EXAM QUESTIONS 1Z0-184-25 VCE | 1Z0-184-25 NEW TEST BOOTCAMP

Exam Questions 1Z0-184-25 Vce | 1Z0-184-25 New Test Bootcamp

Exam Questions 1Z0-184-25 Vce | 1Z0-184-25 New Test Bootcamp

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Oracle 1Z0-184-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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.
Topic 4
  • 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.

>> Exam Questions 1Z0-184-25 Vce <<

Useful 1Z0-184-25 – 100% Free Exam Questions Vce | 1Z0-184-25 New Test Bootcamp

Oracle 1Z0-184-25 Practice test is an integral part of Oracle AI Vector Search Professional (1Z0-184-25) exam preparation. TestSimulate offers desktop-based 1Z0-184-25 practice exam software and web-based Oracle AI Vector Search Professional (1Z0-184-25) practice test that simulates the real Oracle AI Vector Search Professional (1Z0-184-25) exam environment. These Oracle AI Vector Search Professional (1Z0-184-25) practice tests are designed to help identify strengths and weaknesses.

Oracle AI Vector Search Professional Sample Questions (Q53-Q58):

NEW QUESTION # 53
A machine learning team is using IVF indexes in Oracle Database 23ai to find similar images in a large dataset. During testing, they observe that the search results are often incomplete, missing relevant images. They suspect the issue lies in the number of partitions probed. How should they improve the search accuracy?

  • A. Add the TARGET_ACCURACY clause to the query with a higher value for the accuracy
  • B. Change the index type to HNSW for better accuracy
  • C. Increase the VECTOR_MEMORY_SIZE initialization parameter
  • D. Re-create the index with a higher EFCONSTRUCTION value

Answer: A

Explanation:
IVF (Inverted File) indexes in Oracle 23ai partition vectors into clusters, probing a subset during queries for efficiency. Incomplete results suggest insufficient partitions are probed, reducing recall. The TARGET_ACCURACY clause (A) allows users to specify a desired accuracy percentage (e.g., 90%), dynamically increasing the number of probed partitions to meet this target, thus improving accuracy at the cost of latency. Switching to HNSW (B) offers higher accuracy but requires re-indexing and may not be necessary if IVF tuning suffices. Increasing VECTOR_MEMORY_SIZE (C) allocates more memory for vector operations but doesn't directly affect probe count. EFCONSTRUCTION (D) is an HNSW parameter, irrelevant to IVF. Oracle's IVF documentation highlights TARGET_ACCURACY as the recommended tuning mechanism.


NEW QUESTION # 54
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)
  • B. CREATE TABLE vectors (id NUMBER, embedding VECTOR(*, INT8))
  • C. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512, FLOAT32))
  • D. CREATE TABLE vectors (id NUMBER, embedding VECTOR(512))

Answer: C

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 # 55
Which SQL function is used to create a vector embedding for a given text string in Oracle Database 23ai?

  • A. VECTOR_EMBEDDING
  • B. CREATE_VECTOR_EMBEDDING
  • C. EMBED_TEXT
  • D. GENERATE_EMBEDDING

Answer: A

Explanation:
The VECTOR_EMBEDDING function in Oracle Database 23ai generates a vector embedding from input data (e.g., a text string) using a specified model, such as an ONNX model loaded into the database. It's designed for in-database embedding creation, supporting vector search and AI applications. Options A, B, and C (GENERATE_EMBEDDING, CREATE_VECTOR_EMBEDDING, EMBED_TEXT) are not valid SQL functions in 23ai. VECTOR_EMBEDDING integrates seamlessly with the VECTOR data type and is documented as the standard method for embedding generation in SQL queries.


NEW QUESTION # 56
You need to prioritize accuracy over speed in a similarity search for a dataset of images. Which should you use?

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

Answer: D

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 # 57
What is created to facilitate the use of OCI Generative AI with Autonomous Database?

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

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 # 58
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