Autonomous Database and OCI Generative AI managed in the context of Select AI?. By encrypting all communication between the Autonomous Database and OCI Generative AI using TLS/SSL protocols. By utilizing Resource Principals, which grant the Autonomous Database instance access to OCI Generative AI without exposing sensitive credentials. By establishing a secure VPN tunnel between the Autonomous Database and OCI Generative AI service. By requiring users to manually enter their OCI API keys each time they execute a natural language query.

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?. 1 MB. 4 MB. 256 KB. 1 GB.

Which Oracle Cloud Infrastructure (OCI) service is directly integrated with Select AI?. OOO Language. OCI Generative AI. OCT Vision. OCI Data Science.

Which is NOT a feature or capability related to AI and Vector Search in Exadata?. Native Support for Vector Search Only within the Database Server. Vector Replication with Golden Gate. Loading Vector Data using SQL *Loader. AI Smart Scan.

Which statement best describes the core functionality and benefit of Retrieval Augmented Generation (RAG) in Oracle Database 23ai?. It empowers LLMs to interact with private enterprise data stored within the database, leading to more context-aware and precise responses to user queries. 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. It allows users to train their own specialized LLMs directly within the Oracle Database environment using their internal data, thereby reducing the reliance on external AI providers. 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.

If a query vector uses a different distance metric than the one used to create the index, what happens?. The query fails. An exact match search is triggered. The index automatically updates. A warning is logged, but the query executes.

What are the key advantages and considerations of using Retrieval Augmented Generation (RAG) in the context of Oracle AI Vector Search?. It excels at optimizing the performance and efficiency of LLM inference through advanced caching and precomputation techniques, leading to faster response times but potentially storage requirements. It prioritizes real-time data extraction and summarization from various sources to ensure the LLM always has the most up-to-date information. It focuses on training specialized LLMs within the database environment for specific tasks, offering greater control over model behavior and data privacy but potentially requiring more development effort. It leverages existing database security and access controls, thereby enabling secure and controlled access to both the database content and the LLM.

20. Which Python library is used to vectorize text chunks and the user's question in the following example? Import oracledb Connection oracledb, connect (uner-un, password-pw, den-es) Table name – Page With connection.cursor() as cursort Create the table Create_table_sql CREATE TABLE IF NOT EXISTS (table_name) ( id NUMBER PRIMARY KEY, payload CLOR CHECK (payload TS JSON), vector VECTOR ) Try: cursor.execute(create table sql) Except oracledb.DatabaseError as es Raise Connection.autocommit True From sentence_transformers import SentenceTransformer encoder SentenceTransformer(‘all MiniLM-L12-v2’). sentence_transformers. oci. oracledb. json.

What is the function of the COSINE parameter in the SQL query used to retrieve similar vectors? topk = 3 sql = "select payload, vector distance (vector, :vector, COSINE) as score from (table_name) order by score fetch approxifrat (topk) rows only". It filters out vectors with a cosine similarity below a certain threshold. It converts the vectors to a format compatible with the SQL database. It indicates that the cosine distance metric should be used to measure similarity between vectors. It specifies the type of vector encoding used in the database.

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?. GROUP BY with vector operations. FETCH PARTITIONS BY clause. A nested query with ORDER BY. Exact similarity search with a single query vector.

In the following Python code, what is the significance of prepending the source filename to each text chunk before storing it in the vector database? docs = [{"text": filename + "|" + section, "path": filename} for filename, sections in faqs.item() for section in sections] Sample the resulting data docs[:2]. It preserves context and aids in the retrieval process by associating each vectorized chunk with its original source file. It helps differentiate between chunks from different files but has no impact on vectorization. It speeds up the vectorization process by providing a unique identifier for each chunk. It improves the accuracy of the LLM by providing additional training data.

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?. Encodes the question and database chunks into vectors, finds the most similar using cosine similarity, and includes them in the LLM prompt. Trains a separate LLM on the database and uses it to answer, ignoring the general LLM. Converts the question to keywords, searches for matches, and inserts the text into the response. Clusters similar text chunks and randomly selects one from the most relevant cluster.

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?. Enclose vector components in curly braces ({}). 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. Use sparse format for vector data. Rely on SQL*Loader’s automatic normalization of vector data.

Which function is used to generate vector embeddings within an Oracle database?. DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS. DBMS_VECTOR_CHAIN.UTL_TO_TEXT. DBMS_VECTOR_CHAIN.UTL_TO_EMBEDDINGS. DBMS_VECTOR_CHAIN.UTL_TO_GENERATE_TEXT.

