Question 1

A database administrator attempts to execute the following SQL statement but receives an error:

`CREATE VECTOR INDEX doc_idx ON documents (embedding) TYPE IS HNSW DISTANCE COSINE;`

The `documents` table has an `embedding` column of type `VECTOR(1536, FLOAT32)`. What is the most likely cause for the failure of this statement?

Accepted Answer

The vector dimension of 1536 is too large for a vector index., The `DISTANCE` metric `COSINE` is not supported for HNSW indexes., The statement is missing the mandatory `ORGANIZATION INMEMORY NEIGHBOR GRAPH` clause., The `WITH TARGET ACCURACY` clause is mandatory for an HNSW index and is missing.

Question 2

In an Oracle 23ai RAG pipeline implemented using PL/SQL, after relevant document chunks have been retrieved from a vector store, which `DBMS_VECTOR_CHAIN` function is used to pass the user's query and the retrieved context to a Large Language Model (LLM) to generate the final answer?

Accepted Answer

UTL_TO_GENERATE_TEXT, UTL_TO_EMBEDDINGS, UTL_TO_CHUNKS, RERANK

Question 3

In the context of a Python-based RAG application connecting to Oracle Database 23ai, what is the primary role of the `VECTOR_DISTANCE` function within the SQL retrieval query?

Accepted Answer

To generate a new vector embedding from the text content of the retrieved documents., To calculate a similarity score between a query vector and the vectors stored in a table column., To convert a user's text query into its vector representation directly within the database., To create a vector index on the table to accelerate future searches.

Question 4

What is the fundamental trade-off managed by the `efSearch` parameter when querying a table with an HNSW vector index?

Accepted Answer

Storage cost versus data compression, DML throughput versus index size, Index build time versus memory consumption, Query performance versus search accuracy

Question 5

A developer notices their RAG application is providing answers that seem to ignore the user's original question's nuance, instead just summarizing the retrieved documents. Which part of the RAG process is the most likely area to investigate for improvement?

Accepted Answer

The embedding model used for document chunking., The vector index strategy (e.g., HNSW vs. IVF)., The prompt engineering used to create the augmented prompt for the LLM., The data type used for storing vectors (e.g., VECTOR vs. BLOB).

Question 6

A large-scale visual search system, built on a vector database, recently transitioned from a FLAT index to an Inverted File (IVF) index to handle its catalog of 50 million product image embeddings. While query latency improved dramatically, the quality assurance team reported a significant degradation in search relevance, indicating lower recall. Which query-time parameter for the IVF index should be tuned to increase search recall, accepting a moderate increase in latency?

Accepted Answer

ef_construction, nprobe, M, nlist

Question 7

A large-scale media-sharing platform has implemented a similarity search feature using an IVF (Inverted File) vector index on a dataset of over 100 million image embeddings. The platform needs to offer two service tiers: a standard tier prioritizing low-latency results and a premium tier prioritizing the highest possible recall. To implement this, which query-time parameter of the IVF index should be dynamically adjusted per request to control the trade-off between search scope and speed?

Accepted Answer

metric_type, nlist, nprobe, quantizer_type

Question 8

A large-scale digital asset management system uses a database with a native VECTOR data type to store 1536-dimension embeddings for millions of images. To power a new semantic search feature that must return the top 10 most conceptually similar images with sub-second latency, which query strategy should be implemented against the vector column?

Accepted Answer

A range search using Manhattan distance to retrieve all vectors within a predefined similarity threshold., An Approximate Nearest Neighbor (ANN) search using Cosine similarity to balance query speed and relevance., An exact k-Nearest Neighbor (k-NN) search using Hamming distance to ensure absolute precision., A full table scan that computes the Euclidean distance between the query vector and every vector in the table.

Question 9

A business analyst with limited SQL knowledge needs to find the total number of orders placed in the last quarter from the `CUSTOMER_ORDERS` table. Which query demonstrates the correct and most direct way to accomplish this using Oracle's natural language query capability?

Accepted Answer

SELECT * FROM CUSTOMER_ORDERS WHERE NL_QUERY = 'count all orders from the last quarter';, SELECT AI GENERATE SQL 'count all orders from the last quarter';, SELECT AI 'count all orders from the last quarter';, EXECUTE DBMS_AI.QUERY('count all orders from the last quarter');

Question 10

Inside an Oracle database, you must generate vector embeddings from a CLOB column containing product descriptions. Which specific built-in function is designed to perform this transformation by invoking an embedding model?

Accepted Answer

DBMS_VECTOR.UTL_TO_EMBEDDING(), UTL_VECTOR.GENERATE(), TO_VECTOR(), CAST(description AS VECTOR)

Question 11

A data engineering team is tasked with creating a table to store document chunks and their corresponding embeddings generated by a model that outputs 1024-dimensional vectors as single-precision floating-point numbers. Which `CREATE TABLE` statement correctly defines the table structure for this requirement?

