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1 / 41

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?

bash

CollapseWrapCopy

docs = [{"text": filename + "|" + section, "path": filename} for filename, sections in faqs.items() for section in sections]

# Sample the resulting data

docs[:2]

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

It preserves context and aids in the retrieval process by associating each vectorized chunk with its original source file

2 / 41

What is a key characteristic of HNSW vector indexes?

They are disk-based structures

They require exact match for searches

They are hierarchical with multilayered connections

They use hash-based clustering

3 / 41

In Oracle Database 23ai, which data type is used to store vector embeddings for similarity search?

VECTOR2

VARCHAR2

BLOB

VECTOR

4 / 41

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?

Converts the question to keywords, searches for matches, and inserts the text into the response

Trains a separate LLM on the database and uses it to answer, ignoring the general LLM

Encodes the question and database chunks into vectors, finds the most similar using cosine similarity, and includes them in the LLM prompt

Clusters similar text chunks and randomly selects one from the most relevant cluster

5 / 41

Which function is used to generate vector embeddings within an Oracle database?

DBMS_VECTOR_CHAIN.UTL_TO_EMBEDDINGS

DBMS_VECTOR_CHAIN.UTL_TO_GENERATE_TEXT

DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS

DBMS_VECTOR_CHAIN.UTL_TO_TEXT

6 / 41

What is the primary function of AI Smart Scan in Exadata System Software 24ai?

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

To provide real-time monitoring and diagnostics for AI applications

7 / 41

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

Hierarchical Navigable Small World (HNSW) index

Inverted File System (IFS) index

Inverted File (IVF) index

8 / 41

What happens when querying with an IVF index if you increase the value of the NEIGHBOR_PARTITIONS probes parameter?

The number of centroids decreases

More partitions are probed, improving accuracy, but also increasing query latency

Index creation time is reduced

Accuracy decreases

9 / 41

When generating vector embeddings for a new dataset outside of Oracle Database 23ai, which factor is crucial to ensure meaningful similarity search results?

The same vector embedding model must be used for vectorizing the data and creating a query vector

The choice of programming language used to process the dataset (e.g., Python, Java)

The storage format of the new dataset (e.g., CSV, JSON)

The physical location where the vector embeddings are stored

10 / 41

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

FROM_VECTOR

VECTOR_DISTANCE

TO_VECTOR

VECTOR_SERIALIZE

11 / 41

Why would you choose to NOT define a specific size for the VECTOR column during development?

It impacts the accuracy of similarity searches

Different external embedding models produce vectors with varying dimensions and data types

It limits the length of text that can be vectorized

It restricts the database to a single embedding model

12 / 41

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 GB

1 MB

256 KB

4 MB

13 / 41

If a query vector uses a different distance metric than the one used to create the index, whathappens?

The index automatically updates

An exact match search is triggered

The query fails

A warning is logged, but the query executes

14 / 41

What is the default distance metric used by the VECTOR_DISTANCE function if none is specified?

Cosine

Manhattan

Euclidean

Hamming

15 / 41

In Oracle Database 23ai, which SQL function calculates the distance between two vectors using the Euclidean metric?

HAMMING_DISTANCE

L1_DISTANCE

L2_DISTANCE

COSINE_DISTANCE

16 / 41

What is a key advantage of using GoldenGate 23ai for managing and distributing vector data for AI applications?

Specialized vector embedding compression

Automatic translation of vector embeddings between formats

Real-time vector data updates across locations

Built-in version control for vector data

17 / 41

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

18 / 41

What is the purpose of the VECTOR_DISTANCE function in Oracle Database 23ai similarity search?

To fetch rows that match exact vector embeddings

To calculate the distance between vectors using a specified metric

To group vectors by their exact scores

To create vector indexes for efficient searches

19 / 41

What is the purpose of the Vector Pool in Oracle Database 23ai?

To store non-vector data types

To enable longer SQL execution

To manage database partitioning

To store HNSW vector indexes and IVF index metadata

20 / 41

Which PL/SQL package is primarily used for interacting with Generative AI services in Oracle Database 23ai?

DBMS_VECTOR_CHAIN

DBMS_ML

DBMS_AI

DBMS_GENAI

21 / 41

Which DDL operation is NOT permitted on a table containing a VECTOR column in Oracle Database 23ai?

Modifying the data type of an existing VECTOR column to a non-VECTOR type

Dropping an existing VECTOR column from the table

Creating a new table using CTAS (CREATE TABLE AS SELECT) that includes the VECTOR column from the original table

Adding a new VECTOR column to the table

22 / 41

What is the first step in setting up the practice environment for Select AI?

Drop any compartment that does not use OCI Generative AI

Create a policy to enable access to OCI Generative AI

Optionally create an OCI compartment

Create a new user account with elevated privileges

23 / 41

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?

Apply relational filters and a similarity search in the query

Use VECTOR_INDEX_HINT and NO WHERE clause

Perform the similarity search without a WHERE clause

Use UNION ALL with vector operations

24 / 41

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?

