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Oracle 1Z0-184-25 New Test Materials, Valid 1Z0-184-25 Exam Cram

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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

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 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

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.

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

NEW QUESTION # 13
Which is NOT a feature or capability related to AI and Vector Search in Exadata?

A. Vector Replication with GoldenGate
B. AI Smart Scan
C. Native Support for Vector Search Only within the Database Server
D. Loading Vector Data using SQL*Loader

Answer: C

Explanation:
Exadata in Oracle Database 23ai enhances AI and vector search capabilities. Vector Replication with GoldenGate (B) supports real-time vector data distribution. SQL*Loader (C) loads vector data into VECTOR columns. AI Smart Scan (D) accelerates AI workloads using Exadata's storage optimizations. However, "Native Support for Vector Search Only within the Database Server" (A) is not a feature; vector search is natively supported across Exadata's architecture, leveraging both database and storage layers (e.g., via Smart Scan), not restricted to the server alone. This option misrepresents Exadata's distributed capabilities, making it the correct "NOT" answer.

NEW QUESTION # 14
What is the first step in setting up the practice environment for Select AI?

A. Optionally create an OCI compartment
B. Create a new user account with elevated privileges
C. Drop any compartment that does not use OCI Generative AI
D. Create a policy to enable access to OCI Generative AI

Answer: A

Explanation:
Select AI in Oracle Database 23ai enables natural language queries by integrating with OCI Generative AI services. The first step in setting up the practice environment is to optionally create an OCI compartment (A), which organizes and isolates resources in Oracle Cloud Infrastructure (OCI). This is foundational because subsequent steps-like defining policies or configuring the Autonomous Database-depend on a compartment structure, though an existing compartment can be reused, making it optional. Creating a policy (B) is a subsequent step to grant access to OCIGenerative AI, requiring a compartment first. Dropping compartments (C) is irrelevant and disruptive. Creating a user account (D) is not specified as the initial step in Select AI setup. Oracle's Select AI documentation lists compartment setup as the starting point in OCI configuration.

NEW QUESTION # 15
When generating vector embeddings for a new dataset outside of Oracle Database 23ai, which factor is crucial to ensure meaningful similarity search results?

A. The physical location where the vector embeddings are stored
B. The storage format of the new dataset (e.g., CSV, JSON)
C. The choice of programming language used to process the dataset (e.g., Python, Java)
D. The same vector embedding model must be used for vectorizing the data and creating a query vector

Answer: D

Explanation:
Meaningful similarity search relies on the consistency of the vector space in which embeddings reside. Vector embeddings are generated by models (e.g., BERT, SentenceTransformer) that map data into a high-dimensional space, where proximity reflects semantic similarity. If different models are used for the dataset and query vector, the embeddings will be in incompatible spaces, rendering distance metrics (e.g., cosine, Euclidean) unreliable. The programming language (A) affects implementation but not the semantic consistency of embeddings-Python or Java can use the same model equally well. The physical storage location (B) impacts accessibility and latency but not the mathematical validity of similarity comparisons. The storage format (C) influences parsing andingestion but does not determine the embedding space. Oracle 23ai's vector search framework explicitly requires the same embedding model for data and queries to ensure accurate results, a principle that applies universally, even outside the database.

NEW QUESTION # 16
When using SQL*Loader to load vector data for search applications, what is a critical consideration regarding the formatting of the vector data within the input CSV file?

A. Enclose vector components in curly braces ({})
B. Use sparse format for vector data
C. As FVEC is a binary format and the vector dimensions have a known width, fixed offsets can be used to make parsing the vectors fast and efficient
D. Rely on SQL*Loader's automatic normalization of vector data

Answer: A

Explanation:
SQLLoader in Oracle 23ai supports loading VECTOR data from CSV files, requiring vectors to be formatted as text. A critical consideration is enclosing components in curly braces (A), e.g., {1.2, 3.4, 5.6}, to match the VECTOR type's expected syntax (parsed into FLOAT32, etc.). FVEC (B) is a binary format, not compatible with CSV text input; SQLLoader expects readable text, not fixed offsets. Sparse format (C) isn't supported for VECTOR columns, which require dense arrays. SQLLoader doesn't normalize vectors automatically (D); formatting must be explicit. Oracle's documentation specifies curly braces for CSV-loaded vectors.

NEW QUESTION # 17
What is created to facilitate the use of OCI Generative AI with Autonomous Database?

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

Answer: A

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 # 18
......

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