Accepted Answer

CREATE TABLE doc_embeddings (id NUMBER PRIMARY KEY, chunk CLOB, embedding VECTOR(FLOAT32, 1024));, CREATE TABLE doc_embeddings (id NUMBER PRIMARY KEY, chunk CLOB, embedding VECTOR);, CREATE TABLE doc_embeddings (id NUMBER PRIMARY KEY, chunk CLOB, embedding BLOB FORMAT VECTOR(1024, SINGLE));, CREATE TABLE doc_embeddings (id NUMBER PRIMARY KEY, chunk CLOB, embedding VECTOR(1024, FLOAT32));

Question 12

To prepare a dataset of one million product images for similarity search in Oracle Database 23ai, an AI engineer has generated 512-dimensional embeddings using a pre-trained model. What is the most performant method for loading these externally generated embeddings into the database?

Accepted Answer

Write the embeddings to a flat file and use SQL*Loader with a control file that maps the data to the VECTOR column., Create an external table that points to the raw image files and use a PL/SQL procedure to read and process them one by one., Use Oracle Data Pump to import the raw image files and generate embeddings using a database trigger., Execute one million individual INSERT statements within a loop in a Python script.

Question 13

In order to ensure the DOT_PRODUCT distance function yields similarity rankings that are identical to those from the COSINE function, what preprocessing step is essential for all vectors before they are used in Oracle AI Vector Search?

Accepted Answer

All vectors must be normalized to have a unit length (L2 norm of 1)., All vectors must be centered by subtracting the dataset's mean vector., The dimensionality of all vectors must be made an even number., All vector components must be converted to positive values.

Question 14

An e-commerce site's 'visually similar products' feature uses an HNSW index on a VECTOR column. Which SQL clause must be used to perform the fastest possible approximate search?

Accepted Answer

Use the `FETCH APPROXIMATE FIRST N ROWS ONLY` clause., Append the `APPROXIMATE` keyword to the `ORDER BY` clause., Enclose the `VECTOR_DISTANCE` function in an `APPROXIMATE()` function., Add a `FAST` hint to the `SELECT` statement.

Question 15

A retail analytics team is developing a recommendation engine using vector embeddings where a vector's magnitude represents a product's popularity. The goal is to recommend products that are not only contextually similar (angle) but also popular (magnitude). Which distance metric is most suitable for this specific requirement?

Accepted Answer

DOT_PRODUCT, COSINE, HAMMING, EUCLIDEAN

Question 16

A developer at a financial institution is building a Retrieval-Augmented Generation (RAG) system entirely within an Oracle Database using PL/SQL. The system must query a vector index containing embeddings of internal policy documents to find the most semantically relevant sections based on a user's natural language query. Which PL/SQL package and subprogram should be used to perform this core retrieval step?

Accepted Answer

The DBMS_SEARCH.SEARCH_DOCUMENTS procedure to find and rank documents based on keyword frequency., The DBMS_DATA_MINING.APPLY function to classify the user query into a predefined policy category., The DBMS_VECTOR.QUERY function to find the nearest neighbors to a query vector., The CTX_QUERY.CONTAINS procedure to perform a full-text search against a CONTEXT index.

Question 17

To create a table named `PRODUCT_IMAGES` for storing 768-dimensional embeddings with double-precision floating-point format, which SQL statement is correct?

Accepted Answer

CREATE TABLE product_images (id NUMBER, image_embedding VECTOR(768, FLOAT64));, CREATE TABLE product_images (id NUMBER, image_embedding EMBEDDING(768, FLOAT64));, CREATE TABLE product_images (id NUMBER, image_embedding VECTOR(768));, CREATE TABLE product_images (id NUMBER, image_embedding VECTOR(FLOAT64, 768));

Question 18

For a PL/SQL RAG application to securely authenticate with an external LLM service, what is the recommended Oracle Database mechanism for storing and managing the required API keys or authentication tokens?

Accepted Answer

Store the API key in a configuration table with a `VARCHAR2` column., Use the `DBMS_CREDENTIAL` package to create and store a credential object., Hardcode the API key as a constant in the PL/SQL package specification., Read the API key from an operating system environment variable using a Java stored procedure.

Question 19

A financial analytics firm is deploying a fraud detection application on Exadata Database Service that performs real-time vector similarity searches on a massive, multi-terabyte transaction dataset. During performance testing, database server CPUs are consistently saturated by the vector distance calculations, creating a significant bottleneck. To mitigate this issue while maintaining the lowest possible latency, which approach should be implemented?

Accepted Answer

Partition the vector table by transaction date and use partition pruning to limit the scope of each search., Utilize the In-Memory Column Store with vector compression to reduce the I/O and CPU overhead on the database servers., Enable Exadata AI Storage offload for vector search, pushing the distance calculations down to the storage servers., Increase the SGA and PGA on the database nodes to ensure the entire vector index is cached in memory.

Question 20

When inserting a new row into a table, which SQL function is used to generate a vector embedding from a text column's data, assuming an embedding model has been loaded into the database?

Accepted Answer

VECTOR_EMBEDDING(), TO_VECTOR(), EMBEDDING_FROM_TEXT(), GENERATE_VECTOR()

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