Test Oracle 1Z0-184-25 Score Report, 1Z0-184-25 Simulations Pdf

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Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic	Details
Topic 1	Building a RAG Application: This section assesses the knowledge of AI Solutions Architects in implementing retrieval-augmented generation (RAG) applications. Candidates will learn to build RAG applications using PL SQL and Python to integrate AI models with retrieval techniques for enhanced AI-driven decision-making.
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	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 4	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 5	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 (Q22-Q27):

NEW QUESTION # 22
What is the function of the COSINE parameter in the SQL query used to retrieve similar vectors?
topk = 3
sql = f"""select payload, vector_distance(vector, :vector, COSINE) as score from {table_name} order by score fetch approximate {topk} rows only"""

A. It filters out vectors with a cosine similarity below a certain threshold
B. It indicates that the cosine distance metric should be used to measure similarity between vectors
C. It specifies the type of vector encoding used in the database
D. It converts the vectors to a format compatible with the SQL database

Answer: B

Explanation:
In Oracle Database 23ai, the VECTOR_DISTANCE function calculates the distance between two vectors using a specified metric. The COSINE parameter in the query (vector_distance(vector, :vector, COSINE)) instructs the database to use the cosine distance metric (C) to measure similarity. Cosine distance, defined as 1 - cosine similarity, is ideal for high-dimensional vectors (e.g., text embeddings) as it focuses on angular separation rather than magnitude. It doesn't filter vectors (A); filtering requires additional conditions (e.g., WHERE clause). It doesn't convert vector formats (B); vectors are already in the VECTOR type. It also doesn't specify encoding (D), which is defined during vector creation (e.g., FLOAT32). Oracle's documentation confirms COSINE as one of the supported metrics for similarity search.

NEW QUESTION # 23
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?

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

Answer: D

Explanation:
In Oracle Database 23ai, a VECTOR column with a defined dimension count (e.g., VECTOR(4, FLOAT32)) enforces strict dimensional integrity to ensure consistency for similarity search and indexing. Attempting to insert a vector with a mismatched number of dimensions-say, TO_VECTOR('[1.2, 3.4, 5.6]') (3D) into a VECTOR(4)-results in the insert operation failing with an error (D), such as ORA-13199: "vector dimension mismatch." This rigidity protects downstream AI operations; a 3D vector in a 4D column would misalign with indexed data (e.g., HNSW graphs), breaking similarity calculations like cosine distance, which require uniform dimensionality.
Option A (truncation) is tempting but incorrect; Oracle doesn't silently truncate [1.2, 3.4, 5.6] to [1.2, 3.4]-this would discard data arbitrarily, risking semantic loss (e.g., a truncated sentence embedding losing meaning). Option B (padding with zeros) seems plausible-e.g., [1.2, 3.4, 5.6] becoming [1.2, 3.4, 5.6, 0]-but Oracle avoids implicit padding to prevent unintended semantic shifts (zero-padding could alter distances). Option C (ignoring dimensions) only applies to undefined VECTOR columns (e.g., VECTOR without size), not fixed ones; here, the constraint is enforced. The failure (D) forces developers to align data explicitly (e.g., regenerate embeddings), ensuring reliability-a strict but necessary design choice in Oracle's AI framework. In practice, this error prompts debugging upstream data pipelines, avoiding silent failures that could plague production AI systems.

NEW QUESTION # 24
What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?

A. To calculate vector dimensions
B. To calculate vector distances
C. To serialize vectors into a string
D. To generate a single vector embedding for data

Answer: D

NEW QUESTION # 25
What is the primary function of AI Smart Scan in Exadata System Software 24ai?

A. To automatically optimize database queries for improved performance
B. To provide real-time monitoring and diagnostics for AI applications
C. To accelerate AI workloads by leveraging Exadata RDMA Memory (XRMEM), Exadata Smart Cache, and on-storage processing

Answer: C

Explanation:
AI Smart Scan in Exadata System Software 24ai (B) accelerates AI workloads, including vector search, by offloading processing to storage servers using Exadata's RDMA Memory (XRMEM), Smart Cache, and on-storage capabilities. This enhances performance for large-scale vector operations. Real-time monitoring (A) isn't its focus; that's for management tools. Queryoptimization (C) is a general Exadata feature (Smart Scan), but AI Smart Scan specifically targets AI tasks. Oracle's 24ai documentation emphasizes its role in speeding up AI computations.

NEW QUESTION # 26
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?

A. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512))
B. CREATE TABLE vectors (id NUMBER, embedding VECTOR(*, INT8))
C. CREATE TABLE vectors (id NUMBER, embedding VECTOR)
D. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512, FLOAT32))

Answer: D

Explanation:
In Oracle 23ai, the VECTOR data type can specify dimensions and precision. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512, FLOAT32)) (D) defines a column with exactly 512 dimensions and FLOAT32 (32-bit float) format, meeting both requirements. Option A omits the format (defaults vary), risking mismatch. Option B is unspecified, allowing variable dimensions-not "exactly 512." Option C uses INT8, not FLOAT32, and '*' denotes undefined dimensions. Oracle's SQL reference confirms this syntax for precise VECTOR definitions.

NEW QUESTION # 27
......

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TEST ORACLE 1Z0-184-25 SCORE REPORT, 1Z0-184-25 SIMULATIONS PDF

Test Oracle 1Z0-184-25 Score Report, 1Z0-184-25 Simulations Pdf