Which statement best describes the capability of Oracle Data Pump for handling vector data in the context of vector search applications?. Data Pump only export and import vector data if the vector embeddings are stored as BLOB (Binary Large Object) data types in the database. 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. Data Pump provides native support for exporting and importing tables containing vector data types, facilitating the transfer of vector data for vector search applications. 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.

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?. The database truncates the vector to fit the defined dimensions. The database pads the vector with zeros to match the defined dimensions. The insert operation fails, and an error message is thrown. The database ignores the defined dimensions and inserts the vector as is.

In Oracle Database 23ai, which data type is used to store vector embeddings for similarity search?. VECTOR2. BLOB. VECTOR. VARCHAR2.

What is created to facilitate the use of OCI Generative AI with Autonomous Database?. An AI profile for OCI Generative AI. A dedicated OCI compartment. A new user account with elevated privileges.

Why would you choose to NOT define a specific size for the VECTOR column during development?. It impacts the accuracy of similarity searches. It restricts the database to a single embedding model. It limits the length of text that can be vectorized. Different external embedding models produce vectors with varying dimensions and data types.

What is the correct order of steps for building a RAG application using PL/SQL in Oracle Database 23ai?. Load ONNX Model, Vectorize Question, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output. Load Document, Split Text into Chunks, Load ONNX Model, Create Embeddings, Vectorize Question, Perform Vector Search, Generate Output. Vectorize Question, Load ONNX Model, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output. Load Document, Load ONNX Model, Split Text into Chunks, Create Embeddings, Vectorize Question, Perform Vector Search, Generate Output.

What is the primary purpose of a similarity search in Oracle Database 23ai?. Optimize relational database operations. To compute distances between all data points in a database. To find exact matches in BLOB data. To retrieve the most semantically similar entries using distance metrics between different vectors.

What is the advantage of using Euclidean Squared Distance rather than Euclidean Distance in similarity search queries?. It is the default distance metric for Oracle AI Vector Search. It supports hierarchical partitioning of vectors. It is simpler and faster because it avoids square-root calculations. It guarantees higher accuracy than Euclidean Distance.

You need to prioritize accuracy over speed in a similarity search for a dataset of images. Which should you use?. Approximate similarity search with HNSW indexing and target accuracy of 70%. Multivector similarity search with partitioning. Exact similarity search using a full table scan. Approximate similarity search with IVF indexing and target accuracy of 70%.

What is the significance of splitting text into chunks in the process of loading data into Oracle AI Vector Search?. To reduce the computational burden on the embedding model. To facilitate parallel processing of the data during vectorization. To minimize token truncation as each vector embedding model has its own maximum token limit.

What is the purpose of the VECTOR_DISTANCE function in Oracle Database 23ai similarity search?. To fetch rows that match exact vector embeddings. To create vector indexes for efficient searches. To group vectors by their exact scores. To calculate the distance between vectors using a specified metric.

You are asked with creating table to store vector embeddings with the following characteristics: Each vector must have exactly 512 dimensions. The dimensions should be stored as 32-bit floating point numbers. CREATE Table vectors (id NUMBER, embedding VECTOR(512))f. CREATE TABLE vectors (id NUMBER, embedding VECTOR) ,. CREATE TABLE vectors (id NUMBER, embedding VECTOR(*, INT8)),. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512, FLOAT32)); Syntax = Vector(no_of_dimensions, dimensions_element_format).

Which function should you use to determine the storage format of a vector?. VECTOR_DIMENSION_FORMAT. VECTOR_CHUNKS. VECTOR_NORM. VECTOR_ENBEDDING.

What security enhancement is introduced in Exadata system software 24ai?. Integration with third party security tool. Enhanced encryption algorithm for data at rest. Snmp securitys → Security network management Protocol.

You need to generate a vector from the string "[1.2, 3.4]" in FLOAT32 format with 2 dimensions. Which function will you use?. TO_VECTOR. VECTOR_DISTANCE. FROM_VECTOR. VECTOR_SERIALIZE.

What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?. To calculate vector dimensions. To calculate vector distances. To serialize vectors into a string. To generate a single vector embedding for data.

What is a key characteristic of HNSW vector indexes?. They are hierarchical with multilayered connections. They require exact match for searches. They are disk-based structures. They use hash-based clustering.

