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The best 1Z0-184-25 Study Guide: Oracle AI Vector Search Professional is the best select - RealValidExam

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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

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 3

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 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

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.

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Oracle AI Vector Search Professional Sample Questions (Q50-Q55):

NEW QUESTION # 50
Which statement best describes the capability of Oracle Data Pump for handling vector data in thecontext of vector search applications?

A. 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
B. Data Pump provides native support for exporting and importing tables containing vector data types, facilitating the transfer of vector data for vector search applications
C. Data Pump only exports and imports vector data if the vector embeddings are stored as BLOB (Binary Large Object) data types in the database
D. 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

Answer: B

Explanation:
Oracle Data Pump in 23ai natively supports the VECTOR data type (C), allowing export and import of tables with vector columns without conversion or plug-ins. This facilitates vector search application migrations, preserving dimensional and format integrity (e.g., FLOAT32). BLOB storage (A) isn't required; VECTOR is a distinct type. Data Pump doesn't treat vectors as text (B), avoiding corruption; it handles them as structured arrays. No specialized plug-in (D) is needed; native support is built-in. Oracle's Data Pump documentation confirms seamless handling of VECTOR data.

NEW QUESTION # 51
Which parameter is used to define the number of closest vector candidates considered during HNSW index creation?

A. NEIGHBOURS
B. VECTOR_MEMORY_SIZE
C. TARGET_ACCURACY
D. EFCONSTRUCTION

Answer: D

Explanation:
In Oracle 23ai, EFCONSTRUCTION (A) controls the number of closest vector candidates (edges) considered during HNSW index construction, affecting the graph's connectivity and search quality. Higher values improve accuracy but increase build time. VECTOR_MEMORY_SIZE (B) sets memory allocation, not candidate count. NEIGHBOURS (C) isn't a parameter; it might confuse with NEIGHBOR_PARTITIONS (IVF). TARGET_ACCURACY (D) adjusts query-time accuracy, not index creation. Oracle's HNSW documentation specifies EFCONSTRUCTION for this purpose.

NEW QUESTION # 52
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. Rely on SQL*Loader's automatic normalization of vector data
B. Enclose vector components in curly braces ({})
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. Use sparse format for vector data

Answer: B

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 # 53
What is the first step in setting up the practice environment for Select AI?

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

Answer: B

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 # 54
If a query vector uses a different distance metric than the one used to create the index, whathappens?

A. The index automatically updates
B. The query fails
C. An exact match search is triggered
D. A warning is logged, but the query executes

Answer: B

Explanation:
In Oracle Database 23ai, vector indexes (e.g., HNSW, IVF) are built with a specific distance metric (e.g., cosine, Euclidean) that defines how similarity is computed. If a query specifies a different metric (e.g., querying with Euclidean on a cosine-based index), the index cannot be used effectively, and the query fails (A) with an error, as the mismatch invalidates the index's structure. An exact match search (B) doesn't occur automatically; Oracle requires explicit control. The index doesn't update itself (C), and warnings (D) are not the default behavior-errors are raised instead. Oracle's documentation mandates metric consistency for index usage.

NEW QUESTION # 55
......

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Don Reed Don Reed

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