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1Z0-184-25 Minimum Pass Score & 1Z0-184-25 Dumps

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Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

Using Vector Indexes: This section evaluates the expertise of AI Database Specialists in optimizing vector searches using indexing techniques. It covers the creation of vector indexes to enhance search speed, including the use of HNSW and IVF vector indexes for performing efficient search queries in AI-driven applications.

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.

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Oracle AI Vector Search Professional Sample Questions (Q14-Q19):

NEW QUESTION # 14
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 calculate vector distances
D. To generate a single vector embedding for data

Answer: D

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 # 15
You need to generate a vector from the string '[1.2, 3.4]' in FLOAT32 format with 2 dimensions. Which function will you use?

A. TO_VECTOR
B. VECTOR_SERIALIZE
C. FROM_VECTOR
D. VECTOR_DISTANCE

Answer: A

Explanation:
In Oracle Database 23ai, the TO_VECTOR function (A) converts a string representation of a vector (e.g., '[1.2, 3.4]') into a VECTOR data type with specified format (e.g., FLOAT32) and dimensions (here, 2). It's designed for creating vectors from text input, matching the requirement. VECTOR_DISTANCE (B) calculates distances between vectors, not generates them.FROM_VECTOR (C) isn't a documented function; it might be confused with serialization or extraction, but it's not standard. VECTOR_SERIALIZE (D) converts a vector to a string, the opposite of what's needed. Oracle's SQL reference confirms TO_VECTOR for this purpose, parsing the string into a 2D FLOAT32 vector.

NEW QUESTION # 16
Which function should you use to determine the storage format of a vector?

A. VECTOR_CHUNKS
B. VECTOR_NORM
C. VECTOR_EMBEDDING
D. VECTOR_DIMENSION_FORMAT

Answer: D

NEW QUESTION # 17
What is the advantage of using Euclidean Squared Distance rather than Euclidean Distance in similarity search queries?

A. It guarantees higher accuracy than Euclidean Distance
B. It is simpler and faster because it avoids square-root calculations
C. It supports hierarchical partitioning of vectors
D. It is the default distance metric for Oracle AI Vector Search

Answer: B

Explanation:
Euclidean Squared Distance (L2-squared) skips the square-root step of Euclidean Distance (L2), i.e., ∑(xi - yi)² vs. √∑(xi - yi)². Since the square root is monotonic, ranking order remains identical, but avoiding it (C) reduces computational cost, making queries faster-crucial for large-scale vector search. It's not the default metric (A); cosine is often default in Oracle 23ai. It doesn't relate to partitioning (B), an indexing feature. Accuracy (D) is equivalent, as rankings are preserved. Oracle's documentation notes L2-squared as an optimization for performance.

NEW QUESTION # 18
What is created to facilitate the use of OCI Generative AI with Autonomous Database?

A. A dedicated OCI compartment
B. An AI profile for OCI Generative AI
C. A secure VPN tunnel
D. A new user account with elevated privileges

Answer: B

Explanation:
To integrate OCI Generative AI with Autonomous Database in Oracle 23ai (e.g., for Select AI), an AI profile (A) is created within the database using DBMS_AI. This profile configures the connection to OCI Generative AI, specifying the LLM and authentication (e.g., Resource Principals). A compartment (B) organizes OCI resources but isn't "created" specifically for this integration; it's a prerequisite. A new user account (C) or VPN tunnel (D) isn't required; security leverages existing mechanisms. Oracle's Select AI setup documentation highlights the AI profile as the key facilitator.

NEW QUESTION # 19
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