What is the primary function of AI Smart Scan in Exadata System Software 24ai?. To provide real-time monitoring and diagnostics for AI applications. To accelerate AI workloads by leveraging Exadata RDMA Memory (XRMEM), Exadata Smart Cache, and on-storage processing. To automatically optimize database queries for improved performance.

Which parameter is used to define the number of closest vector candidates considered during HNSW index creation?. EFCONSTRUCTION. VECTOR_MEMORY_SIZE. NEIGHBOURS. TARGET_ACCURACY.

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?. Exact similarity search using flat search. Approximate similarity search with a low target accuracy setting. Relational filtering combined with an exact search. Exact similarity search with a high target accuracy setting.

Which is a characteristic of an approximate similarity search in Oracle Database 23ai?. It compares every vector in the dataset. It trades off accuracy for faster performance. Always guarantees 100% accuracy. It is slower than exact similarity search.

Which operation is NOT permitted on tables containing VECTOR columns?. SELECT. UPDATE. DELETE. JOIN ON VECTOR columns.

Which is a characteristic of an approximate similarity search in Oracle Database 23ai?. It compares every vector in the dataset. Trades off accuracy for faster performance. Always guarantees 100% accuracy. It is slower than exact similarity search.

You are asked to fetch the top five vectors nearest to a query vector, but only for a specific category of documents. Which query structure should you use?. Use UNION ALL with vector operations. Perform the similarity search without a WHERE clause. Apply relational filters and a similarity search in the query. Use VECTOR_INDEX_HINT and NO WHERE clause.

What is the primary function of an embedding model in the context of vector search?. To define the schema for a vector database. To execute similarity search operations within a database. To transform text or data into numerical vector representations. To store vectors in a structured format for efficient retrieval.

What is the significance of using local ONNX models for embedding within the database?. Support for legacy SQL*Plus clients. Improved accuracy compared to external models. Reduced embedding dimensions for faster processing. Enhanced security because data remains within the database.

Which of the following actions will result in an error when using VECTOR_DIMENSION_COUNT() in Oracle Database 23ai?. Providing a vector with a dimensionality that exceeds the specified dimension count. Using a vector with a data type that is not supported by the function. Providing a vector with duplicate values for its components. Calling the function on a vector that has been created with to_vector().

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?. Approximate similarity search with the VECTOR_DISTANCE function. Exact similarity search with Euclidean distance. Multivector similarity search with approximate fetching and target accuracy. A combination of relational filters and similarity search.

You are asked with finding the closest matching sentences across books, where each book has multiple paragraphs and sentences. Which SQL structure should you use?. A nested query with ORDER BY. Exact similarity search with a single query vector. GROUP BY with vector operations. FETCH PARTITIONS BY clause.

What is the primary difference between the HNSW and IVF vector indexes in Oracle Database 23ai?. Both operate identically but differ in memory usage. OHNSW guarantees accuracy, whereas IVF sacrifices performance for accuracy. OHNSW uses an in-memory neighbor graph for faster approximate searches, whereas IVF use the buffer cache with partitions. OHNSW is partition based, whereas IVF uses neighbor graphs for indexing.

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?. ALTER SYSTEM SET vector memory size=1G SCOPE=BOTH;. ALTER DATABASE SET vector_memory_size=1G SCOPE=VECTOR/. ALTER SYSTEM SET vector_memory_size=1G SCOPE=SGA. ALTER SYSTEM RESET vector_memory_size;.

Which vector index available in Oracle Database 23ai is known for its speed and accuracy, making it a preferred choice for vector search. Binary Tree (BT) index. Inverted File System (IFS) index. Flat index. Hierarchical Navigable Small World (HNSW) index.

What is the purpose of the Vector Pool in Oracle Database 23ai?. To manage database partitioning. To store HNSW vector indexes and IVF index metadata. To enable longer SQL execution. To store non-vector data types.

What is the default distance metric used by the VECTOR_DISTANCE function if none is specified?. Euclidean. Hamming. Cosine. Manhattan.

In Oracle Database 23ai, which SQL function calculates the distance between two vectors using the Euclidean metric?. L1 DISTANCE. L2 DISTANCE. HAMMING DISTANCE. COSINE DISTANCE.

What is a key advantage of using GoldenGate 23ai for managing and distributing vector data for AI applications?. Real-time vector data updates across locations. Automatic translation of vector embeddings between formats. Specialized vector embedding compression. Built-in version control for vector data.

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?. The database pads the vector with zeros to match the defined dimensions. The database ignores the defined dimensions and inserts the vector as is. The database truncates the vector to fit the defined dimensions. The insert operation fails, and an error message is thrown.