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1Z0-184-25–100% Free Valid Exam Pattern | Perfect Valid Oracle AI Vector Search Professional Exam Dumps

Are you preparing for the Oracle 1Z0-184-25 certification exam? Whether you're an experienced professional Oracle 1Z0-184-25 looking to take your career to the next level or a recent graduate trying to break into the tech field, the road to Oracle 1Z0-184-25 Certification can be a long and challenging one. The good news is that you do not have to navigate it alone.

Oracle 1Z0-184-25 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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.

Topic 4

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 5

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.

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Oracle AI Vector Search Professional Sample Questions (Q13-Q18):

NEW QUESTION # 13
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?

A. ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=SGA
B. ALTER DATABASE SET VECTOR_MEMORY_SIZE=1G SCOPE=VECTOR
C. ALTER SYSTEM RESET VECTOR_MEMORY_SIZE
D. ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=BOTH

Answer: D

Explanation:
VECTOR_MEMORY_SIZE in Oracle 23ai controls memory allocation for vector operations (e.g., indexing, search) in the SGA. For a PDB, ALTER SYSTEM adjusts parameters, andSCOPE=BOTH (A) applies the change immediately and persists it across restarts (modifying the SPFILE). Syntax: ALTER SYSTEM SET VECTOR_MEMORY_SIZE=1G SCOPE=BOTH sets it to 1 GB. Option B (ALTER DATABASE) is invalid for this parameter, and SCOPE=VECTOR isn't a valid scope. Option C (SCOPE=SGA) isn't a scope value; valid scopes are MEMORY, SPFILE, or BOTH. Option D (RESET) reverts to default, not sets a value. In a PDB, this must be executed in the PDB context, not CDB, and BOTH ensures durability-key for production environments where vector workloads demand consistent memory.

NEW QUESTION # 14
What happens when querying with an IVF index if you increase the value of the NEIGHBOR_PARTITIONS probes parameter?

A. The number of centroids decreases
B. More partitions are probed, improving accuracy, but also increasing query latency
C. Accuracy decreases
D. Index creation time is reduced

Answer: B

Explanation:
The NEIGHBOR_PARTITIONS parameter in Oracle 23ai's IVF index controls how many partitions are probed during a query. Increasing this value examines more clusters, raising theprobability of finding relevant vectors, thus improving accuracy (recall). However, this increases computational effort, leading to higher query latency-a classic ANN trade-off. The number of centroids (A) is fixed during index creation and unaffected by query parameters. Accuracy does not decrease (B); it improves. Index creation time (C) is unrelated to query-time settings. Oracle's documentation on IVF confirms that NEIGHBOR_PARTITIONS directly governs this accuracy-latency balance.

NEW QUESTION # 15
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?

A. Encodes the question and database chunks into vectors, finds the most similar using cosine similarity, and includes them in the LLM prompt
B. Trains a separate LLM on the database and uses it to answer, ignoring the general LLM
C. Converts the question to keywords, searches for matches, and inserts the text into the response
D. Clusters similar text chunks and randomly selects one from the most relevant cluster

Answer: A

Explanation:
In Oracle 23ai's RAG framework, vector similarity search (A) encodes a user question and database chunks into vectors (e.g., via VECTOR_EMBEDDING), computes similarity (e.g., cosine via VECTOR_DISTANCE), and retrieves the most relevant chunks. These are then included in the LLM prompt, augmenting its response with context. Training a separate LLM (B) is not RAG; RAG uses existing models. Keyword search (C) is traditional, not vector-based, and less semantic. Clustering and random selection (D) lacks precision and isn't RAG's approach. Oracle's documentation describes this encode-search-augment process as RAG's core mechanism.

NEW QUESTION # 16
You need to prioritize accuracy over speed in a similarity search for a dataset of images. Which should you use?

A. Exact similarity search using a full table scan
B. Multivector similarity search with partitioning
C. Approximate similarity search with HNSW indexing and target accuracy of 70%
D. Approximate similarity search with IVF indexing and target accuracy of 70%

Answer: A

Explanation:
To prioritize accuracy over speed, exact similarity search with a full table scan (C) computes distances between the query vector and all stored vectors, guaranteeing 100% recall without approximation trade-offs. HNSW with 70% target accuracy (A) and IVF with 70% (D) are approximate methods, sacrificing accuracy for speed via indexing (e.g., probing fewer neighbors). Multivector search (B) isn't a standard Oracle 23ai term; partitioning aids scale, not accuracy. Exact search, though slower, ensures maximum accuracy, as per Oracle's vector search options.

NEW QUESTION # 17
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 converts the vectors to a format compatible with the SQL database
D. It specifies the type of vector encoding used in the 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 # 18
......

Undoubtedly, passing the Oracle 1Z0-184-25 Certification Exam is one big achievement. Regardless of how tough the Oracle AI Vector Search Professional (1Z0-184-25) exam is, it serves an important purpose of improving your skills and knowledge of a specific field. Once you become certified by Oracle, a whole new career scope will open up to you.

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