CREATE TABLE vectors (id NUMBER, embedding VECTOR(512, FLOAT32))

CREATE TABLE vectors (id NUMBER, embedding VECTOR)

CREATE TABLE vectors (id NUMBER, embedding VECTOR(512))

CREATE TABLE vectors (id NUMBER, embedding VECTOR(*, INT8))

25 / 41

What is the primary purpose of the DBMS_VECTOR_CHAIN.UTL_TO_CHUNKS package in a RAG application?

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

To load a document into the database

To generate vector embeddings from a text document

26 / 41

What is the correct order of steps for building a RAG application using PL/SQL in Oracle Database 23ai?

Load Document, Load ONNX Model, Split Text into Chunks, Create Embeddings, VectorizeQuestion, Perform Vector Search, Generate Output

Vectorize Question, Load ONNX Model, Load Document, Split Text into Chunks, Create Embeddings, Perform Vector Search, Generate Output

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

27 / 41

Which operation is NOT permitted on tables containing VECTOR columns?

JOIN ON VECTOR columns

DELETE

SELECT

UPDATE

28 / 41

Multi-Vector Similarity Search is typically used for:

Multi-document search

Exact vector matching

Image recognition

Single-vector comparisons

29 / 41

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?

Increase the VECTOR_MEMORY_SIZE initialization parameter

Change the index type to HNSW for better accuracy

Add the TARGET_ACCURACY clause to the query with a higher value for the accuracy

Re-create the index with a higher EFCONSTRUCTION value

30 / 41

Which function should you use to determine the storage format of a vector?

VECTOR_CHUNKS

VECTOR_NORM

VECTOR_DIMENSION_FORMAT

VECTOR_EMBEDDING

31 / 41

Which of the fpllowing statements is NOT true regarding operations on VECTOR columns?

You can use CTAS (Create Table As Select) to create a new table with a VECTOR column based on an existing table

You can perform direct comparisons between two VECTOR columns using operators like

You tan update a VECTOR column based on the value of another column in the same table

You can drop a VECTOR column from a table using ALTER DROP COLUMN

32 / 41

What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?

To calculate vector distances

To calculate vector dimensions

To serialize vectors into a string

To generate a single vector embedding for data

33 / 41

Assuming we have a table called VT2 that contains vectors for mapping various shapes, colors, and sizes,

what is the primary purpose of the following UPDATE statements? SQL> UPDATE vt2

SET shape = 'Triangle

WHERE id IN (2, 4, 7, 22, 31, 41, 44, 55);

SQL> UPDATE vt2

SET size = 'Medium'

WHERE id IN (5, 22, 25, 32, 34, 42, 43, 53, 54, 55);

To delete rows from the VT2 table that do not meet specific criteria

To modify the vector values stored in the VT2 table

To add new rows of data to the VT2 table with different sizes and shapes

To assign attributes like size and shape to existing vectors based on their IDs

34 / 41

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 secure VPN tunnel

A new user account with elevated privileges

35 / 41

How is the security interaction between Autonomous Database and OCI Generative AI managed in the context of Select AI?

By establishing a secure VPN tunnel between the Autonomous Database and OCI Generative AI service

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 requiring users to manually enter their OCI API keys each time they execute a natural language query

36 / 41

What is the primary purpose of the VECTOR NORM() function in Oracle Database 23ai?

To determine the number of dimensions in a vector

To return the magnitude or length of a vector

To convert a vector to a string representation

To calculate the Euclidean distance between two vectors

37 / 41

How can vector data be manipulated within an Oracle Database using Data Manipulation Language (DML)?

Insertion and updates

Insertion only

Insertion, updates, and deletion

Insertion, updates, deletion, and loading

38 / 41

How are vectqr embeddings stored in Oracle Database 23ai?

By creating columns with the VECTOR data type in relational tables

They are stored in a separate, specialized vector database

They are appended as metadata to existing data files

They are not stored; they are only used for querying

39 / 41

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?

The physical storage location of the vector data

Regularly updating vector embeddings to reflect changes in the source data

The specific distance algorithm employed for vector comparisons

Using the same embedding model for both vector creation and similarity search

40 / 41

Which SQL statement correctly adds a VECTOR column named "v" with 4 dimensions and FLOAT32 format to an existing table named "my_table"?

UPDATE my_table SET v = VECTOR(4, FLOAT32)

ALTER TABLE my_table MODIFY (v VECTOR(4, FLOAT32))

ALTER TABLE my_table ADD v VECTOR(4, FLOAT32)

ALTER TABLE my_table ADD (v VECTOR(4, FLOAT32))

41 / 41

What type of data, can be used for creating Vector Embeddings?

Image data

All of the above

Audio data

Text data

Your score is

The average score is 96%

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Free 1Z0-184-25 Exam Questions – 2025 Updated

Study Smarter for the 1Z0-184-25 Exam with Our Free and Reliable 1Z0-184-25 Exam Questions – Updated for 2025.

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