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Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic	Details
Topic 1	Performing Similarity Search: This section tests the skills of Machine Learning Engineers in conducting similarity searches to find relevant data points. It includes performing exact and approximate similarity searches using vector indexes. Candidates will also work with multi-vector similarity search to handle searches across multiple documents for improved retrieval accuracy.
Topic 2	Understand Vector Fundamentals: This section of the exam measures the skills of Data Engineers in working with vector data types for storing embeddings and enabling semantic queries. It covers vector distance functions and metrics used in AI vector search. Candidates must demonstrate proficiency in performing DML and DDL operations on vectors to manage data efficiently.
Topic 3	Using Vector Embeddings: This section measures the abilities of AI Developers in generating and storing vector embeddings for AI applications. It covers generating embeddings both inside and outside the Oracle database and effectively storing them within the database for efficient retrieval and processing.
Topic 4	Leveraging Related AI Capabilities: This section evaluates the skills of Cloud AI Engineers in utilizing Oracle’s AI-enhanced capabilities. It covers the use of Exadata AI Storage for faster vector search, Select AI with Autonomous for querying data using natural language, and data loading techniques using SQL Loader and Oracle Data Pump to streamline AI-driven workflows.

NEW QUESTION 34
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

NEW QUESTION 35
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

NEW QUESTION 36
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

NEW QUESTION 37
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

NEW QUESTION 38
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

NEW QUESTION 39
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

NEW QUESTION 40
What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?

To calculate vector distances

To generate a single vector embedding for data

To calculate vector dimensions

To serialize vectors into a string

NEW QUESTION 41
Which statement best describes the capability of Oracle Data Pump for handling vector data in thecontext of vector search applications?

Data Pump only exports and imports 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

NEW QUESTION 42
Which parameter is used to define the number of closest vector candidates considered during HNSW index creation?

EFCONSTRUCTION

VECTOR_MEMORY_SIZE

NEIGHBOURS

TARGET_ACCURACY

NEW QUESTION 43
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

NEW QUESTION 44
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

NEW QUESTION 45
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, VectorizeQuestion, Perform Vector Search, Generate Output

NEW QUESTION 46
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

A secure VPN tunnel

NEW QUESTION 47
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

NEW QUESTION 48
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

Create a new user account with elevated privileges

NEW QUESTION 49
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

To group vectors by their exact scores

NEW QUESTION 50
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

NEW QUESTION 51
Which SQL statement correctly adds a VECTOR column named “v” with 4 dimensions and FLOAT32 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)

NEW QUESTION 52
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

NEW QUESTION 53
Which function should you use to determine the storage format of a vector?

VECTOR_DIMENSION_FORMAT

VECTOR_CHUNKS

VECTOR_NORM

VECTOR_EMBEDDING

NEW QUESTION 54
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

NEW QUESTION 55
What happens when querying with an IVF index if you increase the value of the NEIGHBOR_PARTITIONS 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

NEW QUESTION 56
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

NEW QUESTION 57
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

It always guarantees 100% accuracy

It is slower than exact similarity search

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